{"slug":"claesbackman-review-paper-code","name":"review-paper-code","description":"Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.","long_description":"---\nname: review-paper-code\ndescription: Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.\nargument-hint: [optional: path/to/main.tex] [optional: path/to/code_dir] [optional: main|full]\nallowed-tools: Read, Write, Edit, Glob, Grep, Bash, Agent\ndisable-model-invocation: true\n---\n\n# Review Paper Code\n\nReview a research project's paper and code for reproducibility, code quality, and paper-code alignment. Be constructive, concrete, and calibrated. Treat gaps as items to verify, not accusations.\n\n## Scope\n\nThis skill supports:\n- LaTeX papers\n- Stata (`.do`), R (`.R`, `.r`), and Python (`.py`) code\n\nDefault review depth:\n- `main`: prioritize the main paper, main scripts, and core outputs\n- `full`: inspect all detected code files in scope\n\nIf no depth is provided, default to `main`.\n\n## Phase 1: Discover the Project\n\nFirst parse `$ARGUMENTS`:\n- If one argument looks like a `.tex` path, use it as `PAPER_FILE`.\n- If one argument looks like a directory path, use it as `CODE_DIR`.\n- If one argument is `main` or `full`, use it as `REVIEW_DEPTH`.\n\nIf any of the above are missing, auto-detect them.\n\n### 1. Find the paper\n\nUse Glob to search for `**/*.tex`, excluding obvious build folders such as `_minted-*`, `build/`, `output/`, `.git/`, `node_modules/`.\n\nIdentify the main paper file as the best candidate containing `\\documentclass` or `\\begin{document}`.\n\nIf multiple candidates exist, first discard files whose document class is `beamer` (slides) and files whose name or folder suggests an old draft or a response letter (`response*`, `letter*`, `slides*`, `old*`, `archive/`, etc.). Then prefer:\n1. A path explicitly provided in `$ARGUMENTS`\n2. A file in `Writing/`, `writing/`, `Paper/`, `paper/`, `Draft/`, or the repo root\n3. The file that appears to include the most component files via `\\input{}` / `\\include{}`\n\nRecord the result as `PAPER_FILE`.\n\n### 2. Find the code\n\nIf `CODE_DIR` was not provided, look for likely code roots in this order:\n- `Code/`\n- `Analysis/`\n- `code/`\n- `analysis/`\n- `scripts/`\n- `src/`\n- `programs/`\n- `replication/`\n\nIf no single directory is clearly best, use the repo root and limit later discovery to likely code files.\n\nRecord the result as `CODE_DIR`.\n\n### 3. Find code files\n\nWithin `CODE_DIR` and subdirectories, find:\n- `**/*.do`\n- `**/*.R`\n- `**/*.r`\n- `**/*.py`\n\nExclude obvious caches, environments, and generated folders where appropriate.\n\nIf `REVIEW_DEPTH = main`, prioritize:\n- Master scripts such as `main.do`, `master.do`, `run_all.R`, `main.R`, `main.py`, `run.py`\n- Files referenced by those scripts\n- Files that generate tables, figures, or final datasets\n- If no master script exists, select the most central files and cap the initial review set at a reasonable number\n\nIf `REVIEW_DEPTH = full`, include all detected code files.\n\nRecord:\n- `CODE_FILES_ALL`\n- `CODE_FILES_REVIEWED`\n- languages present\n\n### 4. Find supporting documentation\n\nLook for:\n- `README.md`, `README.txt`, `readme.md`\n- `requirements.txt`, `environment.yml`, `pyproject.toml`\n- `renv.lock`, `DESCRIPTION`\n\nRecord relevant files as available.\n\n### 5. Handle ambiguity gracefully\n\nIf you find a paper and at least some code, continue even if discovery is imperfect.\n\nOnly stop if you cannot find either:\n- a main paper file, or\n- any relevant Stata, R, or Python code files\n\nIf you stop, tell the user briefly what was missing and what paths they can pass explicitly.\n\nBefore proceeding, tell the user:\n- the paper file chosen\n- the code directory chosen\n- the number of code files detected and the number selected for review\n- the review depth\n- any ambiguity worth noting\n\n## Phase 2: Read the Paper\n\nRead `PAPER_FILE`.\n\nRecursively read files referenced by:\n- `\\input{}`\n- `\\include{}`\n- `\\subfile{}`\n\nExtract a compact working summary for later cross-checking:\n- Paper title\n- Main research question\n- Main sample description\n- Main data sources\n- Main dependent variables\n- Main explanatory variables or treatments\n- Main estimation methods\n- Fixed effects and clustering, if stated\n- Main sample restrictions\n- Main tables and figures only\n- Headline quantitative claims only\n\nDo not try to extract every statistic in the paper. Prioritize the main empirical design and the outputs most likely to map to code.\n\nStore this as `PAPER_SUMMARY`.\n\n## Phase 3: Launch 2 Agents in Parallel\n\nIn a single message, launch both agents using the Agent tool with `subagent_type: \"general-purpose\"`.