{"slug":"gaasher-ml-autoresearch","name":"ml-autoresearch","description":"Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps.","long_description":"---\nname: ml-autoresearch\ndescription: >\n  Use when the user wants an autonomous ML research loop that does more than blindly try changes.\n  After every training run the agent analyses what actually happened inside the model — gradients,\n  activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>`\n  on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on\n  searches papers, grades the evidence, and implements only what prior work supports. One change per\n  run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps.\ncompatibility: Requires Python 3.9+\nmetadata:\n  version: \"0.1.0\"\n---\n\n# ML Autoresearch Loop\n\nThis loop is **analysis-first**: every experiment is followed by a diagnostic pass that examines what\nhappened inside the model, and the next change is a hypothesis grounded in that evidence — not a guess.\nThe feedback signal is `<metric>` read from the run log; the analysis is the spine that decides what to\nchange. A `<literature>` dial (`on`/`off`) optionally grounds each change in prior work via the sibling\n`literature-search` skill. One change per iteration, so each metric move is attributable.\n\nYou are the researcher. Do not pause to ask for permission once the loop is running.\n\n## When to use\nUse for an open-ended, autonomous ML research campaign where you want each change motivated by analysis\nof the model's actual behaviour. Set `<literature> = off` for a self-contained analysis-and-score loop;\nset `<literature> = on` to additionally ground changes in the scientific literature (paper search,\nevidence grading, a reusable findings backlog). Not for a single training run, a fixed sweep, or tasks\nwith no measurable scalar metric. Default to `off` unless the user wants literature grounding or the\nproblem is a known, well-published one where prior recipes will pay off.\n\n## Setup\n**Resolve bindings interactively.** If `loop.run.yaml` exists in the working dir, load it and skip to\nthe loop. Otherwise: on Claude Code (the `AskUserQuestion` tool is available — record `<host>` =\n`claude-code`) infer a likely value for each binding from the project and present it as the recommended\noption; on other hosts (`<host>` = `other`) ask each as a quoted plain-text prompt. Then write\n`loop.run.yaml` (format: `examples/run.example.yaml`) and **confirm every value with the user before\ncreating any other files.** For `branches` strategy, create `git checkout -b autoresearch/<run_tag>`\n(tag from today's date; branch must not exist). For `time` gating, write `<sandbox_root>/run_with_timeout.sh`\n(`timeout $(( <budget> * 60 )) <entrypoint> \"$@\"`) and use it as the run command, hard-killing at\n`2 × <budget>` min; for `epochs`, patch the epoch cap in an `<editable_files>` file.\n\n| binding | meaning | default | how to infer |\n|---|---|---|---|\n| `<metric>` / `<metric_direction>` | scalar to optimize + `minimize`/`maximize` | — | scan editable files + README for metric names |\n| `<run_cmd>` / `<entrypoint>` | command that runs one experiment end to end | — | `pyproject.toml` / `.venv` / README |\n| `<editable_files>` | files fair game to edit (never the eval harness) | — | model / config / train scripts; exclude data, logs, env, harness |\n| `<sandbox_root>` | where snapshots + ledgers live | `./sandbox` | next to the editable files |\n| `<iter_strategy>` | `snapshots` or `branches` | `snapshots` | is the working dir a clean git repo? |\n| `<gate>` / `<budget>` | `time` (min) or `epochs`, and the limit | — | existing time/epoch settings in config |\n| `<literature>` | `on` = literature-grounded; `off` = analysis-only | `off` | does the user want paper grounding? |\n| `<research_scale>` *(on only)* | depth dial `low`/`medium`/`high`/`x-high` | `medium` | see roles/research-subagent.md |\n| `<domain>` *(on only)* | one-phrase problem domain; seeds query phrasing only, never filters | — | infer from data/model/task |\n| `<lit_skill_dir>` *(on only)* | install dir of the `literature-search` skill | sibling of this loop | `~/.claude/skills/literature-search/` (adjust per host) |\n| `<lit_py>` *(on only)* | Python ≥3.9 interpreter for the lit helper (stdlib-only) | `python3` | independent of `<run_cmd>` |\n\n**FILE EDIT GUARD**: before touching any file at any point — setup or loop — confirm it is in\n`<editable_files>`, because everything else is read-only ground truth (the eval harness defines\n`<metric>`). No exceptions.\n\n### Literature toolchain (only when `<literature> = on`)\nPaper search goes through the sibling **`literature-search` skill** (stdlib-only, no installs):\n`<lit> = <lit_skill_dir>/tools/lit_search.py` (note the `tools/` segment). Reuse one cache by\nappending `--cache-dir <sandbox_root>/literature/.cache` after the subcommand. Subcommands print JSON;\non failure they print `{\"error\",\"fallback\"}` and exit non-zero — then **degrade to the host's\nWebSearch/WebFetch** (never fabricate citations). Smoke-test `<lit> --help` at setup; if the skill is\nabsent, tell the user and offer to install it (`cp -r <repo>/loops/literature-search ~/.claude/skills/`)\nor proceed degraded. For onboarding and API keys (all optional; a free `S2_API_KEY` is recommended),\nrun `<lit> keys --init` — it manages the shared gitignored `keys.env` at the project root and reports\npresence as booleans (secrets never enter chat). Persist the live tiers to `loop.run.yaml`.