Creator · wanshuiyin
Last updated · Sep 2, 2026
Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
Creator · wanshuiyin
Last updated · Sep 2, 2026
Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
Creator · wanshuiyin
Last updated · Sep 2, 2026
Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
Creator · wanshuiyin
Last updated · Sep 2, 2026
Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
Sandbox only
Install targets
Codex install prompt
Install the "auto-paper-improvement-loop" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/auto-paper-improvement-loop. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop","task":"Install auto-paper-improvement-loop","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Maintenance
fresh
2d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
16K
100/100 Quality · 71/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
16K GitHub stars
Repo activity
16K stars, 1.4K forks
Maintenance
2d since push
License
MIT
Install
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loopDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
Agent should check
Copy prompt
Task: Use auto-paper-improvement-loop in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
Install command: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
LLM text format
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install?format=text
Find alternatives
/api/skills/search?q=auto-paper-improvement-loop&limit=3
Agent prompt
Use auto-paper-improvement-loop for this task. Review https://www.openagentskill.com/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install, then install with: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loopRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop
LLM text
/api/registry/manifest/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop?format=text
Install alias
/api/registry/install/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop
Recommend
/api/registry/recommend?task=Use%20auto-paper-improvement-loop%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Workflow automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS16K GitHub stars
Stars/forks activity
PASS16K stars, 1.4K forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: auto-paper-improvement-loop description: "Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper." argument-hint: "[paper-directory] [— style-ref: <source>] [— edit-whitelist <path>]" allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply ---
# Auto Paper Improvement Loop: Review → Fix → Recompile
> 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** It > already loops internally (review → fix → recompile) with its own round > structure and a deliberate fresh-reviewer bias guard each round (no > `codex-reply`). Re-asking it to "improve the paper" on a > wall-clock timer produces no new signal — quality changes when the *review* > changes, not when the clock ticks — and a timed re-run that also accepts its > own output to decide when to stop crosses into self-acquittal > (`acceptance-gate.md`). Schedule the *external wait that precedes it*, not the > improvement loop. See > [`shared-references/external-cadence.md`](../shared-references/external-cadence.md).
Autonomously improve the paper at: **$ARGUMENTS**
## Context
This skill is designed to run **after** Workflow 3 (`/paper-plan` → `/paper-figure` → `/paper-write` → `/paper-compile`). It takes a compiled paper and iteratively improves it through external LLM review.
Unlike `/auto-review-loop` (which iterates on **research** — running experiments, collecting data, rewriting narrative), this skill iterates on **paper writing quality** — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.
## Constants
- **MAX_ROUNDS = 2** — Two rounds of review→fix→recompile. Empirically, Round 1 catches structural issues (4→6/10), Round 2 catches remaining presentation issues (6→7/10). Diminishing returns beyond 2 rounds for writing-only improvements. - **REVIEWER_MODEL = `gpt-5.6-sol`** — Model used via Codex MCP for paper review. - **REVIEWER_BIAS_GUARD = true** — When `true`, every review round uses a fresh `mcp__codex__codex` thread with no prior review context. Never use `mcp__codex__codex-reply` for review rounds. Set to `false` only for deliberate debugging of the legacy behavior. **Empirical evidence:** running the same paper with `codex-reply` + "since last round we did X" prompts inflated scores from real 3/10 → fake 8/10 across multiple rounds; switching to fresh threads recovered the true 3/10 assessment. - **REVIEW_LOG = `PAPER_IMPROVEMENT_LOG.md`** — Cumulative log of all rounds, stored in paper directory. - **HUMAN_CHECKPOINT = false** — When `true`, pause after each round's review and present score + weaknesses to the user. The user can approve fixes, provide custom modification instructions, skip specific fixes, or stop early. When `false` (default), runs fully autonomously. - **EDIT_WHITELIST = `null`** — Optional path to a YAML/JSON whitelist file constraining which paths and operations the fix-implementation step may touch. When `null` (default), all edits proceed unconstrained. When set via `— edit-whitelist <path>` (also accepts `— edit_whitelist <path>`), the loop loads the file at startup and consults it before each edit; rejected edits are logged to `PAPER_IMPROVEMENT_LOG.md` rather than silently dropped. See "Optional: Edit Whitelist" below.
> 💡 Override: `/auto-paper-improvement-loop "paper/" — human checkpoint: true`
## Optional: Style reference (`— style-ref: <source>`, opt-in)
Lets the user steer **structural fixes only** during improvement (section reordering hints, paragraph length nudges, figure density adjustments) toward a reference paper. **Default OFF — when the user does not pass `— style-ref`, do nothing differently from before.**
Only when `— style-ref: <source>` appears in `$ARGUMENTS`, run the helper FIRST, before the loop starts:
```bash # Resolve $STYLE_HELPER via the canonical strict-safe chain (see # shared-references/integration-contract.md §2). Policy A — gate: # unresolved helper means --style-ref cannot be satisfied, so abort. cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1 if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true fi if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true fi STYLE_HELPER=".aris/tools/extract_paper_style.py" [ -f "$STYLE_HELPER" ] || STYLE_HELPER="tools/extract_paper_style.py" [ -f "$STYLE_HELPER" ] || { [ -n "${ARIS_REPO:-}" ] && STYLE_HELPER="$ARIS_REPO/tools/extract_paper_style.py"; } [ -f "$STYLE_HELPER" ] || { echo "ERROR: extract_paper_style.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2 echo " Fix: rerun bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or copy the helper to tools/." >&2 echo " --style-ref cannot be satisfied; aborting." >&2 exit 1 } STYLE_STATUS=0 CACHE=$(python3 "$STYLE_HELPER" --source "<source>") || STYLE_STATUS=$? case "$STYLE_STATUS" in 0) ;; # use $CACHE/style_profile.md as structural guidance for the FIX phase only 2) echo "warning: style-ref skipped (missing optional dep)" >&2 ;; 3) echo "error: --style-ref source failed; aborting loop" >&2 ; exit 1 ;; *) echo "error: helper failed unexpectedly; aborting loop" >&2 ; exit 1 ;; esac ```
Sources accepted: local TeX dir / file, local PDF, arXiv id, http(s) URL. Overleaf URLs/IDs are rejected — clone via `/overleaf-sync setup <id>` first and pass the local clone path.
