{"slug":"aperivue-design-ai-benchmarking","name":"design-ai-benchmarking","description":"Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation.","long_description":"---\nname: design-ai-benchmarking\ndescription: >\n  Design and validity review for studies that benchmark one or more AI systems against a human-expert\n  panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional\n  rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability\n  targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a\n  structured rating-export schema. Use before data collection on an AI-vs-expert evaluation.\ntriggers: AI benchmarking, AI vs human expert, reader study design, expert panel evaluation, LLM-as-judge, AI evaluation rubric, model benchmark design, human baseline comparison, AI-output rating, evaluation rubric design\ntools: Read, Write, Edit, Bash, Grep, Glob\nmodel: inherit\n---\n\n# Design-AI-Benchmarking Skill\n\n## Purpose\n\nThis skill pressure-tests an AI-vs-human-expert benchmark **before any ratings are collected**, so that\nthe comparison is fair, the rubric measures distinct constructs, the scale is calibrated, and the\nreported reliability is interpretable. It is the AI-evaluation specialization of `/design-study`: where\n`/design-study` reviews a study in general, this skill owns the specific machinery of comparing AI\nsystem(s) to a panel of human experts (or to each other) on rated outputs.\n\nUse it when:\n- one or more AI systems will be scored against a human-expert reference (reader study, annotation\n  panel, AI-output evaluation, model-vs-model bench)\n- a rubric and rating protocol must be locked before reviewers begin\n- a benchmark feels vulnerable to \"the highest score is just the most tautological item\" or\n  \"low agreement, but we cannot tell why\" criticism\n- a reviewer or editor asks how the evaluation controlled for rater drift, leakage, or judge bias\n\nDo **not** use it for: general study/validity review (use `/design-study`); statistical execution such\nas ICC or DeLong (use `/analyze-stats`); reporting-guideline item audits (use `/check-reporting`);\nor reviewing an already-written manuscript (use `/peer-review` or `/self-review`).\n\n---\n\n## Communication Rules\n\n- Communicate with the user in their preferred language.\n- Use English for statistical, machine-learning, and reporting-guideline terminology.\n- Be direct about evaluation-validity risks, but always propose the smallest feasible fix first.\n- Never invent reviewer ratings, reference labels, or agreement statistics; those come from collected\n  data only.\n\n---\n\n## Standard Output\n\n```text\n## AI-Benchmark Design Review\nEvaluation question: ...\nArms / systems compared: ...\nReference (human-expert panel): ...\nUnit of rating: (item / case / output)\n\n### Rubric (decoupled dimensions)\n- dimension -> construct -> anchors (1..k)\n\n### Calibration probes (blinded, randomized)\n- positive-control / known-bad / instability / mechanism-contradiction\n\n### Reviewer panel\n- n reviewers, metadata captured, per-reviewer randomized order\n\n### Reliability plan\n- overall IRR target + control-item IRR (reported separately)\n\n### Judge strategy\n- human-as-judge / LLM-as-judge / both + adjudication rule\n\n### Validity risks\n1. ...\n\n### Minimal fixes\n- ...\n\n### Decision\n- Ready to collect / Needs rubric revision / Needs arm or judge redesign\n```\n\n---\n\n## Workflow\n\n### Phase 1: Define the evaluation question and arms\n\nPin down, in writing:\n- the exact claim the benchmark must support (e.g., \"system A's outputs are perceptually\n  indistinguishable from expert outputs\", not \"system A is deployment-ready\")\n- every arm/system being compared, and what each arm receives as input (same items, same information\n  access, same output format) so no arm has a hidden advantage\n- the human-expert reference: who they are, and whether they set ground truth, provide a comparison\n  arm, or both\n- the unit of rating (item, case, output) and how many units each reviewer sees\n\n**Gate:** Present the reconstructed evaluation question, arms, and reference to the user and confirm\nbefore designing the rubric. A wrong reconstruction misdirects the entire benchmark.\n\n### Phase 2: Design a decoupled multi-dimensional rubric\n\n- **Decouple the axes.** Each rated dimension measures one construct. Keep \"is the output valid/correct\"\n  separate from \"is it novel\", \"is it feasible/measurable\", \"does it add value over current tools\", and\n  \"would it change action\". A candidate can be high-validity yet low-added-value (\"real but redundant\");\n  a single blended score hides this divergence.\n- **Anchor every scale point** with a short verbal descriptor; pilot the anchors with at least one\n  reviewer before locking.\n- **Pre-specify discriminant validity**: hypothesize which dimensions should correlate vs be orthogonal,\n  then report the full inter-dimension correlation matrix to confirm the rubric measures distinct\n  constructs.\n- A worked rubric template lives in `${CLAUDE_SKILL_DIR}/references/elicitation_rubric_template.md`.\n\n### Phase 3: Insert and randomize calibration probes\n\nPlant a small number of deliberate control items, blinded and randomized across raters (record who\nreceived which via a `probe_arm` flag), to (i) anchor the scale, (ii) measure rater drift/fatigue, and\n(iii) audit the rubric and pipeline itself. Four useful flavors:\n- **Positive control / \"too-good\" item** — a known-strong or near-tautological item; tests whether\n  raters equate \"largest effect\" with \"best\", and whether the construct-independence gate (Phase 7) works.\n- **Known-bad negative control** — an engineered defect (fabricated reference, missing key statistic);\n  expected to score low.