\n\nEach agent must produce a compact, high-signal output. Do not ask for exhaustive per-file prose on every file unless the project is very small.\n\n---\n\n### AGENT A: Code Reproducibility and Quality\n\nStore as `CODE_REVIEW_SUMMARY`.\n\nPrompt:\n\n> You are reviewing research code for reproducibility and code quality in a social science / economics project.\n>\n> Files in scope:\n> - Reviewed code files: [insert `CODE_FILES_REVIEWED`]\n> - README / documentation files: [insert discovered supporting files or \"none found\"]\n>\n> Review ONLY the files in scope. Do not use Glob or Grep to discover other files, and ignore any previous review reports (`code_review_report*.md`, `PRE_SUBMISSION_REVIEW_*.md`, `QUICK_REVIEW_*.md`, anything in a `reviews/` folder) — they must not influence your review.\n>\n> Review the files and produce a compact report focused on the most decision-relevant findings.\n>\n> Check:\n> 1. Hardcoded absolute paths or machine-specific assumptions\n> 2. Randomized procedures without an obvious seed in local or upstream execution context\n> 3. Outputs that appear to be consumed but not obviously generated in the reviewed pipeline\n> 4. Data inputs and whether path conventions are consistent\n> 5. Dependency management and software requirements\n> 6. Run order and presence of a master script or documented pipeline\n> 7. Large commented-out blocks, weak script structure, or hard-to-follow long files\n> 8. Opaque transformations, unexplained filters, recodes, merges, or thresholds that are important for interpretation\n>\n> Use these labels:\n> - PASS: looks solid\n> - NOTE: minor improvement opportunity\n> - VERIFY: worth human confirmation before treating as a problem\n> - MISSING: expected project support file or documentation is absent\n>\n> Output exactly these sections:\n>\n> ## Overall\n> 3-6 bullets on the overall state of the codebase.\n>\n> ## Top Findings\n> Up to 10 items total, ordered by importance.\n> Format each item as:\n> - [LABEL] Short finding title — file(s): line reference(s) if available — why it matters — what to check next\n>\n> ## Strengths\n> 3-8 bullets with genuine positives.\n>\n> ## Reproducibility Checklist\n> One line each for:\n> - Relative paths\n> - Random seed practice\n> - Outputs generated by pipeline\n> - Dependency management\n> - Run order\n> - README / documentation\n>\n> Use this format:\n> - Check name: PASS / NOTE / VERIFY / MISSING — brief note\n>\n> ## File Notes\n> Include brief notes only for files that have a VERIFY, NOTE, or especially strong positive signal.\n> Use at most 1-3 bullets per file.\n>\n> Be calibrated. If something might be handled in an upstream script, say so.\n\n---\n\n### AGENT B: Paper-to-Code Mapping\n\nStore as `MAPPING_SUMMARY`.\n\nPrompt:\n\n> You are mapping a research paper's main empirical claims to its code implementation.\n>\n> Inputs:\n> - Paper summary: [insert `PAPER_SUMMARY`]\n> - Reviewed code files: [insert `CODE_FILES_REVIEWED`]\n> - Code directory: [insert `CODE_DIR`]\n>\n> Read the code files as needed and identify whether the paper's core empirical design appears in the code. Confine your reading to the listed code files and files inside the code directory that they reference. Ignore any previous review reports (`code_review_report*.md`, `PRE_SUBMISSION_REVIEW_*.md`, `QUICK_REVIEW_*.md`, anything in a `reviews/` folder) and old paper drafts — they must not influence the mapping.\n>\n> Focus on the main paper elements only:\n> 1. Main tables and figures\n> 2. Main variables and treatments\n> 3. Main sample restrictions and time period\n> 4. Main estimation methods\n> 5. Fixed effects and clustering, if central\n> 6. Main datasets or intermediate analysis files\n>\n> Use these confidence labels:\n> - HIGH: clear and specific match\n> - MEDIUM: plausible match but not airtight\n> - LOW: weak or indirect match\n> - NOT FOUND: no plausible match found in reviewed files\n>\n> Output exactly these sections:\n>\n> ## Verified Matches\n> Up to 10 bullets.\n> Format:\n> - Paper element -> Code evidence -> HIGH / MEDIUM -> brief note\n>\n> ## Items To Verify\n> Up to 12 bullets.\n> Format:\n> - Paper element -> Code evidence or absence -> LOW / NOT FOUND / MEDIUM -> why this deserves a check\n>\n> ## Likely Discrepancies\n> Only include items where paper and code appear to point in different directions.\n> Use up to 8 bullets.\n>\n> ## Coverage Notes\n> 3-6 bullets on what was easy to match, what was ambiguous, and what may sit outside the reviewed files.\n>\n> Be conservative. Do not mark a match HIGH unless the specification, output, or variable mapping is genuinely clear.