**Strict rules** (full contract in `tools/extract_paper_style.py` docstring):
- Use `style_profile.md` only during the **fix-implementation** phase, to nudge structural choices when applying reviewer feedback. Reviewer feedback always takes precedence; style ref is tie-breaker for *how* to apply a fix, not *whether* to apply it. - **Never copy prose, claims, examples, or terminology** from anything reachable through the cache when implementing fixes. - **Never pass `— style-ref` (or the cache contents) to the GPT-5.6-Sol reviewer sub-agent.** The Reviewer Independence Protocol below requires reviewers see only the artifact and the user's prompt — leaking the style ref would contaminate the review with author-side context. **This is the most critical invariant in this skill.**
## Optional: Edit Whitelist (`— edit-whitelist <path>`, opt-in)
Lets the caller hard-constrain which files and operations the **fix-implementation** step (Step 3 / Step 6) is allowed to touch. **Default OFF — when the user does not pass `— edit-whitelist` (or the alias `— edit_whitelist`), the loop applies all reviewer-driven edits without restriction, exactly as before.**
This is the parameter that upstream pipelines (e.g. `/resubmit-pipeline` Phase 2) use to enforce text-only resubmit microedits: no `.bib` mutations, no `.sty` / `.bst` mutations, no edits to prior-submission directories, no new `\cite{...}`, no new theorem environments, no new numerical claims.
### Schema
The whitelist file is YAML or JSON. All four sections are optional:
```yaml allowed_paths: - sec/*.tex - main.tex - figures/*.tex forbidden_paths: - "**/*.bib" - "**/*.sty" - "**/*.bst" - "../OldSubmission/**" forbidden_operations: - new_cite # blocks \cite{...}, \citep{...}, \citet{...}, \citeauthor{...} additions - new_bibitem # blocks \bibitem{...} additions - new_theorem_env # blocks \begin{theorem|lemma|proposition|corollary} additions - numerical_claim # blocks adding new numbers / percentages / metrics forbidden_deletions: # operations that block REMOVALS, not additions - delete_existing_cite # blocks removal of \cite{...} from the body (use citation-audit --soft-only instead) - delete_theorem_env # blocks removal of an existing \begin{theorem|...} block requires_user_approval_for: # operations that don't auto-reject but pause for explicit user OK - rewrite_abstract # paraphrasing the entire abstract triggers a checkpoint - rewrite_intro_first_para - delete_section max_edits_per_round: 30 # hard cap on number of accepted edits per round (rejections are not counted; if cap is hit, remaining proposed edits are deferred to the next round with a warning) rationale: "Resubmit mode: text-only microedits, paper structure frozen by user constraint." ```
### Resolution rules
- **`allowed_paths` empty AND `forbidden_paths` empty** → whitelist is a no-op (advisory: the file is loaded and `rationale` echoed to the log, but no path filtering is applied). - **`allowed_paths` empty, `forbidden_paths` non-empty** → all paths NOT matched by `forbidden_paths` are mutable. - **`allowed_paths` non-empty, `forbidden_paths` empty** → only paths matching `allowed_paths` are mutable. - **Both non-empty** → an edit is allowed iff the target matches `allowed_paths` AND does NOT match `forbidden_paths`. `forbidden_paths` always wins on overlap. - **`forbidden_operations` missing or empty** → no operation-level guard; only path-level filtering applies.
### Glob semantics
Use bash `extglob` / Python `fnmatch.fnmatch` semantics. `**` matches any depth (zero or more directory segments). Patterns are matched against the path **relative to the paper directory** (e.g. `paper/sec/intro.tex` matches `sec/*.tex` when paper-directory is `paper/`).
### Forbidden-operation detectors
For each candidate edit's diff (the new lines being added — deletions are exempt), the loop runs these regex checks and rejects if any forbidden operation matches:
| Operation | Detector (added lines only) | |-----------|------------------------------| | `new_cite` | `\\cite[a-zA-Z]*\{[^}]+\}` (catches `\cite`, `\citep`, `\citet`, `\citeauthor`, `\citeyear`, `\citealp`, etc.) | | `new_bibitem` | `\\bibitem\{[^}]+\}` | | `new_theorem_env` | `\\begin\{(theorem|lemma|proposition|corollary)\*?\}` | | `numerical_claim` | New token matching `\b\d+(\.\d+)?%?\b` that did NOT appear in the deleted/replaced lines (i.e. genuinely new numbers, not edits to existing ones) |
### Behavior at loop start (before Round 1 fix-implementation)
1. If `— edit-whitelist <path>` is present in `$ARGUMENTS`, set `EDIT_WHITELIST = <path>`. 2. Load the file (`yaml.safe_load`; if it fails, fall back to `json.loads`). On load failure, abort the loop with a clear error — do NOT silently proceed unconstrained. 3. Echo `rationale` (if present) into `PAPER_IMPROVEMENT_LOG.md` under a new "Edit Whitelist" preamble section so the audit trail records why edits were constrained.
### Behavior during fix-implementation (Steps 3 and 6)
Before applying each proposed edit:
1. Resolve target file path relative to the paper directory. 2. Path check: if `allowed_paths` is non-empty, target must match at least one pattern. Then if `forbidden_paths` is non-empty, target must NOT match any pattern. If either fails → reject as `path` violation. 3. Operation check: build the unified diff (or just the set of newly-added lines) for the proposed edit. For each entry in `forbidden_operations`, run its detector on the added lines. If any detector matches → reject as `operation` violation. 4. If all checks pass, apply the edit normally. 5. If rejected, append an entry to `PAPER_IMPROVEMENT_LOG.md` under a `## Rejected by edit_whitelist (Round N)` heading with this schema: ``` - file: <relative path> reason: path | operation pattern: <the offending forbidden_path glob, OR the offending forbidden_operation name + the matched substring> reviewer_concern: <the original Round-N weakness that motivated this edit> ``` 6. Continue with the remaining edits in the round. Do NOT abort the whole round
Decision snapshot
15,640 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for auto-paper-improvement-loop, ready for a manual X post.