\n\n## Initialise the sandbox\nCreate the layout (extra `literature/` tree only when `<literature> = on`) and write the ledger headers:\n```\n<sandbox_root>/\n├── loop.run.yaml       ← resolved bindings (written now)\n├── results.tsv         ← experiment ledger, header only (written now)\n├── literature/         ← (on only)\n│   ├── corpus.tsv      ← findings ledger, header only (written now)\n│   ├── .cache/         ← lit_search on-disk cache\n│   ├── pdfs/           ← fallback PDF reads\n│   └── text/           ← extracted LaTeX section text\n└── iter1/              ← created at loop start\n```\n`results.tsv` header (tab-separated; the `literature_basis` column exists only when `<literature> = on`):\n```\niter\t<metric>\tstatus\tanalysis_summary\t[literature_basis\t]description\n```\n\n## The loop (LOOP FOREVER — until interrupted)\nIteration 1 is always the **unmodified baseline**: skip change-planning and research (no diagnostics\nyet to ground a change), but still write a baseline `plan.md` and run the **mandatory analysis** — it\nproduces the first empirical anchor that iteration 2 builds on. Everything in `<editable_files>` is fair\ngame (architecture, optimizer, hyperparameters, data pipeline, loss); the only constraints are that the\ncode runs without crashing and finishes within `<budget>`. **Simplicity criterion**: all else equal,\nsimpler is better — a 0.001 gain that adds 20 lines of hacky code is not worth it; a 0.001 gain (or an\nequal metric) from *deleting* code is a `keep`.\n\nCopy this checklist each iteration and tick items off:\n- [ ] **1. Look at the state.** *branches*: `git log --oneline -5`. *snapshots*: confirm `iter<N>/`\n      doesn't exist. Read iter N-1's analysis summary; (on) skim `corpus.tsv` for unimplemented keepers.\n- [ ] **2. Plan one change** (iteration 1: SKIP — run baseline unmodified). Grounded in iter N-1's\n      analysis. See **Planning a change** below; (on) it also runs the literature step.\n- [ ] **3a. Snapshot / commit, then apply the one change.** *snapshots*: create\n      `iter<N>/{code_snapshot,analysis,results}/`, copy every `<editable_files>` into `code_snapshot/`,\n      copy `loop.run.yaml` to `iter<N>/`, then apply. *branches*: apply, then `git commit -am \"<desc>\"`.\n      When implementing a published/library technique, ground it in a real current example or the actual\n      library in the repo (read it first) — never write the API from memory.\n- [ ] **3b. Write the analysis plan BEFORE the run** + add any instrumentation it needs. See **The\n      analysis plan** below.\n- [ ] **4. Run the experiment**, redirecting everything (never `tee`):\n      `<entrypoint> > <sandbox_root>/iter<N>/<run_log> 2>&1` (or `run_with_timeout.sh` when time-gated).\n      If it overruns, kill it and treat as a crash.\n- [ ] **5. Read the metric**: `grep '^<metric>:' <sandbox_root>/iter<N>/<run_log>`. If empty,\n      `tail -n 50 <run_log>`, read the trace, attempt one trivial fix (typo/import); if fundamentally\n      broken, log `crash` and continue.\n- [ ] **6. Analyse the results** — MANDATORY, produces real artifact files. See **Analysing** below.\n- [ ] **7. Log to the ledger(s)** (untracked — never commit). See **Ledger**.\n- [ ] **8. Keep or revert** (the change ran this iteration). Improved per `<metric_direction>` → `keep`,\n      update current-best. Equal/worse/crash → `discard`/`crash`; *branches* `git reset --hard HEAD~1`,\n      *snapshots* restore `<editable_files>` from `iter<N>/code_snapshot/`. Apply the simplicity\n      criterion before logging `discard`. On a crash/OOM, fix with the *minimal* change that preserves\n      the intent (OOM → smaller batch + grad-accum to hold effective batch) — never mutate the\n      experiment into something the plan didn't call for.\n- [ ] **9. Go to step 1** — the analysis from step 6 is the primary input to the next hypothesis.\n\n### Planning a change (step 2 — iterations 2+)\nThe latest analysis sets the direction; it is the master input every iteration. Decide exactly **one**\nlever, grounded in iter N-1's analysis. Other vetted ideas are queued, not bundled into one run.\n\n**State explicitly** before applying:\n- the one change and which `<editable_files>` it touches;\n- the **empirical anchor** — a specific file + value/pattern from iter N-1's `results/` that motivates\n  it. Every non-simplification change must cite an anchor; theoretical reasoning alone is insufficient.\n  A *swing* to a different architecture is anchored too (a ceiling/structural finding, e.g. \"the family\n  plateaus at X with headroom\" or \"it fails exactly on cases needing Y\"), not a local pathology.\n- what you predict will happen and why the finding supports it.\n\n**When `<literature> = off`:** that anchor is the whole basis — pick the change directly from the\nanalysis. Before writing the plan, scan prior analyses (`ls <sandbox_root>/iter*/analysis/*.py`) so you\ndon't repeat a diagnostic without a comparison reason.\n\n**When `<literature> = on`:** after fixing the anchor, *ground the change in the literature* —\n- **2a. Retire drift, then consult the backlog as a cache.** If the last kept change altered the\n  architecture *family* (e.g. CNN→transformer), set `result=stale` for every unimplemented keeper whose\n  `scope` is a non-matching architecture tag; `scope=agnostic` keepers (schedules, weight decay,\n  augmentation, init *philosophies*) survive. Then check `corpus.tsv` for an unimplemented `keep`\n  targeting the analysis's direction — reuse it **only if it still passes the gate (2c) against the\n  CURRENT architecture** (re-validate now; a finding that no longer applies is retired, not forced in).\n- **2b. Research the direction** (the default unless 2a yielded a still-valid lever). Turn the\n  analysis's limitations into questions (tie limitations to questions), record them in\n  `iter<N>/questions.md`, then dispatch **research subagents** — see `roles/research-subagent.md`\n  (spawn-or-degrade: real isolated subagents on Claude Code, otherwise run the research inline in this\n  context) at the dial's depth/effort (see that file's dial table for `<research_scale>`).