\n- **Instability item** — an estimate that reverses or fails to replicate on a holdout; tests\n  caveat-handling.\n- **Mechanism-contradiction item** — an empirical direction that opposes the proposed mechanism.\n\nProbes are *planted or adjudicated*, never fabricated to fit a hypothesis.\n\n### Phase 4: Construct the reviewer panel\n\n- Recruit reviewers spanning the intended expertise gradient; pre-specify any expertise stratification.\n- Capture reviewer metadata (years of experience, prior AI-evaluation experience, subspecialty) for\n  descriptive reporting and stratified analysis.\n- Randomize item order **per reviewer** (not one global seed) and record the order; plan to analyze\n  order and fatigue effects.\n- Require each item to be judged standalone; discourage cross-item references in free-text, which signal\n  non-independent rating.\n\n**Gate:** Present the panel composition, stratification, and randomization plan for user review before\nrecruitment is finalized.\n\n### Phase 5: Set inter-rater reliability targets\n\n- Pre-specify the agreement statistic (e.g., ICC for continuous ratings, weighted kappa for ordinal)\n  and a target with justification.\n- **Report reliability on the planted control items separately** as primary evidence of rubric and\n  scale validity. A low overall ICC is interpretable only if raters at least converge on the controls;\n  surfacing both numbers prevents \"low agreement => bad rubric\" or \"bad raters\" misreads.\n- Plan the minimum ratings-per-item needed for a stable agreement estimate (delegate the math to\n  `/analyze-stats`).\n\n### Phase 5b: Reader allocation under burden constraints (anchor-and-rotate)\n\nWhen the item pool is larger than one reader can rate in a session, do **not** force every reader\nto rate every item (that caps the total pool at the per-reader limit and discards coverage). Use an\n**anchor-and-rotate** (balanced-incomplete-block) layout: all readers rate a shared **anchor set**\n(which carries the inter-rater ICC/kappa, alongside the planted controls), and each reader\nadditionally rates a **rotating unique block**, so total coverage grows independently of the\nper-reader cap. The usually-binding constraint is the **number of available expert readers**, not the\nitem count — solve the reverse problem (`max_pool = anchor + R*(cap-anchor)//m`) to size the must-rate\nset to a realistic panel. Pre-specify anchor membership, raters-per-item, and the rotation seed before\nrating. Formulas, trade-offs, and a stdlib reference implementation are in\n`${CLAUDE_SKILL_DIR}/references/anchor_rotate_reader_allocation.md`.\n\n### Phase 6: Choose the judge strategy and adjudication\n\n- Decide human-as-judge, LLM-as-judge, or both. If an LLM is used as a judge, treat it as one more arm\n  whose ratings must themselves be validated against the human panel on the control items.\n- Pre-specify the **adjudication rule** for disagreement (e.g., majority, a third senior reviewer,\n  consensus discussion) and who adjudicates.\n- Blind judges to arm identity wherever feasible; record any unavoidable unblinding.\n\n### Phase 7: Construct-independence and leakage guards\n\n- Exclude any predictor or input that is a definitional component of the outcome (mathematical\n  definition), and flag near-tautological composites built from the outcome's defining components — they\n  produce an inflated, near-circular result and belong as labeled probes, not discoveries.\n- Verify no arm sees post-decision or outcome-derived information the others do not.\n- Confirm the reference labels were not derived from the same model output being evaluated.\n\n### Phase 8: Lock a structured export schema\n\nDefine the machine-readable rating record up front: per-item ratings across every rubric dimension,\nfree-text justifications, follow-up flags, the `probe_arm` flag, reviewer id and metadata, item order,\nand timing. A synthetic schema lives in `${CLAUDE_SKILL_DIR}/references/benchmark_export_schema.json`.\n\n**Gate:** Present the final rubric, probe set, panel plan, judge strategy, and export schema together;\ncollect explicit user approval before any rating begins. Locking these before data collection is the\nwhole point — changes afterward compromise the comparison.\n\n---\n\n## Handoff Rules\n\n- route to `/analyze-stats` for ICC / weighted kappa / DeLong, agreement sample size, and effect-size\n  real-world translation of the benchmark results\n- route to `/check-reporting` for STARD-AI, CLAIM, or TRIPOD+AI item-level reporting once the design is locked\n- route to `/design-study` when the broader study around the benchmark (cohort logic, analysis unit,\n  comparator) also needs review\n- route to `/peer-review` or `/self-review` only after ratings exist and a manuscript is being assessed\n\n---\n\n## What This Skill Does NOT Do\n\n- It does not compute agreement statistics or run analyses directly (that is `/analyze-stats`).\n- It does not collect or fabricate ratings, reference labels, or probe outcomes.\n- It does not draft manuscript prose or run a reporting-guideline audit.\n- It does not replace a full peer review of a finished manuscript.\n\n## Anti-Hallucination\n\n- **Never fabricate references.** All citations must be verified via `/search-lit` with a confirmed DOI\n  or PMID. Mark unverified references as `[UNVERIFIED - NEEDS MANUAL CHECK]`.\n- **Never invent reviewer ratings, agreement statistics, reference labels, or probe outcomes** — these\n  come from collected data only. A reported ICC, kappa, or score with no underlying rating record is the\n  failure mode this skill exists to prevent.