{"slug":"wanshuiyin-experiment-plan","name":"experiment-plan","description":"Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.","long_description":"---\nname: experiment-plan\ndescription: 'Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.'\nallowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch\n---\n\n# Experiment Plan: Claim-Driven, Paper-Oriented Validation\n\nRefine and concretize: **$ARGUMENTS**\n\n## Overview\n\nUse this skill after the method is stable enough that the next question becomes: **what exact experiments should we run, in what order, to defend the paper?** If the user wants the full chain in one request, prefer `/research-refine-pipeline`.\n\nThe goal is not to generate a giant benchmark wishlist. The goal is to turn a proposal into a **claim -> evidence -> run order** roadmap that supports four things:\n\n1. the method actually solves the anchored problem\n2. the dominant contribution is real and focused\n3. the method is elegant enough that extra complexity is unnecessary\n4. any frontier-model-era component is genuinely useful, not decorative\n\n## Constants\n\n- **OUTPUT_DIR = `refine-logs/`** — Default destination for experiment planning artifacts.\n- **MAX_PRIMARY_CLAIMS = 2** — Prefer one dominant claim plus one supporting claim.\n- **MAX_CORE_BLOCKS = 5** — Keep the must-run experimental story compact.\n- **MAX_BASELINE_FAMILIES = 3** — Prefer a few strong baselines over many weak ones.\n- **DEFAULT_SEEDS = 3** — Use 3 seeds when stochastic variance matters and budget allows.\n\n## Workflow\n\n### Phase 0: Load the Proposal Context\n\nRead the most relevant existing files first if they exist:\n\n- `refine-logs/FINAL_PROPOSAL.md`\n- `refine-logs/REVIEW_SUMMARY.md`\n- `refine-logs/REFINEMENT_REPORT.md`\n\nExtract:\n\n- **Problem Anchor**\n- **Dominant contribution**\n- **Optional supporting contribution**\n- **Critical reviewer concerns**\n- **Data / compute / timeline constraints**\n- **Which frontier primitive is central, if any**\n\nIf these files do not exist, derive the same information from the user's prompt.\n\n### Phase 1: Freeze the Paper Claims\n\nBefore proposing experiments, write down the claims that must be defended.\n\nUse this structure:\n\n- **Primary claim**: the main mechanism-level contribution\n- **Supporting claim**: optional, only if it directly strengthens the main paper story\n- **Anti-claim to rule out**: e.g. \"the gain only comes from more parameters,\" \"the gain only comes from a larger search space,\" or \"the modern component is just decoration\"\n- **Minimum convincing evidence**: what would make each claim believable to a strong reviewer?\n\nDo not exceed `MAX_PRIMARY_CLAIMS` unless the paper truly has multiple inseparable claims.\n\n### Phase 2: Build the Experimental Storyline\n\nDesign the paper around a compact set of experiment blocks. Default to the following blocks and delete any that are not needed:\n\n1. **Main anchor result** — does the method solve the actual bottleneck?\n2. **Novelty isolation** — does the dominant contribution itself matter?\n3. **Simplicity / elegance check** — can a bigger or more fragmented version be avoided?\n4. **Frontier necessity check** — if an LLM / VLM / Diffusion / RL-era component is central, is it actually the right tool?\n5. **Failure analysis or qualitative diagnosis** — what does the method still miss?\n\nFor each block, decide whether it belongs in:\n\n- **Main paper** — essential to defend the core claims\n- **Appendix** — useful but non-blocking\n- **Cut** — interesting, but not worth the paper budget\n\nPrefer one strong baseline family over many weak baselines. If a stronger modern baseline exists, use it instead of padding the list.\n\n### Phase 3: Specify Each Experiment Block\n\nFor every kept block, fully specify:\n\n- **Claim tested**\n- **Why this block exists**\n- **Dataset / split / task**\n- **Compared systems**: strongest baselines, ablations, and variants only\n- **Metrics**: decisive metrics first, secondary metrics second\n- **Setup details**: backbone, frozen vs trainable parts, key hyperparameters, training budget, seeds\n- **Success criterion**: what outcome would count as convincing evidence?\n- **Failure interpretation**: if the result is negative, what does it mean?\n- **Table / figure target**: where this result should appear in the paper\n\nSpecial rules:\n\n- A **simplicity check** should usually compare the final method against either an overbuilt variant or a tempting extra component that the paper intentionally rejects.\n- A **frontier necessity check** should usually compare the chosen modern primitive against the strongest plausible simpler or older alternative.\n- If the proposal is intentionally non-frontier, say so explicitly and skip the frontier block instead of forcing one.\n\n### Phase 4: Turn the Plan Into an Execution Order\n\nBuild a realistic run order so the user knows what to do first.\n\nUse this milestone structure:\n\n1. **Sanity stage** — data pipeline, metric correctness, one quick overfit or toy split\n2. **Baseline stage** — reproduce the strongest baseline(s)\n3. **Main method stage** — run the final method on the primary setting\n4. **Decision stage** — run the decisive ablations for novelty, simplicity, and frontier necessity\n5. **Polish stage** — robustness, qualitative figures, appendix extras\n\nFor each milestone, estimate:\n\n- compute cost\n- expected turnaround time\n- stop / go decision gate\n- risk and mitigation\n\nSeparate **must-run** from **nice-to-have** experiments.\n\n### Phase 5: Write the Outputs\n\n#### Step 5.1: Write `refine-logs/EXPERIMENT_PLAN.md`\n\nUse this structure:\n\n```markdown\n# Experiment Plan\n\n**Problem**: [problem]\n**Method Thesis**: [one-sentence thesis]\n**Date**: [today]\n\n## Claim Map\n| Claim | Why It Matters | Minimum Convincing Evidence | Linked Blocks |\n|-------|-----------------|-----------------------------|---------------|\n| C1    | ...             | ...                         | B1, B2        |\n\n## Paper Storyline\n- Main paper must prove:\n- Appendix can support:\n- Experiments intentionally cut:\n\n## Experiment Blocks\n\n### Block 1: [Name]\n- Claim tested:\n- Why this block exists:\n- Dataset / split / task:\n- Compared systems:\n- Metrics:\n- Setup details:\n- Success criterion:\n- Failure interpretation:\n- Table / figure target:\n- Priority: MUST-RUN / NICE-TO-HAVE\n\n### Block 2: [Name]\n...\n\n## Run Order and Milestones\n| Milestone | Goal | Runs | Decision Gate | Cost | Risk |\n|-----------|------|------|---------------|------|------|\n| M0        | ...  | ...  | ...           | ...  | ...  |\n\n## Compute and Data Budget\n- Total estimated GPU-hours:\n- Data preparation needs:\n- Human evaluation needs:\n- Biggest bottleneck:\n\n## Risks and Mitigations\n- [Risk]:\n- [Mitigation]:\n\n## Final Checklist\n- [ ] Main paper tables are covered\n- [ ] Novelty is isolated\n- [ ] Simplicity is defended\n- [ ] Frontier contribution is justified or explicitly not claimed\n- [ ] Nice-to-have runs are separated from must-run runs\n```\n\n#### Step 5.2: Write `refine-logs/EXPERIMENT_TRACKER.md`\n\nUse this structure:\n\n```markdown\n# Experiment Tracker\n\n| Run ID | Milestone | Purpose | System / Variant | Split | Metrics | Priority | Status | Notes |\n|--------|-----------|---------|------------------|-------|---------|----------|--------|-------|\n| R001   | M0        | sanity  | ...              | ...   | ...     | MUST     | TODO   | ...   |\n```\n\nKeep the tracker compact and execution-oriented.\n\n#### Step 5.3: Present a Brief Summary to the User\n\n```\nExperiment plan ready.\n\nMust-run blocks:\n- [Block 1]\n- [Block 2]\n\nHighest-risk assumption:\n- [risk]\n\nFirst three runs to launch:\n1. [run]\n2. [run]\n3. [run]\n\nPlan file: refine-logs/EXPERIMENT_PLAN.md\nTracker file: refine-logs/EXPERIMENT_TRACKER.md\n```\n\n## Output Protocols\n\n> Follow these shared protocols for all output files:\n> - **[Output Versioning Protocol](../shared-references/output-versioning.md)** — write timestamped file first, then copy to fixed name\n> - **[Output Manifest Protocol](../shared-references/output-manifest.md)** — log every output to MANIFEST.md\n> - **[Output Language Protocol](../shared-references/output-language.md)** — respect the project's language setting\n\n## Key Rules\n\n- **Large file handling**: If the Write tool fails due to file size, immediately retry using Bash (`cat << 'EOF' > file`) to write in chunks. Do NOT ask the user for permission — just do it silently.\n\n- **Every experiment must defend a claim.** If it does not change a reviewer belief, cut it.\n- **Prefer a compact paper story.** Design the main table first, then add only the ablations that defend it.\n- **Defend simplicity explicitly.** If complexity is a concern, include a deletion study or a stronger-but-bloated variant comparison.\n- **Defend frontier choices explicitly.** If a modern primitive is central, prove why it is better than the strongest simpler alternative.\n- **Prefer strong baselines over long baseline lists.** A short, credible comparison set is better than a padded one.\n- **Separate must-run from nice-to-have.** Do not let appendix ideas delay the core paper evidence.\n- **Reuse proposal constraints.** Do not invent unrealistic budgets or data assumptions.\n- **Do not fabricate results.** Plan evidence; do not claim evidence.\n\n## Composing with Other Skills\n\n```\n/research-refine-pipeline -> one-shot method + experiment planning\n/research-refine   -> method and claim refinement\n/experiment-plan   -> detailed experiment roadmap\n/run-experiment    -> execute the runs\n/auto-review-loop  -> react to results and iterate on the paper\n```\n","tagline":"Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. 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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: Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"wanshuiyin-experiment-plan\",\"task\":\"Install experiment-plan\",\"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 \"experiment-plan\" as a Claude Code skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan. 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: Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"wanshuiyin-experiment-plan\",\"task\":\"Install experiment-plan\",\"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 \"experiment-plan\" from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan 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: Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"wanshuiyin-experiment-plan\",\"task\":\"Install experiment-plan\",\"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/wanshuiyin-experiment-plan/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/wanshuiyin-experiment-plan"},"trust":{"score":84,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"16K GitHub stars","repoActivity":"16K stars, 1.4K forks","lastPushed":"10d since push","license":"MIT","repository":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan","install":"npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-plan","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Usable metadata, review docs","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":["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. 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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: Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"wanshuiyin-experiment-plan\",\"task\":\"Install experiment-plan\",\"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 \"experiment-plan\" as a Claude Code skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan. 