\n  Level 1 = high-level (architecture fit, prior approaches, does the literature show success); research\n  L1 first — if it surfaces a compelling new direction that becomes the lever. Level 2 = specific\n  micro-opts (init, weight-decay dynamics, attention/cache for the sequence length, norm placement,\n  schedule). *Anti-rut: a keeper passed over for ~3 iterations, or no longer on any live direction, is\n  retired (`result=stale`) so it stops resurfacing.*\n- **2c. Evidence gate** (re-valid","tagline":"Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A","category":"research","tags":["agent-skill"],"author":"gaasher","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github fast track","sourceDetail":"gaasher/Agent-Loop-Skills","creatorName":"gaasher","creatorUrl":"https://github.com/gaasher","sourceUrl":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/gaasher-ml-autoresearch#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":163,"forks":19,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":35.6},"quality":{"score":63,"tier":"promising","label":"Promising","summary":"Useful candidate, but compare it with alternatives before adopting.","signals":[{"label":"GitHub stars","value":"163","tone":"neutral"},{"label":"Freshness","value":"2mo ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":[]},"trust":{"version":"trust-score-v5","score":65,"base_score":73,"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":["65/100 Trust Score v5","73/100 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":"163 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":57,"weight":0.08,"status":"warn","detail":"163 stars, 19 forks; 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issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"2mo since push"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"info","label":"Dependency/runtime risk","detail":"credential or environment access, network or browser surface"},{"status":"pass","label":"Install availability","detail":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"fail","label":"Permission surface","detail":"secrets or environment access, shell or command execution"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch"},{"status":"pass","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"info","label":"OpenAgentSkill usage","detail":"No local usage activity yet"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern","Outcome loop is ready but needs first real agent run"],"warnings":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution","No real agent outcome reports yet","Human review required before unattended installation"],"evidence":{"stars":"163 GitHub stars","repoActivity":"163 stars, 19 forks","lastPushed":"2mo since push","license":"MIT","repository":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch","install":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"human_review_before_install"},"installReadiness":{"ready":true,"command":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","policy":"human_review_before_install","label":"Human review before install","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","2mo since push","Financial domain: human review is required before use in a live investment workflow.","Trust Score v5 requires review or sandbox-only use before install."]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"]},"outcomeEvidence":{"total":0,"successes":0,"failures":0,"notRelevant":0,"successRate":null,"installAttempts":0,"riskBlocked":0,"setupRequired":0,"installSuccessRate":null,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"recentSuccessRate":null,"recentFailureRate":null,"uniqueAgents":0,"agentProvenScore":0,"agentProvenLabel":"Needs first agent run","lastOutcomeAt":null,"label":"No agent outcome data yet"},"autoInstall":{"allowed":false,"sandboxRequired":true,"policy":"human_review_before_install","reason":"Compare alternatives before installing."},"outcome_loop":{"version":"openagentskill-agent-outcome-v4","required_after_install":true,"endpoint":"/api/agent/outcome","method":"POST","event_id_source":"feedback.event_id, install_receipt.resolve_event_id, or decision_packet.outcome_feedback.event_id","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"required_fields":["event_id","skill_slug","task"],"quality_fields":["task_success","output_quality","error_type","human_review_required","used_in_production","workspace","evidence_url","time_to_useful_ms","source_version"],"ranking_inputs_updated":["Trust Score v5 outcome confidence","Agent Proven Score","Resolve ranking task-fit evidence","Skill detail machine-readable metadata","Outcome leaderboard"]},"agent_contract":{"suited_tasks":["research","agent-skill"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"install_command":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","trust_score":65,"trust_version":"trust-score-v5","risk_level":"medium","do_not_use_when":["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","Autonomous investment, trading, tax, or suitability decisions without a qualified human review"],"before_install":["Read the audit page and machine-readable metadata.","Confirm the install command, license, and permission surface fit the workspace.","Get explicit human approval or choose an alternative before installing."],"after_run":["Report the outcome to /api/agent/outcome using the resolve event id.","Include output_quality, workspace, human_review_required, and evidence_url when available.","Re-resolve before broad production rollout."]