\n\n## Phase 4: Synthesize\n\nAfter both agents return, synthesize the results yourself.\n\nDo not launch another critic agent by default. Instead:\n- compare the two outputs for agreement and tension\n- downgrade any overconfident claims\n- note where limited file coverage or naming ambiguity weakens confidence\n\nIf the repo is unusually complex and a second-pass critic is truly necessary, you may launch one additional agent. Otherwise, keep the workflow lean.\n\nCreate:\n- `OVERALL_ASSESSMENT`: 2-4 sentences leading with what works\n- `TOP_ACTIONS`: 3-8 concrete next steps, ordered by importance\n- `MATCHED_ITEMS`: high-confidence paper-code matches\n- `VERIFY_ITEMS`: gaps or ambiguous matches worth checking\n- `NOT_FOUND_ITEMS`: important paper elements with no plausible code match in reviewed files\n\n## Phase 5: Write the Report\n\nWrite the final report to a `reviews/` subfolder of the current working directory (create it if it does not exist) as:\n- `reviews/code_review_report.md`\n\nKeeping the report in `reviews/` prevents it from being picked up as project material by future review runs.\n\nUse this structure:\n\n```markdown\n# Code Review Report: [Paper Title]\n\n*Reviewed: [today's date] | Languages: [languages found] | Depth: [REVIEW_DEPTH] | Paper: [PAPER_FILE filename]*\n\n## Overall Assessment\n\n[2-4 sentences. Lead with strengths. Then summarize the main reproducibility or alignment issues worth checking.]\n\n## What's Working Well\n\n- [Specific positive]\n- [Specific positive]\n- [Specific positive]\n\n## Reproducibility Checklist\n\n| Check | Status | Details |\n|---|---|---|\n| Relative file paths | [PASS / NOTE / VERIFY / MISSING] | [...] |\n| Random seed practice | [PASS / NOTE / VERIFY / MISSING] | [...] |\n| Outputs generated by pipeline | [PASS / NOTE / VERIFY / MISSING] | [...] |\n| Dependency management | [PASS / NOTE / VERIFY / MISSING] | [...] |\n| Run order documented | [PASS / NOTE / VERIFY / MISSING] | [...] |\n| README / documentation | [PASS / NOTE / VERIFY / MISSING] | [...] |\n\n## Code Quality Summary\n\n[Short prose summary grouped by module, pipeline stage, or only the files with notable findings. Do not force one paragraph per file if the project is large.]\n\n## Paper-Code Consistency\n\n### Matched\n- [High-confidence match]\n\n### Items To Verify\n- [Paper element] — [what the paper says] — [what the code appears to do] — [why it is worth checking] — [specific suggested next step]\n\n### Not Found In Reviewed Files\n- [Important paper element] — [brief note]\n\n## Suggested Next Steps\n\n1. ...\n2. ...\n3. ...\n\n## Appendix: Compact Evidence\n\n### Code Review Summary\n[Paste `CODE_REVIEW_SUMMARY`]\n\n### Paper Summary\n[Paste the compact `PAPER_SUMMARY`]\n\n### Mapping Summary\n[Paste `MAPPING_SUMMARY`]\n```\n\nKeep the final report readable. Prefer concise, high-signal summaries over exhaustive dumps.\n\n## Final U","tagline":"Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.","category":"research","tags":["agent-skill"],"author":"claesbackman","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github fast track","sourceDetail":"claesbackman/AI-research-feedback","creatorName":"claesbackman","creatorUrl":"https://github.com/claesbackman","sourceUrl":"https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/claesbackman-review-paper-code#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":476,"forks":83,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":41.85},"quality":{"score":73,"tier":"strong","label":"Strong","summary":"Solid option that is likely worth shortlisting for production workflows.","signals":[{"label":"GitHub stars","value":"476","tone":"neutral"},{"label":"Freshness","value":"9d ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":[]},"trust":{"version":"trust-score-v5","score":70,"base_score":78,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["70/100 Trust Score v5","78/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is available","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":62,"weight":0.13,"status":"info","detail":"476 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":62,"weight":0.08,"status":"info","detail":"476 stars, 83 forks; 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require human review before any live investment decision.","Quality score needs review"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":78,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout."}}},"trust_score_v5":{"version":"trust-score-v5","score":70,"base_score":78,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.","recommendedAction":"Run only in a sandbox and compare close alternatives before using it for real work.