\n- **Never invent clinical definitions, diagnostic criteria, or guideline recommendations.** If uncertain,\n  flag with `[VERIFY]` and ask the user.\n- If a reporting-guideline item, journal policy, or evaluation standard is uncertain, state the\n  uncertainty rather than guessing.\n\n## Reference Files\n\n- `${CLAUDE_SKILL_DIR}/references/elicitation_rubric_template.md` -- a synthetic, decoupled\n  multi-dimension rating rubric with anchors and a planted-probe column.\n- `${CLAUDE_SKILL_DIR}/references/benchmark_export_schema.json` -- a synthetic JSON schema for the\n  per-item rating export (ratings, justifications, probe_arm, reviewer metadata, order, timing).\n- `${CLAUDE_SKILL_DIR}/references/anchor_rotate_reader_allocation.md` -- anchor-and-rotate\n  (balanced-incomplete-block) reader allocation: formulas, the reverse \"max pool for R read","tagline":"Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction","category":"design-creative","tags":["agent-skill"],"author":"Aperivue","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github candidate review","sourceDetail":"Aperivue/medsci-skills","creatorName":"Aperivue","creatorUrl":"https://github.com/Aperivue","sourceUrl":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/aperivue-design-ai-benchmarking#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":283,"forks":69,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":40.57},"quality":{"score":71,"tier":"strong","label":"Strong","summary":"Solid option that is likely worth shortlisting for production workflows.","signals":[{"label":"GitHub stars","value":"283","tone":"neutral"},{"label":"Freshness","value":"Today","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":["No critical security issues detected; the skill is advisory and uses standard file tools only."]},"trust":{"version":"trust-score-v5","score":62,"base_score":70,"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":["62/100 Trust Score v5","70/100 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":"283 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":62,"weight":0.08,"status":"info","detail":"283 stars, 69 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"Pushed today"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":76,"weight":0.14,"status":"info","detail":"Public metadata needs stronger README/SKILL.md context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":72,"weight":0.12,"status":"info","detail":"command execution surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking"},{"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":64,"weight":0.07,"status":"info","detail":"shell or command execution, database access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking"},{"id":"review_status","label":"Review status","score":66,"weight":0.05,"status":"info","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":"283 GitHub stars"},{"status":"info","label":"Stars/forks activity","detail":"283 stars, 69 forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"Pushed today"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"info","label":"README/SKILL.md completeness","detail":"Public metadata needs stronger README/SKILL.md context"},{"status":"info","label":"Dependency/runtime risk","detail":"command execution surface"},{"status":"pass","label":"Install availability","detail":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"info","label":"Permission surface","detail":"shell or command execution, database access"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking"},{"status":"info","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":"pass","label":"OpenAgentSkill usage","detail":"1 views, 0 install copies"},{"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":["No critical security issues detected; the skill is advisory and uses standard file tools only.","Quality score needs review","No real agent outcome reports yet","Human review required before unattended installation"],"evidence":{"stars":"283 GitHub stars","repoActivity":"283 stars, 69 forks","lastPushed":"Pushed today","license":"MIT","repository":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","install":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, database access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"human_review_before_install"},"installReadiness":{"ready":true,"command":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","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","Pushed today","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":["No critical security issues detected; 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the skill is advisory and uses standard file tools only.","Quality score needs review"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":70,"tier":"review","label":"Manual review","summary":"Potentially useful, but at least one trust signal needs human inspection."}}},"trust_score_v5":{"version":"trust-score-v5","score":62,"base_score":70,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. 