},"bestFor":["research","agent-skill"],"doNotUseFor":["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","Autonomous investment, trading, tax, or suitability decisions without a qualified human review"],"knownRisks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":73,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout."}}},"trust_score_v4":{"version":"trust-score-v4","score":73,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout.","recommendedAction":"Test in a sandbox workflow and compare its install path with close alternatives.","dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":62,"weight":0.13,"status":"info","detail":"163 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":57,"weight":0.08,"status":"warn","detail":"163 stars, 19 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":88,"weight":0.14,"status":"pass","detail":"2mo since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":86,"weight":0.14,"status":"pass","detail":"Metadata includes enough usage and workflow context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":64,"weight":0.12,"status":"info","detail":"credential or environment access, network or browser surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":22,"weight":0.07,"status":"fail","detail":"secrets or environment access, shell or command execution"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch"},{"id":"review_status","label":"Review status","score":88,"weight":0.05,"status":"pass","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"info","label":"GitHub adoption","detail":"163 GitHub stars"},{"status":"warn","label":"Stars/forks activity","detail":"163 stars, 19 forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"2mo since push"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"info","label":"Dependency/runtime risk","detail":"credential or environment access, network or browser surface"},{"status":"pass","label":"Install availability","detail":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"fail","label":"Permission surface","detail":"secrets or environment access, shell or command execution"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch"},{"status":"pass","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"info","label":"OpenAgentSkill usage","detail":"No local usage activity yet"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern"],"warnings":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"],"evidence":{"stars":"163 GitHub stars","repoActivity":"163 stars, 19 forks","lastPushed":"2mo since push","license":"MIT","repository":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch","install":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"installReadiness":{"ready":true,"command":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","policy":"human_review_before_install","label":"Human review before install","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","2mo since push","Financial domain: human review is required before use in a live investment workflow."]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"]},"outcomeEvidence":{"total":0,"successes":0,"failures":0,"notRelevant":0,"successRate":null,"installAttempts":0,"riskBlocked":0,"setupRequired":0,"installSuccessRate":null,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"recentSuccessRate":null,"recentFailureRate":null,"uniqueAgents":0,"agentProvenScore":0,"agentProvenLabel":"Needs first agent run","lastOutcomeAt":null,"label":"No agent outcome data yet"},"autoInstall":{"allowed":false,"sandboxRequired":true,"policy":"human_review_before_install","reason":"Human review or sandbox validation is required before automatic installation."},"bestFor":["research","agent-skill"],"doNotUseFor":["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","Autonomous investment, trading, tax, or suitability decisions without a qualified human review"],"knownRisks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"]},"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"]},"outcome_stats":null,"safety":{"score":31,"level":"avoid_auto_install","label":"Avoid automatic install","safety_tier":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","summary":"This skill should not be selected by an agent without explicit human security review.","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","auto_install_policy":"block","reasons":["Metadata combines secrets access with shell or command execution","High-risk permission hints: Shell or command execution, Secrets or environment access"]},"auto_install_allowed":false,"human_review_required":true,"blocked":true,"audit_risk":"needs_review","permission_hints":[{"id":"shell","label":"Shell or command execution","reason":"Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.","severity":"high"},{"id":"network","label":"Network access","reason":"Skill likely fetches remote pages, APIs, repositories, or external services.","severity":"medium"},{"id":"filesystem","label":"Filesystem access","reason":"Skill may read or write project files, documents, generated artifacts, or local workspace state.","severity":"medium"},{"id":"secrets","label":"Secrets or environment access","reason":"Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.","severity":"high"},{"id":"database","label":"Database access","reason":"Skill may inspect schemas, query databases, or work with persistent stores.","severity":"medium"}],"policy_warnings":["High-risk permission hints: Shell or command execution, Secrets or environment access","Permission surface may require sandboxing"],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","auto_install_policy":"block","auto_install_allowed":false,"blocked":true,"human_review_required":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","reasons":["Metadata