auto-paper-improvement-loop: Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recom... 15.6K stars https://www.openagentskill.com/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop?ref=x
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsSandbox only
Install targets
Codex install prompt
Install the "auto-paper-improvement-loop" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/auto-paper-improvement-loop. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop","task":"Install auto-paper-improvement-loop","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Maintenance
fresh
2d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
16K
100/100 Quality · 71/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
16K GitHub stars
Repo activity
16K stars, 1.4K forks
Maintenance
2d since push
License
MIT
Install
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loopDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
Agent should check
Copy prompt
Task: Use auto-paper-improvement-loop in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
Install command: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
LLM text format
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install?format=text
Find alternatives
/api/skills/search?q=auto-paper-improvement-loop&limit=3
Agent prompt
Use auto-paper-improvement-loop for this task. Review https://www.openagentskill.com/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install, then install with: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loopRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop
LLM text
/api/registry/manifest/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop?format=text
Install alias
/api/registry/install/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop
Recommend
/api/registry/recommend?task=Use%20auto-paper-improvement-loop%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Workflow automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS16K GitHub stars
Stars/forks activity
PASS16K stars, 1.4K forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: auto-paper-improvement-loop description: "Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper." argument-hint: "[paper-directory] [— style-ref: <source>] [— edit-whitelist <path>]" allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply ---
# Auto Paper Improvement Loop: Review → Fix → Recompile
> 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** It > already loops internally (review → fix → recompile) with its own round > structure and a deliberate fresh-reviewer bias guard each round (no > `codex-reply`). Re-asking it to "improve the paper" on a > wall-clock timer produces no new signal — quality changes when the *review* > changes, not when the clock ticks — and a timed re-run that also accepts its > own output to decide when to stop crosses into self-acquittal > (`acceptance-gate.md`). Schedule the *external wait that precedes it*, not the > improvement loop. See > [`shared-references/external-cadence.md`](../shared-references/external-cadence.md).
Autonomously improve the paper at: **$ARGUMENTS**
## Context
This skill is designed to run **after** Workflow 3 (`/paper-plan` → `/paper-figure` → `/paper-write` → `/paper-compile`). It takes a compiled paper and iteratively improves it through external LLM review.
Unlike `/auto-review-loop` (which iterates on **research** — running experiments, collecting data, rewriting narrative), this skill iterates on **paper writing quality** — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.
## Constants
- **MAX_ROUNDS = 2** — Two rounds of review→fix→recompile. Empirically, Round 1 catches structural issues (4→6/10), Round 2 catches remaining presentation issues (6→7/10). Diminishing returns beyond 2 rounds for writing-only improvements. - **REVIEWER_MODEL = `gpt-5.6-sol`** — Model used via Codex MCP for paper review. - **REVIEWER_BIAS_GUARD = true** — When `true`, every review round uses a fresh `mcp__codex__codex` thread with no prior review context. Never use `mcp__codex__codex-reply` for review rounds. Set to `false` only for deliberate debugging of the legacy behavior. **Empirical evidence:** running the same paper with `codex-reply` + "since last round we did X" prompts inflated scores from real 3/10 → fake 8/10 across multiple rounds; switching to fresh threads recovered the true 3/10 assessment. - **REVIEW_LOG = `PAPER_IMPROVEMENT_LOG.md`** — Cumulative log of all rounds, stored in paper directory. - **HUMAN_CHECKPOINT = false** — When `true`, pause after each round's review and present score + weaknesses to the user. The user can approve fixes, provide custom modification instructions, skip specific fixes, or stop early. When `false` (default), runs fully autonomously. - **EDIT_WHITELIST = `null`** — Optional path to a YAML/JSON whitelist file constraining which paths and operations the fix-implementation step may touch. When `null` (default), all edits proceed unconstrained. When set via `— edit-whitelist <path>` (also accepts `— edit_whitelist <path>`), the loop loads the file at startup and consults it before each edit; rejected edits are logged to `PAPER_IMPROVEMENT_LOG.md` rather than silently dropped. See "Optional: Edit Whitelist" below.
> 💡 Override: `/auto-paper-improvement-loop "paper/" — human checkpoint: true`
## Optional: Style reference (`— style-ref: <source>`, opt-in)
Lets the user steer **structural fixes only** during improvement (section reordering hints, paragraph length nudges, figure density adjustments) toward a reference paper. **Default OFF — when the user does not pass `— style-ref`, do nothing differently from before.**
Only when `— style-ref: <source>` appears in `$ARGUMENTS`, run the helper FIRST, before the loop starts:
```bash # Resolve $STYLE_HELPER via the canonical strict-safe chain (see # shared-references/integration-contract.md §2). Policy A — gate: # unresolved helper means --style-ref cannot be satisfied, so abort. cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1 if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true fi if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true fi STYLE_HELPER=".aris/tools/extract_paper_style.py" [ -f "$STYLE_HELPER" ] || STYLE_HELPER="tools/extract_paper_style.py" [ -f "$STYLE_HELPER" ] || { [ -n "${ARIS_REPO:-}" ] && STYLE_HELPER="$ARIS_REPO/tools/extract_paper_style.py"; } [ -f "$STYLE_HELPER" ] || { echo "ERROR: extract_paper_style.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2 echo " Fix: rerun bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or copy the helper to tools/." >&2 echo " --style-ref cannot be satisfied; aborting." >&2 exit 1 } STYLE_STATUS=0 CACHE=$(python3 "$STYLE_HELPER" --source "<source>") || STYLE_STATUS=$? case "$STYLE_STATUS" in 0) ;; # use $CACHE/style_profile.md as structural guidance for the FIX phase only 2) echo "warning: style-ref skipped (missing optional dep)" >&2 ;; 3) echo "error: --style-ref source failed; aborting loop" >&2 ; exit 1 ;; *) echo "error: helper failed unexpectedly; aborting loop" >&2 ; exit 1 ;; esac ```
Sources accepted: local TeX dir / file, local PDF, arXiv id, http(s) URL. Overleaf URLs/IDs are rejected — clone via `/overleaf-sync setup <id>` first and pass the local clone path.