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the skill is advisory and uses standard file tools only.","Quality score needs review","No real agent outcome reports yet","Human review required before unattended installation"],"evidence":{"stars":"283 GitHub stars","repoActivity":"283 stars, 69 forks","lastPushed":"Pushed today","license":"MIT","repository":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","install":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, database access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"human_review_before_install"},"installReadiness":{"ready":true,"command":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","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","Pushed today","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":["No critical security issues detected; the skill is advisory and uses standard file tools only.","Quality score needs review"]},"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":["design-creative","agent-skill"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"install_command":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","trust_score":62,"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"],"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":["design-creative","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"],"knownRisks":["No critical security issues detected; the skill is advisory and uses standard file tools only.","Quality score needs review"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":70,"tier":"review","label":"Manual review","summary":"Potentially useful, but at least one trust signal needs human inspection."}}},"trust_score_v4":{"version":"trust-score-v4","score":70,"tier":"review","label":"Manual review","summary":"Potentially useful, but at least one trust signal needs human inspection.","recommendedAction":"Inspect the repository, license, and recent activity before connecting it to agent workflows.","dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":62,"weight":0.13,"status":"info","detail":"283 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":62,"weight":0.08,"status":"info","detail":"283 stars, 69 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"Pushed today"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":76,"weight":0.14,"status":"info","detail":"Public metadata needs stronger README/SKILL.md context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":72,"weight":0.12,"status":"info","detail":"command execution surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking"},{"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":64,"weight":0.07,"status":"info","detail":"shell or command execution, database access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking"},{"id":"review_status","label":"Review status","score":66,"weight":0.05,"status":"info","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":"283 GitHub stars"},{"status":"info","label":"Stars/forks activity","detail":"283 stars, 69 forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"Pushed today"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"info","label":"README/SKILL.md completeness","detail":"Public metadata needs stronger README/SKILL.md context"},{"status":"info","label":"Dependency/runtime risk","detail":"command execution surface"},{"status":"pass","label":"Install availability","detail":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"info","label":"Permission surface","detail":"shell or command execution, database access"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking"},{"status":"info","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":"pass","label":"OpenAgentSkill usage","detail":"1 views, 0 install copies"},{"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":["No critical security issues detected; the skill is advisory and uses standard file tools only.","Quality score needs review"],"evidence":{"stars":"283 GitHub stars","repoActivity":"283 stars, 69 forks","lastPushed":"Pushed today","license":"MIT","repository":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","install":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, database access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet"},"installReadiness":{"ready":true,"command":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","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","Pushed today"]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["No critical security issues detected; the skill is advisory and uses standard file tools only.","Quality score needs review"]},"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":["design-creative","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"],"knownRisks":["No critical security issues detected; the skill is advisory and uses standard file tools only.","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"]},"outcome_stats":null,"safety":{"score":46,"level":"avoid_auto_install","label":"Avoid automatic install","safety_tier":{"tier":"experimental","label":"Experimental","badge":"EXPERIMENTAL","summary":"Sparse or mixed signals. Useful for discovery, but not for autonomous installation.","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","auto_install_policy":"review","reasons":["High-risk permission hints: Shell or command execution","46/100 agent safety score"]},"auto_install_allowed":false,"human_review_required":true,"blocked":false,"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":"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","No critical security issues detected; the skill is advisory and uses standard file tools only."],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"experimental","label":"Experimental","badge":"EXPERIMENTAL","auto_install_policy":"review","auto_install_allowed":false,"blocked":false,"human_review_required":true,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","reasons":["High-risk permission hints: Shell or command execution","46/100 agent safety score"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"review","score":70,"risk_level":"medium","decision":{"recommendation":"manual_review","reason":"Test manually in an isolated workspace and compare against safer alternatives.","auto_install_allowed":false,"policy":"review","human_review_required":true},"blockers":[],"warnings":["Trust score: Potentially useful, but at least one trust signal needs human inspection.","Audit