combines secrets access with shell or command execution","High-risk permission hints: Shell or command execution, Secrets or environment access"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"failed","score":65,"risk_level":"high","decision":{"recommendation":"do_not_auto_install","reason":"Agent safety gate: This skill should not be selected by an agent without explicit human security review.","auto_install_allowed":false,"policy":"block","human_review_required":true},"blockers":["Agent safety gate: This skill should not be selected by an agent without explicit human security review.","Permission surface: secrets or environment access, shell or command execution"],"warnings":["Trust score: Good trust signals with a few areas worth checking before rollout.","Audit score: Needs review","High-risk permission hints: Shell or command execution, Secrets or environment access","Permission surface may require sandboxing","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","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"],"validation_plan":["Inspect repository, README/SKILL.md, license, and recent commits before production use.","Install in an isolated workspace or sandbox with no production secrets available.","Run the smallest representative task and record files touched, commands run, network access, and outputs.","Compare the selected skill against at least one alternative when the eval status is review or failed.","Promote only after the agent reports a successful verification result and unresolved warnings are accepted."],"checks":[{"id":"task_fit","label":"Task fit","status":"pass","score":94,"required_for_auto_install":true,"detail":"Task wording matches this skill metadata.","evidence":["Evaluate ml-autoresearch before installing it in an agent workflow","research","Research agents workflows; Claude Code teams; builders willing to evaluate younger projects"]},{"id":"install_path","label":"Install path","status":"pass","score":92,"required_for_auto_install":true,"detail":"Install handoff is available.","evidence":["npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch"]},{"id":"install_safety","label":"Install command safety","status":"pass","score":92,"required_for_auto_install":true,"detail":"standard package or runtime install path","evidence":["npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch"]},{"id":"trust_score","label":"Trust score","status":"warn","score":73,"required_for_auto_install":true,"detail":"Good trust signals with a few areas worth checking before rollout.","evidence":["Strong shortlist","163 GitHub stars","MIT"]},{"id":"audit_score","label":"Audit score","status":"warn","score":75,"required_for_auto_install":true,"detail":"Needs review","evidence":["Permission surface may require sandboxing"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"fail","score":31,"required_for_auto_install":true,"detail":"This skill should not be selected by an agent without explicit human security review.","evidence":["Do not auto-install. Inspect the source, dependencies, and permission surface first.","Metadata combines secrets access with shell or command execution"]},{"id":"readme_skillmd_completeness","label":"README/SKILL.md completeness","status":"pass","score":86,"required_for_auto_install":false,"detail":"Metadata includes enough usage and workflow context","evidence":["Strong README/SKILL.md context"]},{"id":"license_clarity","label":"License clarity","status":"pass","score":86,"required_for_auto_install":true,"detail":"MIT","evidence":["MIT"]},{"id":"recent_maintenance","label":"Recent maintenance","status":"pass","score":88,"required_for_auto_install":false,"detail":"2mo since push","evidence":["2mo since push"]},{"id":"permission_surface","label":"Permission surface","status":"fail","score":22,"required_for_auto_install":true,"detail":"secrets or environment access, shell or command execution","evidence":["Shell or command execution: high","Network access: medium","Filesystem access: medium"]},{"id":"alternatives","label":"Alternatives available","status":"info","score":55,"required_for_auto_install":false,"detail":"No close alternatives were found in the current shortlist.","evidence":[]}],"endpoints":{"web":"https://www.openagentskill.com/skills/gaasher-ml-autoresearch/evals","api":"/api/agent/evals?slug=gaasher-ml-autoresearch","text":"/api/agent/evals?slug=gaasher-ml-autoresearch&format=text"}},"agent_readable_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"gaasher-ml-autoresearch","name":"ml-autoresearch","description":"Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps.","category":"research","url":"https://www.openagentskill.com/skills/gaasher-ml-autoresearch","repository":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch","github_repo":"gaasher/Agent-Loop-Skills"},"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Chunk documents","Create embeddings"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","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 gaasher-ml-autoresearch"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"ml-autoresearch\" agent skill from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch. 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: Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps. 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\":\"gaasher-ml-autoresearch\",\"task\":\"Install ml-autoresearch\",\"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 \"ml-autoresearch\" as a Claude Code skill from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch. 