**Strict rules** (full contract in `tools/extract_paper_style.py` docstring):
- Use `style_profile.md` only during the **fix-implementation** phase, to nudge structural choices when applying reviewer feedback. Reviewer feedback always takes precedence; style ref is tie-breaker for *how* to apply a fix, not *whether* to apply it. - **Never copy prose, claims, examples, or terminology** from anything reachable through the cache when implementing fixes. - **Never pass `— style-ref` (or the cache contents) to the GPT-5.6-Sol reviewer sub-agent.** The Reviewer Independence Protocol below requires reviewers see only the artifact and the user's prompt — leaking the style ref would contaminate the review with author-side context. **This is the most critical invariant in this skill.**
## Optional: Edit Whitelist (`— edit-whitelist <path>`, opt-in)
Lets the caller hard-constrain which files and operations the **fix-implementation** step (Step 3 / Step 6) is allowed to touch. **Default OFF — when the user does not pass `— edit-whitelist` (or the alias `— edit_whitelist`), the loop applies all reviewer-driven edits without restriction, exactly as before.**
This is the parameter that upstream pipelines (e.g. `/resubmit-pipeline` Phase 2) use to enforce text-only resubmit microedits: no `.bib` mutations, no `.sty` / `.bst` mutations, no edits to prior-submission directories, no new `\cite{...}`, no new theorem environments, no new numerical claims.
### Schema
The whitelist file is YAML or JSON. All four sections are optional:
```yaml allowed_paths: - sec/*.tex - main.tex - figures/*.tex forbidden_paths: - "**/*.bib" - "**/*.sty" - "**/*.bst" - "../OldSubmission/**" forbidden_operations: - new_cite # blocks \cite{...}, \citep{...}, \citet{...}, \citeauthor{...} additions - new_bibitem # blocks \bibitem{...} additions - new_theorem_env # blocks \begin{theorem|lemma|proposition|corollary} additions - numerical_claim # blocks adding new numbers / percentages / metrics forbidden_deletions: # operations that block REMOVALS, not additions - delete_existing_cite # blocks removal of \cite{...} from the body (use citation-audit --soft-only instead) - delete_theorem_env # blocks removal of an existing \begin{theorem|...} block requires_user_approval_for: # operations that don't auto-reject but pause for explicit user OK - rewrite_abstract # paraphrasing the entire abstract triggers a checkpoint - rewrite_intro_first_para - delete_section max_edits_per_round: 30 # hard cap on number of accepted edits per round (rejections are not counted; if cap is hit, remaining proposed edits are deferred to the next round with a warning) rationale: "Resubmit mode: text-only microedits, paper structure frozen by user constraint." ```
### Resolution rules
- **`allowed_paths` empty AND `forbidden_paths` empty** → whitelist is a no-op (advisory: the file is loaded and `rationale` echoed to the log, but no path filtering is applied). - **`allowed_paths` empty, `forbidden_paths` non-empty** → all paths NOT matched by `forbidden_paths` are mutable. - **`allowed_paths` non-empty, `forbidden_paths` empty** → only paths matching `allowed_paths` are mutable. - **Both non-empty** → an edit is allowed iff the target matches `allowed_paths` AND does NOT match `forbidden_paths`. `forbidden_paths` always wins on overlap. - **`forbidden_operations` missing or empty** → no operation-level guard; only path-level filtering applies.
### Glob semantics
Use bash `extglob` / Python `fnmatch.fnmatch` semantics. `**` matches any depth (zero or more directory segments). Patterns are matched against the path **relative to the paper directory** (e.g. `paper/sec/intro.tex` matches `sec/*.tex` when paper-directory is `paper/`).
### Forbidden-operation detectors
For each candidate edit's diff (the new lines being added — deletions are exempt), the loop runs these regex checks and rejects if any forbidden operation matches:
| Operation | Detector (added lines only) | |-----------|------------------------------| | `new_cite` | `\\cite[a-zA-Z]*\{[^}]+\}` (catches `\cite`, `\citep`, `\citet`, `\citeauthor`, `\citeyear`, `\citealp`, etc.) | | `new_bibitem` | `\\bibitem\{[^}]+\}` | | `new_theorem_env` | `\\begin\{(theorem|lemma|proposition|corollary)\*?\}` | | `numerical_claim` | New token matching `\b\d+(\.\d+)?%?\b` that did NOT appear in the deleted/replaced lines (i.e. genuinely new numbers, not edits to existing ones) |
### Behavior at loop start (before Round 1 fix-implementation)
1. If `— edit-whitelist <path>` is present in `$ARGUMENTS`, set `EDIT_WHITELIST = <path>`. 2. Load the file (`yaml.safe_load`; if it fails, fall back to `json.loads`). On load failure, abort the loop with a clear error — do NOT silently proceed unconstrained. 3. Echo `rationale` (if present) into `PAPER_IMPROVEMENT_LOG.md` under a new "Edit Whitelist" preamble section so the audit trail records why edits were constrained.
### Behavior during fix-implementation (Steps 3 and 6)
Before applying each proposed edit:
1. Resolve target file path relative to the paper directory. 2. Path check: if `allowed_paths` is non-empty, target must match at least one pattern. Then if `forbidden_paths` is non-empty, target must NOT match any pattern. If either fails → reject as `path` violation. 3. Operation check: build the unified diff (or just the set of newly-added lines) for the proposed edit. For each entry in `forbidden_operations`, run its detector on the added lines. If any detector matches → reject as `operation` violation. 4. If all checks pass, apply the edit normally. 5. If rejected, append an entry to `PAPER_IMPROVEMENT_LOG.md` under a `## Rejected by edit_whitelist (Round N)` heading with this schema: ``` - file: <relative path> reason: path | operation pattern: <the offending forbidden_path glob, OR the offending forbidden_operation name + the matched substring> reviewer_concern: <the original Round-N weakness that motivated this edit> ``` 6. Continue with the remaining edits in the round. Do NOT abort the whole round
Decision snapshot
15,640 GitHub stars
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for auto-paper-improvement-loop, ready for a manual X post.