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: Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"wanshuiyin-experiment-plan\",\"task\":\"Install experiment-plan\",\"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 \"experiment-plan\" from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan 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: Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"wanshuiyin-experiment-plan\",\"task\":\"Install experiment-plan\",\"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/wanshuiyin-experiment-plan/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/wanshuiyin-experiment-plan"},"trust":{"score":84,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"16K GitHub stars","repoActivity":"16K stars, 1.4K forks","lastPushed":"10d since push","license":"MIT","repository":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan","install":"npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-plan","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, filesystem or document access","documentation":"Usable metadata, review docs","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":["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":88,"risk_level":"safe_to_try","risk_label":"Safe to try","warnings":["Quality score needs review"]},"safety_gate":{"tier":"reviewed","label":"Reviewed with permission notes","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Require human approval before installing into a real workspace."},"quality":{"score":89,"label":"Excellent"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"10d since push","risk":"Safe to try"},"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","Quality score needs 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"],"agent_contract":{"task_input":"Use experiment-plan in an agent workflow","recommended_action":"Require human approval before installing into a real workspace.","install_policy":"review","minimum_review_before_use":["Trust: 84/100 Strong shortlist","Audit: 88/100 Safe to try","Safety: 60/100 Review before install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"wanshuiyin-experiment-plan (experiment-plan)","install_command":"npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-plan","risk_summary":"Safe to try; Reviewed with permission notes; Low metadata risk","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":"wanshuiyin-experiment-plan","task":"Use experiment-plan 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/wanshuiyin-experiment-plan","api":"https://www.openagentskill.com/api/agent/skills/wanshuiyin-experiment-plan","audit":"https://www.openagentskill.com/skills/wanshuiyin-experiment-plan/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=wanshuiyin-experiment-plan&task=Use%20experiment-plan%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20experiment-plan%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20experiment-plan%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/wanshuiyin-experiment-plan/install","manifest":"https://www.openagentskill.com/api/registry/manifest/wanshuiyin-experiment-plan"}},"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":"browser-automation","title":"Browser automation"},{"slug":"workflow-automation","title":"Workflow automation"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-plan","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":15641,"starsLabel":"16K","forks":1362,"license":"MIT","qualityScore":89,"trustScore":84,"auditScore":88},"maintenance":{"status":"fresh","label":"10d since push","daysSincePush":10,"lastPushedAt":"2026-08-26T09:30:40+00:00"},"risk":{"level":"safe_to_try","label":"Safe to try","requiresReview":true,"notes":["Quality score needs review"]},"coverageTags":["Research","Research agents","agent-skill"]},"audit":{"audit_score":88,"risk_level":"safe_to_try","risk_label":"Safe to try","quality_score":89,"trust_score":84,"maintenance_score":100,"security_score":83,"install_score":92,"warnings":["Quality score needs review"]},"quality_signals":{"model":"v2","star_score":29.36,"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":"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"},{"slug":"document-processing","title":"Document processing","url":"https://www.openagentskill.com/use-cases/document-processing"}],"stacks":[{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"},{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"}],"install":"npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill experiment-plan","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 wanshuiyin-experiment-plan","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 \"experiment-plan\" agent skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan. 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: Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"wanshuiyin-experiment-plan\",\"task\":\"Install experiment-plan\",\"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 \"experiment-plan\" as a Claude Code skill from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan. 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: Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"wanshuiyin-experiment-plan\",\"task\":\"Install experiment-plan\",\"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 \"experiment-plan\" from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan 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: Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"wanshuiyin-experiment-plan\",\"task\":\"Install experiment-plan\",\"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/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan","github_repo":"wanshuiyin/Auto-claude-code-research-in-sleep","version":"1.0.0","license":"MIT","urls":{"web":"https://www.openagentskill.com/skills/wanshuiyin-experiment-plan","repository":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/experiment-plan","api":"/api/agent/skills/wanshuiyin-experiment-plan","install_api":"/api/skills/wanshuiyin-experiment-plan/install"},"meta":{"created_at":"2026-09-02T15:42:25.412803+00:00","updated_at":"2026-09-02T15:42:25.469228+00:00","agent_friendly":true}}