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: Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps. 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\":\"gaasher-ml-autoresearch\",\"task\":\"Install ml-autoresearch\",\"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 \"ml-autoresearch\" from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch 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: Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps. 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\":\"gaasher-ml-autoresearch\",\"task\":\"Install ml-autoresearch\",\"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/gaasher-ml-autoresearch/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/gaasher-ml-autoresearch"},"trust":{"score":73,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"163 GitHub stars","repoActivity":"163 stars, 19 forks","lastPushed":"2mo since push","license":"MIT","repository":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch","install":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","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","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"]},"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":75,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Permission surface may require sandboxing","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","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"]},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","auto_install_policy":"block","auto_install_allowed":false,"human_review_required":true,"blocked":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first."},"quality":{"score":63,"label":"Promising"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"2mo 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, Secrets or environment access","Permission surface may require sandboxing","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"],"agent_contract":{"task_input":"Use ml-autoresearch in an agent workflow","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","install_policy":"block","minimum_review_before_use":["Trust: 73/100 Strong shortlist","Audit: 75/100 Needs review","Safety: 31/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"gaasher-ml-autoresearch (ml-autoresearch)","install_command":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","risk_summary":"Needs review; Blocked for auto-install; 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":"gaasher-ml-autoresearch","task":"Use ml-autoresearch 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/gaasher-ml-autoresearch","api":"https://www.openagentskill.com/api/agent/skills/gaasher-ml-autoresearch","audit":"https://www.openagentskill.com/skills/gaasher-ml-autoresearch/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=gaasher-ml-autoresearch&task=Use%20ml-autoresearch%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20ml-autoresearch%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20ml-autoresearch%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/gaasher-ml-autoresearch/install","manifest":"https://www.openagentskill.com/api/registry/manifest/gaasher-ml-autoresearch"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"gaasher-ml-autoresearch","name":"ml-autoresearch","description":"Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps.","category":"research","url":"https://www.openagentskill.com/skills/gaasher-ml-autoresearch","repository":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch","github_repo":"gaasher/Agent-Loop-Skills"},"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Chunk documents","Create embeddings"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","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 gaasher-ml-autoresearch"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"ml-autoresearch\" agent skill from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch. 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: Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps. 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\":\"gaasher-ml-autoresearch\",\"task\":\"Install ml-autoresearch\",\"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 \"ml-autoresearch\" as a Claude Code skill from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch. 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: Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps. 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\":\"gaasher-ml-autoresearch\",\"task\":\"Install ml-autoresearch\",\"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 \"ml-autoresearch\" from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch 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: Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps. 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\":\"gaasher-ml-autoresearch\",\"task\":\"Install ml-autoresearch\",\"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/gaasher-ml-autoresearch/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/gaasher-ml-autoresearch"},"trust":{"score":73,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"163 GitHub stars","repoActivity":"163 stars, 19 forks","lastPushed":"2mo since push","license":"MIT","repository":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch","install":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","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","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"]},"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":75,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Permission surface may require sandboxing","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","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"]},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","auto_install_policy":"block","auto_install_allowed":false,"human_review_required":true,"blocked":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first."