auto-paper-improvement-loop: Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recom... 15.6K stars https://www.openagentskill.com/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop?ref=x
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Sandbox only
mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsSandbox only
Install targets
Codex install prompt
Install the "auto-paper-improvement-loop" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/auto-paper-improvement-loop. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop","task":"Install auto-paper-improvement-loop","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Maintenance
fresh
2d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
16K
100/100 Quality · 71/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
16K GitHub stars
Repo activity
16K stars, 1.4K forks
Maintenance
2d since push
License
MIT
Install
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loopDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
Agent should check
Copy prompt
Task: Use auto-paper-improvement-loop in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
Install command: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
LLM text format
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install?format=text
Find alternatives
/api/skills/search?q=auto-paper-improvement-loop&limit=3
Agent prompt
Use auto-paper-improvement-loop for this task. Review https://www.openagentskill.com/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install, then install with: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loopRegistry metadata
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/api/registry/manifest/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop
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Install alias
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Recommend
/api/registry/recommend?task=Use%20auto-paper-improvement-loop%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Workflow automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS16K GitHub stars
Stars/forks activity
PASS16K stars, 1.4K forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
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Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: auto-paper-improvement-loop description: "Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper." argument-hint: "[paper-directory] [— style-ref: <source>] [— edit-whitelist <path>]" allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply ---
# Auto Paper Improvement Loop: Review → Fix → Recompile
> 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** It > already loops internally (review → fix → recompile) with its own round > structure and a deliberate fresh-reviewer bias guard each round (no > `codex-reply`). Re-asking it to "improve the paper" on a > wall-clock timer produces no new signal — quality changes when the *review* > changes, not when the clock ticks — and a timed re-run that also accepts its > own output to decide when to stop crosses into self-acquittal > (`acceptance-gate.md`). Schedule the *external wait that precedes it*, not the > improvement loop. See > [`shared-references/external-cadence.md`](../shared-references/external-cadence.md).
Autonomously improve the paper at: **$ARGUMENTS**
## Context
This skill is designed to run **after** Workflow 3 (`/paper-plan` → `/paper-figure` → `/paper-write` → `/paper-compile`). It takes a compiled paper and iteratively improves it through external LLM review.
Unlike `/auto-review-loop` (which iterates on **research** — running experiments, collecting data, rewriting narrative), this skill iterates on **paper writing quality** — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.
## Constants
- **MAX_ROUNDS = 2** — Two rounds of review→fix→recompile. Empirically, Round 1 catches structural issues (4→6/10), Round 2 catches remaining presentation issues (6→7/10). Diminishing returns beyond 2 rounds for writing-only improvements. - **REVIEWER_MODEL = `gpt-5.6-sol`** — Model used via Codex MCP for paper review. - **REVIEWER_BIAS_GUARD = true** — When `true`, every review round uses a fresh `mcp__codex__codex` thread with no prior review context. Never use `mcp__codex__codex-reply` for review rounds. Set to `false` only for deliberate debugging of the legacy behavior. **Empirical evidence:** running the same paper with `codex-reply` + "since last round we did X" prompts inflated scores from real 3/10 → fake 8/10 across multiple rounds; switching to fresh threads recovered the true 3/10 assessment. - **REVIEW_LOG = `PAPER_IMPROVEMENT_LOG.md`** — Cumulative log of all rounds, stored in paper directory. - **HUMAN_CHECKPOINT = false** — When `true`, pause after each round's review and present score + weaknesses to the user. The user can approve fixes, provide custom modification instructions, skip specific fixes, or stop early. When `false` (default), runs fully autonomously. - **EDIT_WHITELIST = `null`** — Optional path to a YAML/JSON whitelist file constraining which paths and operations the fix-implementation step may touch. When `null` (default), all edits proceed unconstrained. When set via `— edit-whitelist <path>` (also accepts `— edit_whitelist <path>`), the loop loads the file at startup and consults it before each edit; rejected edits are logged to `PAPER_IMPROVEMENT_LOG.md` rather than silently dropped. See "Optional: Edit Whitelist" below.
> 💡 Override: `/auto-paper-improvement-loop "paper/" — human checkpoint: true`
## Optional: Style reference (`— style-ref: <source>`, opt-in)
Lets the user steer **structural fixes only** during improvement (section reordering hints, paragraph length nudges, figure density adjustments) toward a reference paper. **Default OFF — when the user does not pass `— style-ref`, do nothing differently from before.**
Only when `— style-ref: <source>` appears in `$ARGUMENTS`, run the helper FIRST, before the loop starts:
```bash # Resolve $STYLE_HELPER via the canonical strict-safe chain (see # shared-references/integration-contract.md §2). Policy A — gate: # unresolved helper means --style-ref cannot be satisfied, so abort. cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1 if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true fi if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true fi STYLE_HELPER=".aris/tools/extract_paper_style.py" [ -f "$STYLE_HELPER" ] || STYLE_HELPER="tools/extract_paper_style.py" [ -f "$STYLE_HELPER" ] || { [ -n "${ARIS_REPO:-}" ] && STYLE_HELPER="$ARIS_REPO/tools/extract_paper_style.py"; } [ -f "$STYLE_HELPER" ] || { echo "ERROR: extract_paper_style.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2 echo " Fix: rerun bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or copy the helper to tools/." >&2 echo " --style-ref cannot be satisfied; aborting." >&2 exit 1 } STYLE_STATUS=0 CACHE=$(python3 "$STYLE_HELPER" --source "<source>") || STYLE_STATUS=$? case "$STYLE_STATUS" in 0) ;; # use $CACHE/style_profile.md as structural guidance for the FIX phase only 2) echo "warning: style-ref skipped (missing optional dep)" >&2 ;; 3) echo "error: --style-ref source failed; aborting loop" >&2 ; exit 1 ;; *) echo "error: helper failed unexpectedly; aborting loop" >&2 ; exit 1 ;; esac ```
Sources accepted: local TeX dir / file, local PDF, arXiv id, http(s) URL. Overleaf URLs/IDs are rejected — clone via `/overleaf-sync setup <id>` first and pass the local clone path.
**Strict rules** (full contract in `tools/extract_paper_style.py` docstring):
- Use `style_profile.md` only during the **fix-implementation** phase, to nudge structural choices when applying reviewer feedback. Reviewer feedback always takes precedence; style ref is tie-breaker for *how* to apply a fix, not *whether* to apply it. - **Never copy prose, claims, examples, or terminology** from anything reachable through the cache when implementing fixes. - **Never pass `— style-ref` (or the cache contents) to the GPT-5.6-Sol reviewer sub-agent.** The Reviewer Independence Protocol below requires reviewers see only the artifact and the user's prompt — leaking the style ref would contaminate the review with author-side context. **This is the most critical invariant in this skill.**
## Optional: Edit Whitelist (`— edit-whitelist <path>`, opt-in)
Lets the caller hard-constrain which files and operations the **fix-implementation** step (Step 3 / Step 6) is allowed to touch. **Default OFF — when the user does not pass `— edit-whitelist` (or the alias `— edit_whitelist`), the loop applies all reviewer-driven edits without restriction, exactly as before.**
This is the parameter that upstream pipelines (e.g. `/resubmit-pipeline` Phase 2) use to enforce text-only resubmit microedits: no `.bib` mutations, no `.sty` / `.bst` mutations, no edits to prior-submission directories, no new `\cite{...}`, no new theorem environments, no new numerical claims.