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["70/100 Trust Score v5","78/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is available","Review before production"],"review_required_when":["The workspace contains production secrets, payments, private customer data, or irreversible actions.","The install command requests shell, network, credential, database, or broad filesystem access.","Outcome evidence is missing, recently failed, or required human review.","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace"]},"dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":62,"weight":0.13,"status":"info","detail":"476 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":62,"weight":0.08,"status":"info","detail":"476 stars, 83 forks; 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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: Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code. 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\":\"claesbackman-review-paper-code\",\"task\":\"Install review-paper-code\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. 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Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.","category":"research","url":"https://www.openagentskill.com/skills/claesbackman-review-paper-code","repository":"https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code","github_repo":"claesbackman/AI-research-feedback"},"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Read uploaded files","Extract structured fields"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add claesbackman/AI-research-feedback --skill review-paper-code","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add claesbackman-review-paper-code"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"review-paper-code\" agent skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code. 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: Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code. 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\":\"claesbackman-review-paper-code\",\"task\":\"Install review-paper-code\",\"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."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"review-paper-code\" as a Claude Code skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code. 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\":\"claesbackman-review-paper-code\",\"task\":\"Install review-paper-code\",\"agent\":\"claude-code\",\"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."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"review-paper-code\" from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code. 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\":\"claesbackman-review-paper-code\",\"task\":\"Install review-paper-code\",\"agent\":\"cursor\",\"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."}],"handoff_url":"https://www.openagentskill.com/api/skills/claesbackman-review-paper-code/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/claesbackman-review-paper-code"},"trust":{"score":78,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"476 GitHub stars","repoActivity":"476 stars, 83 forks","lastPushed":"9d since push","license":"MIT","repository":"https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code","install":"npx skills add claesbackman/AI-research-feedback --skill review-paper-code","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["research","agent-skill"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":82,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":73,"label":"Strong"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"9d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No OpenAgentSkill engagement data yet","High-risk permission hints: Shell or command execution","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Production credentials, payments, or irreversible account changes without explicit human review"],"agent_contract":{"task_input":"Use review-paper-code in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 78/100 Strong shortlist","Audit: 82/100 Needs review","Safety: 54/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"claesbackman-review-paper-code (review-paper-code)","install_command":"npx skills add claesbackman/AI-research-feedback --skill review-paper-code","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"claesbackman-review-paper-code","task":"Use review-paper-code