},"quality":{"score":63,"label":"Promising"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"2mo 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, Secrets or environment access","Permission surface may require sandboxing","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"],"agent_contract":{"task_input":"Use ml-autoresearch in an agent workflow","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","install_policy":"block","minimum_review_before_use":["Trust: 73/100 Strong shortlist","Audit: 75/100 Needs review","Safety: 31/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"gaasher-ml-autoresearch (ml-autoresearch)","install_command":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","risk_summary":"Needs review; Blocked for auto-install; 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":"gaasher-ml-autoresearch","task":"Use ml-autoresearch 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/gaasher-ml-autoresearch","api":"https://www.openagentskill.com/api/agent/skills/gaasher-ml-autoresearch","audit":"https://www.openagentskill.com/skills/gaasher-ml-autoresearch/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=gaasher-ml-autoresearch&task=Use%20ml-autoresearch%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20ml-autoresearch%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20ml-autoresearch%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/gaasher-ml-autoresearch/install","manifest":"https://www.openagentskill.com/api/registry/manifest/gaasher-ml-autoresearch"}},"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":"rag-knowledge","title":"RAG and knowledge"},{"slug":"browser-automation","title":"Browser automation"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":163,"starsLabel":"163","forks":19,"license":"MIT","qualityScore":63,"trustScore":73,"auditScore":75},"maintenance":{"status":"active","label":"2mo since push","daysSincePush":70,"lastPushedAt":"2026-06-30T04:03:49+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Permission surface may require sandboxing","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","Permission surface needs review: secrets or environment access, shell or command execution"]},"coverageTags":["Research","Research agents","agent-skill"]},"audit":{"audit_score":75,"risk_level":"needs_review","risk_label":"Needs review","quality_score":63,"trust_score":73,"maintenance_score":88,"security_score":78,"install_score":92,"warnings":["Permission surface may require sandboxing","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","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata","Permission surface: secrets or environment access, shell or command execution"]},"quality_signals":{"model":"v2","star_score":15.5,"usage_score":0,"review_score":5.1,"metadata_score":3,"freshness_score":12},"platforms":["Claude Code"],"use_cases":[{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"},{"slug":"rag-knowledge","title":"RAG and knowledge","url":"https://www.openagentskill.com/use-cases/rag-knowledge"},{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"},{"slug":"workflow-automation","title":"Workflow automation","url":"https://www.openagentskill.com/use-cases/workflow-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":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"}],"install":"npx skills add gaasher/Agent-Loop-Skills --skill ml-autoresearch","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 gaasher-ml-autoresearch","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 \"ml-autoresearch\" agent skill from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch. 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: Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps. 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\":\"gaasher-ml-autoresearch\",\"task\":\"Install ml-autoresearch\",\"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 \"ml-autoresearch\" as a Claude Code skill from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch. 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: Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps. 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\":\"gaasher-ml-autoresearch\",\"task\":\"Install ml-autoresearch\",\"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 \"ml-autoresearch\" from https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch 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: Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A `<literature>` on/off dial adds scientific-literature grounding: off behaves as a pure analysis-first loop; on searches papers, grades the evidence, and implements only what prior work supports. One change per run; loops forever until interrupted. Not for one-off training runs or hyperparameter sweeps. 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\":\"gaasher-ml-autoresearch\",\"task\":\"Install ml-autoresearch\",\"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/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch","github_repo":"gaasher/Agent-Loop-Skills","version":"1.0.0","license":"MIT","urls":{"web":"https://www.openagentskill.com/skills/gaasher-ml-autoresearch","repository":"https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/ml-autoresearch","api":"/api/agent/skills/gaasher-ml-autoresearch","install_api":"/api/skills/gaasher-ml-autoresearch/install"},"meta":{"created_at":"2026-09-04T05:11:02.690012+00:00","updated_at":"2026-09-04T05:11:02.774371+00:00","agent_friendly":true}}