### Schema
The whitelist file is YAML or JSON. All four sections are optional:
```yaml allowed_paths: - sec/*.tex - main.tex - figures/*.tex forbidden_paths: - "**/*.bib" - "**/*.sty" - "**/*.bst" - "../OldSubmission/**" forbidden_operations: - new_cite # blocks \cite{...}, \citep{...}, \citet{...}, \citeauthor{...} additions - new_bibitem # blocks \bibitem{...} additions - new_theorem_env # blocks \begin{theorem|lemma|proposition|corollary} additions - numerical_claim # blocks adding new numbers / percentages / metrics forbidden_deletions: # operations that block REMOVALS, not additions - delete_existing_cite # blocks removal of \cite{...} from the body (use citation-audit --soft-only instead) - delete_theorem_env # blocks removal of an existing \begin{theorem|...} block requires_user_approval_for: # operations that don't auto-reject but pause for explicit user OK - rewrite_abstract # paraphrasing the entire abstract triggers a checkpoint - rewrite_intro_first_para - delete_section max_edits_per_round: 30 # hard cap on number of accepted edits per round (rejections are not counted; if cap is hit, remaining proposed edits are deferred to the next round with a warning) rationale: "Resubmit mode: text-only microedits, paper structure frozen by user constraint." ```
### Resolution rules
- **`allowed_paths` empty AND `forbidden_paths` empty** → whitelist is a no-op (advisory: the file is loaded and `rationale` echoed to the log, but no path filtering is applied). - **`allowed_paths` empty, `forbidden_paths` non-empty** → all paths NOT matched by `forbidden_paths` are mutable. - **`allowed_paths` non-empty, `forbidden_paths` empty** → only paths matching `allowed_paths` are mutable. - **Both non-empty** → an edit is allowed iff the target matches `allowed_paths` AND does NOT match `forbidden_paths`. `forbidden_paths` always wins on overlap. - **`forbidden_operations` missing or empty** → no operation-level guard; only path-level filtering applies.
### Glob semantics
Use bash `extglob` / Python `fnmatch.fnmatch` semantics. `**` matches any depth (zero or more directory segments). Patterns are matched against the path **relative to the paper directory** (e.g. `paper/sec/intro.tex` matches `sec/*.tex` when paper-directory is `paper/`).
### Forbidden-operation detectors
For each candidate edit's diff (the new lines being added — deletions are exempt), the loop runs these regex checks and rejects if any forbidden operation matches:
| Operation | Detector (added lines only) | |-----------|------------------------------| | `new_cite` | `\\cite[a-zA-Z]*\{[^}]+\}` (catches `\cite`, `\citep`, `\citet`, `\citeauthor`, `\citeyear`, `\citealp`, etc.) | | `new_bibitem` | `\\bibitem\{[^}]+\}` | | `new_theorem_env` | `\\begin\{(theorem|lemma|proposition|corollary)\*?\}` | | `numerical_claim` | New token matching `\b\d+(\.\d+)?%?\b` that did NOT appear in the deleted/replaced lines (i.e. genuinely new numbers, not edits to existing ones) |
### Behavior at loop start (before Round 1 fix-implementation)
1. If `— edit-whitelist <path>` is present in `$ARGUMENTS`, set `EDIT_WHITELIST = <path>`. 2. Load the file (`yaml.safe_load`; if it fails, fall back to `json.loads`). On load failure, abort the loop with a clear error — do NOT silently proceed unconstrained. 3. Echo `rationale` (if present) into `PAPER_IMPROVEMENT_LOG.md` under a new "Edit Whitelist" preamble section so the audit trail records why edits were constrained.
### Behavior during fix-implementation (Steps 3 and 6)
Before applying each proposed edit:
1. Resolve target file path relative to the paper directory. 2. Path check: if `allowed_paths` is non-empty, target must match at least one pattern. Then if `forbidden_paths` is non-empty, target must NOT match any pattern. If either fails → reject as `path` violation. 3. Operation check: build the unified diff (or just the set of newly-added lines) for the proposed edit. For each entry in `forbidden_operations`, run its detector on the added lines. If any detector matches → reject as `operation` violation. 4. If all checks pass, apply the edit normally. 5. If rejected, append an entry to `PAPER_IMPROVEMENT_LOG.md` under a `## Rejected by edit_whitelist (Round N)` heading with this schema: ``` - file: <relative path> reason: path | operation pattern: <the offending forbidden_path glob, OR the offending forbidden_operation name + the matched substring> reviewer_concern: <the original Round-N weakness that motivated this edit> ``` 6. Continue with the remaining edits in the round. Do NOT abort the whole round
Decision snapshot
15,640 GitHub stars
Audit
Install and adoption review
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Outcome reports after resolve, review, install, and one narrow run.