in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/claesbackman-review-paper-code","api":"https://www.openagentskill.com/api/agent/skills/claesbackman-review-paper-code","audit":"https://www.openagentskill.com/skills/claesbackman-review-paper-code/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=claesbackman-review-paper-code&task=Use%20review-paper-code%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20review-paper-code%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20review-paper-code%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/claesbackman-review-paper-code/install","manifest":"https://www.openagentskill.com/api/registry/manifest/claesbackman-review-paper-code"}},"supply_profile":{"track":{"slug":"research","label":"Research and knowledge work","shortLabel":"Research","description":"Deep research, source comparison, literature review, RAG, knowledge search, and reports."},"scenario":{"label":"Research agents","description":"I need my agent to research a topic, compare sources, and produce a concise report.","useCases":[{"slug":"research-agents","title":"Research agents"},{"slug":"document-processing","title":"Document processing"},{"slug":"github-automation","title":"GitHub automation"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add claesbackman/AI-research-feedback --skill review-paper-code","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":476,"starsLabel":"476","forks":83,"license":"MIT","qualityScore":73,"trustScore":78,"auditScore":82},"maintenance":{"status":"fresh","label":"9d since push","daysSincePush":9,"lastPushedAt":"2026-08-27T21:11:26+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Needs review"]},"coverageTags":["Research","Research agents","agent-skill"]},"audit":{"audit_score":82,"risk_level":"needs_review","risk_label":"Needs review","quality_score":73,"trust_score":78,"maintenance_score":100,"security_score":83,"install_score":92,"warnings":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"quality_signals":{"model":"v2","star_score":18.75,"usage_score":0,"review_score":5.1,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code"],"use_cases":[{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"},{"slug":"document-processing","title":"Document processing","url":"https://www.openagentskill.com/use-cases/document-processing"},{"slug":"github-automation","title":"GitHub automation","url":"https://www.openagentskill.com/use-cases/github-automation"},{"slug":"content-automation","title":"Content automation","url":"https://www.openagentskill.com/use-cases/content-automation"}],"stacks":[{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"rag-knowledge-base","title":"RAG knowledge base","url":"https://www.openagentskill.com/collections/rag-knowledge-base"},{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"}],"install":"npx skills add claesbackman/AI-research-feedback --skill review-paper-code","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add claesbackman-review-paper-code","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"review-paper-code\" agent skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code. 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: Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code. 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\":\"claesbackman-review-paper-code\",\"task\":\"Install review-paper-code\",\"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.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"review-paper-code\" as a Claude Code skill from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code. 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\":\"claesbackman-review-paper-code\",\"task\":\"Install review-paper-code\",\"agent\":\"claude-code\",\"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.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"review-paper-code\" from https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code. 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\":\"claesbackman-review-paper-code\",\"task\":\"Install review-paper-code\",\"agent\":\"cursor\",\"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.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code","github_repo":"claesbackman/AI-research-feedback","version":"1.0.0","license":"MIT","urls":{"web":"https://www.openagentskill.com/skills/claesbackman-review-paper-code","repository":"https://github.com/claesbackman/AI-research-feedback/tree/main/Skills/review-paper-code","api":"/api/agent/skills/claesbackman-review-paper-code","install_api":"/api/skills/claesbackman-review-paper-code/install"},"meta":{"created_at":"2026-09-03T07:11:44.77819+00:00","updated_at":"2026-09-03T07:11:44.895198+00:00","agent_friendly":true}}