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Install
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Growth loop
Scenario-led draft for auto-paper-improvement-loop, ready for a manual X post.
auto-paper-improvement-loop: Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recom... 15.6K stars https://www.openagentskill.com/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop?ref=x
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsSandbox only
Install targets
Codex install prompt
Install the "auto-paper-improvement-loop" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/auto-paper-improvement-loop. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop","task":"Install auto-paper-improvement-loop","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Maintenance
fresh
2d since push
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
16K
100/100 Quality · 71/100 Trust
Coverage tags
Review notes
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent adoption scorecard
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Quality
ExcellentHigh-confidence pick with strong adoption and healthy maintenance signals.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
16K GitHub stars
Repo activity
16K stars, 1.4K forks
Maintenance
2d since push
License
MIT
Install
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loopDo not use when
Alternative
1.9K Stars
npx skills add yanliudesign/mono-color-skill --skill mono-color
Alternative
61.0K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
28.0K Stars
npx skills add assafelovic/gpt-researcher
Agent safety v2
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Skill may read or write project files, documents, generated artifacts, or local workspace state.
high
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
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/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
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Install handoff
/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
Agent should check
Copy prompt
Task: Use auto-paper-improvement-loop in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20auto-paper-improvement-loop%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
Install command: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loop
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/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install
LLM text format
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Find alternatives
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Agent prompt
Use auto-paper-improvement-loop for this task. Review https://www.openagentskill.com/api/skills/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop/install, then install with: npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-paper-improvement-loopRegistry metadata
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LLM text
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Install alias
/api/registry/install/wanshuiyin-auto-claude-code-research-in-sleep-auto-paper-improvement-loop
Recommend
/api/registry/recommend?task=Use%20auto-paper-improvement-loop%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Role in stack
Primary pick
Primary fit
Workflow automation
Trust label
Production-ready
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
PASS16K GitHub stars
Stars/forks activity
PASS16K stars, 1.4K forks; issue activity unavailable in current metadata
Recent maintenance
PASS2d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
High-confidence pick with strong adoption and healthy maintenance signals.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Search private knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
Inspect, patch, and verify code
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Similar skills that may fit this task.
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills for Claude Code: research → write → review → revise → finalize
Run autonomous deep research over web and local sources
--- name: auto-paper-improvement-loop description: "Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper." argument-hint: "[paper-directory] [— style-ref: <source>] [— edit-whitelist <path>]" allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, mcp__codex__codex, mcp__codex__codex-reply ---
# Auto Paper Improvement Loop: Review → Fix → Recompile
> 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** It > already loops internally (review → fix → recompile) with its own round > structure and a deliberate fresh-reviewer bias guard each round (no > `codex-reply`). Re-asking it to "improve the paper" on a > wall-clock timer produces no new signal — quality changes when the *review* > changes, not when the clock ticks — and a timed re-run that also accepts its > own output to decide when to stop crosses into self-acquittal > (`acceptance-gate.md`). Schedule the *external wait that precedes it*, not the > improvement loop. See > [`shared-references/external-cadence.md`](../shared-references/external-cadence.md).
Autonomously improve the paper at: **$ARGUMENTS**
## Context
This skill is designed to run **after** Workflow 3 (`/paper-plan` → `/paper-figure` → `/paper-write` → `/paper-compile`). It takes a compiled paper and iteratively improves it through external LLM review.
Unlike `/auto-review-loop` (which iterates on **research** — running experiments, collecting data, rewriting narrative), this skill iterates on **paper writing quality** — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.
## Constants
- **MAX_ROUNDS = 2** — Two rounds of review→fix→recompile. Empirically, Round 1 catches structural issues (4→6/10), Round 2 catches remaining presentation issues (6→7/10). Diminishing returns beyond 2 rounds for writing-only improvements. - **REVIEWER_MODEL = `gpt-5.6-sol`** — Model used via Codex MCP for paper review. - **REVIEWER_BIAS_GUARD = true** — When `true`, every review round uses a fresh `mcp__codex__codex` thread with no prior review context. Never use `mcp__codex__codex-reply` for review rounds. Set to `false` only for deliberate debugging of the legacy behavior. **Empirical evidence:** running the same paper with `codex-reply` + "since last round we did X" prompts inflated scores from real 3/10 → fake 8/10 across multiple rounds; switching to fresh threads recovered the true 3/10 assessment. - **REVIEW_LOG = `PAPER_IMPROVEMENT_LOG.md`** — Cumulative log of all rounds, stored in paper directory. - **HUMAN_CHECKPOINT = false** — When `true`, pause after each round's review and present score + weaknesses to the user. The user can approve fixes, provide custom modification instructions, skip specific fixes, or stop early. When `false` (default), runs fully autonomously. - **EDIT_WHITELIST = `null`** — Optional path to a YAML/JSON whitelist file constraining which paths and operations the fix-implementation step may touch. When `null` (default), all edits proceed unconstrained. When set via `— edit-whitelist <path>` (also accepts `— edit_whitelist <path>`), the loop loads the file at startup and consults it before each edit; rejected edits are logged to `PAPER_IMPROVEMENT_LOG.md` rather than silently dropped. See "Optional: Edit Whitelist" below.
> 💡 Override: `/auto-paper-improvement-loop "paper/" — human checkpoint: true`
## Optional: Style reference (`— style-ref: <source>`, opt-in)
Lets the user steer **structural fixes only** during improvement (section reordering hints, paragraph length nudges, figure density adjustments) toward a reference paper. **Default OFF — when the user does not pass `— style-ref`, do nothing differently from before.**
Only when `— style-ref: <source>` appears in `$ARGUMENTS`, run the helper FIRST, before the loop starts:
```bash # Resolve $STYLE_HELPER via the canonical strict-safe chain (see # shared-references/integration-contract.md §2). Policy A — gate: # unresolved helper means --style-ref cannot be satisfied, so abort. cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1 if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true fi if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true fi STYLE_HELPER=".aris/tools/extract_paper_style.py" [ -f "$STYLE_HELPER" ] || STYLE_HELPER="tools/extract_paper_style.py" [ -f "$STYLE_HELPER" ] || { [ -n "${ARIS_REPO:-}" ] && STYLE_HELPER="$ARIS_REPO/tools/extract_paper_style.py"; } [ -f "$STYLE_HELPER" ] || { echo "ERROR: extract_paper_style.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2 echo " Fix: rerun bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or copy the helper to tools/." >&2 echo " --style-ref cannot be satisfied; aborting." >&2 exit 1 } STYLE_STATUS=0 CACHE=$(python3 "$STYLE_HELPER" --source "<source>") || STYLE_STATUS=$? case "$STYLE_STATUS" in 0) ;; # use $CACHE/style_profile.md as structural guidance for the FIX phase only 2) echo "warning: style-ref skipped (missing optional dep)" >&2 ;; 3) echo "error: --style-ref source failed; aborting loop" >&2 ; exit 1 ;; *) echo "error: helper failed unexpectedly; aborting loop" >&2 ; exit 1 ;; esac ```
Sources accepted: local TeX dir / file, local PDF, arXiv id, http(s) URL. Overleaf URLs/IDs are rejected — clone via `/overleaf-sync setup <id>` first and pass the local clone path.
**Strict rules** (full contract in `tools/extract_paper_style.py` docstring):
- Use `style_profile.md` only during the **fix-implementation** phase, to nudge structural choices when applying reviewer feedback. Reviewer feedback always takes precedence; style ref is tie-breaker for *how* to apply a fix, not *whether* to apply it. - **Never copy prose, claims, examples, or terminology** from anything reachable through the cache when implementing fixes. - **Never pass `— style-ref` (or the cache contents) to the GPT-5.6-Sol reviewer sub-agent.** The Reviewer Independence Protocol below requires reviewers see only the artifact and the user's prompt — leaking the style ref would contaminate the review with author-side context. **This is the most critical invariant in this skill.**
## Optional: Edit Whitelist (`— edit-whitelist <path>`, opt-in)
Lets the caller hard-constrain which files and operations the **fix-implementation** step (Step 3 / Step 6) is allowed to touch. **Default OFF — when the user does not pass `— edit-whitelist` (or the alias `— edit_whitelist`), the loop applies all reviewer-driven edits without restriction, exactly as before.**
This is the parameter that upstream pipelines (e.g. `/resubmit-pipeline` Phase 2) use to enforce text-only resubmit microedits: no `.bib` mutations, no `.sty` / `.bst` mutations, no edits to prior-submission directories, no new `\cite{...}`, no new theorem environments, no new numerical claims.
### Schema
The whitelist file is YAML or JSON. All four sections are optional:
```yaml allowed_paths: - sec/*.tex - main.tex - figures/*.tex forbidden_paths: - "**/*.bib" - "**/*.sty" - "**/*.bst" - "../OldSubmission/**" forbidden_operations: - new_cite # blocks \cite{...}, \citep{...}, \citet{...}, \citeauthor{...} additions - new_bibitem # blocks \bibitem{...} additions - new_theorem_env # blocks \begin{theorem|lemma|proposition|corollary} additions - numerical_claim # blocks adding new numbers / percentages / metrics forbidden_deletions: # operations that block REMOVALS, not additions - delete_existing_cite # blocks removal of \cite{...} from the body (use citation-audit --soft-only instead) - delete_theorem_env # blocks removal of an existing \begin{theorem|...} block requires_user_approval_for: # operations that don't auto-reject but pause for explicit user OK - rewrite_abstract # paraphrasing the entire abstract triggers a checkpoint - rewrite_intro_first_para - delete_section max_edits_per_round: 30 # hard cap on number of accepted edits per round (rejections are not counted; if cap is hit, remaining proposed edits are deferred to the next round with a warning) rationale: "Resubmit mode: text-only microedits, paper structure frozen by user constraint." ```
### Resolution rules
- **`allowed_paths` empty AND `forbidden_paths` empty** → whitelist is a no-op (advisory: the file is loaded and `rationale` echoed to the log, but no path filtering is applied). - **`allowed_paths` empty, `forbidden_paths` non-empty** → all paths NOT matched by `forbidden_paths` are mutable. - **`allowed_paths` non-empty, `forbidden_paths` empty** → only paths matching `allowed_paths` are mutable. - **Both non-empty** → an edit is allowed iff the target matches `allowed_paths` AND does NOT match `forbidden_paths`. `forbidden_paths` always wins on overlap. - **`forbidden_operations` missing or empty** → no operation-level guard; only path-level filtering applies.
### Glob semantics
Use bash `extglob` / Python `fnmatch.fnmatch` semantics. `**` matches any depth (zero or more directory segments). Patterns are matched against the path **relative to the paper directory** (e.g. `paper/sec/intro.tex` matches `sec/*.tex` when paper-directory is `paper/`).
### Forbidden-operation detectors
For each candidate edit's diff (the new lines being added — deletions are exempt), the loop runs these regex checks and rejects if any forbidden operation matches:
| Operation | Detector (added lines only) | |-----------|------------------------------| | `new_cite` | `\\cite[a-zA-Z]*\{[^}]+\}` (catches `\cite`, `\citep`, `\citet`, `\citeauthor`, `\citeyear`, `\citealp`, etc.) | | `new_bibitem` | `\\bibitem\{[^}]+\}` | | `new_theorem_env` | `\\begin\{(theorem|lemma|proposition|corollary)\*?\}` | | `numerical_claim` | New token matching `\b\d+(\.\d+)?%?\b` that did NOT appear in the deleted/replaced lines (i.e. genuinely new numbers, not edits to existing ones) |
### Behavior at loop start (before Round 1 fix-implementation)
1. If `— edit-whitelist <path>` is present in `$ARGUMENTS`, set `EDIT_WHITELIST = <path>`. 2. Load the file (`yaml.safe_load`; if it fails, fall back to `json.loads`). On load failure, abort the loop with a clear error — do NOT silently proceed unconstrained. 3. Echo `rationale` (if present) into `PAPER_IMPROVEMENT_LOG.md` under a new "Edit Whitelist" preamble section so the audit trail records why edits were constrained.
### Behavior during fix-implementation (Steps 3 and 6)
Before applying each proposed edit:
1. Resolve target file path relative to the paper directory. 2. Path check: if `allowed_paths` is non-empty, target must match at least one pattern. Then if `forbidden_paths` is non-empty, target must NOT match any pattern. If either fails → reject as `path` violation. 3. Operation check: build the unified diff (or just the set of newly-added lines) for the proposed edit. For each entry in `forbidden_operations`, run its detector on the added lines. If any detector matches → reject as `operation` violation. 4. If all checks pass, apply the edit normally. 5. If rejected, append an entry to `PAPER_IMPROVEMENT_LOG.md` under a `## Rejected by edit_whitelist (Round N)` heading with this schema: ``` - file: <relative path> reason: path | operation pattern: <the offending forbidden_path glob, OR the offending forbidden_operation name + the matched substring> reviewer_concern: <the original Round-N weakness that motivated this edit> ``` 6. Continue with the remaining edits in the round. Do NOT abort the whole round
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mono-color
Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.
1.9K StarsLast30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
61.0K StarsAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K StarsGPT Researcher
Run autonomous deep research over web and local sources
28.0K StarsPermission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Install readiness