score: Needs review","Agent safety gate: Sparse or mixed signals. Useful for discovery, but not for autonomous installation.","README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context","Permission surface: shell or command execution, database access","High-risk permission hints: Shell or command execution","No critical security issues detected; the skill is advisory and uses standard file tools only.","The SKILL.md excerpt is truncated in the review material, but the provided content is coherent and complete enough for assessment.","Quality score needs review"],"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 design-ai-benchmarking before installing it in an agent workflow","design-creative","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 Aperivue/medsci-skills --skill design-ai-benchmarking"]},{"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 Aperivue/medsci-skills --skill design-ai-benchmarking"]},{"id":"trust_score","label":"Trust score","status":"warn","score":70,"required_for_auto_install":true,"detail":"Potentially useful, but at least one trust signal needs human inspection.","evidence":["Manual review","283 GitHub stars","MIT"]},{"id":"audit_score","label":"Audit score","status":"warn","score":78,"required_for_auto_install":true,"detail":"Needs review","evidence":["No critical security issues detected; the skill is advisory and uses standard file tools only."]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"warn","score":46,"required_for_auto_install":true,"detail":"Sparse or mixed signals. Useful for discovery, but not for autonomous installation.","evidence":["Test manually in an isolated workspace and compare against safer alternatives.","High-risk permission hints: Shell or command execution"]},{"id":"readme_skillmd_completeness","label":"README/SKILL.md completeness","status":"warn","score":76,"required_for_auto_install":false,"detail":"Public metadata needs stronger README/SKILL.md context","evidence":["Usable metadata, review docs"]},{"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":100,"required_for_auto_install":false,"detail":"Pushed today","evidence":["Pushed today"]},{"id":"permission_surface","label":"Permission surface","status":"warn","score":64,"required_for_auto_install":true,"detail":"shell or command execution, database access","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/aperivue-design-ai-benchmarking/evals","api":"/api/agent/evals?slug=aperivue-design-ai-benchmarking","text":"/api/agent/evals?slug=aperivue-design-ai-benchmarking&format=text"}},"agent_readable_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"aperivue-design-ai-benchmarking","name":"design-ai-benchmarking","description":"Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation.","category":"design-creative","url":"https://www.openagentskill.com/skills/aperivue-design-ai-benchmarking","repository":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","github_repo":"Aperivue/medsci-skills"},"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Navigate pages","Click and type safely"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","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 aperivue-design-ai-benchmarking"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"design-ai-benchmarking\" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking. 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: Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation. 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\":\"aperivue-design-ai-benchmarking\",\"task\":\"Install design-ai-benchmarking\",\"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 \"design-ai-benchmarking\" as a Claude Code skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking. 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: Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation. 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\":\"aperivue-design-ai-benchmarking\",\"task\":\"Install design-ai-benchmarking\",\"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 \"design-ai-benchmarking\" from https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking 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: Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation. 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\":\"aperivue-design-ai-benchmarking\",\"task\":\"Install design-ai-benchmarking\",\"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/aperivue-design-ai-benchmarking/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/aperivue-design-ai-benchmarking"},"trust":{"score":70,"label":"Manual review","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"283 GitHub stars","repoActivity":"283 stars, 69 forks","lastPushed":"Pushed today","license":"MIT","repository":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","install":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, database 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":["design-creative","agent-skill"],"known_risks":["No critical security issues detected; the skill is advisory and uses standard file tools only.","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":78,"risk_level":"needs_review","risk_label":"Needs review","warnings":["No critical security issues detected; the skill is advisory and uses standard file tools only.","The SKILL.md excerpt is truncated in the review material, but the provided content is coherent and complete enough for assessment.","Quality score needs review"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":71,"label":"Strong"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"Pushed today","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","No critical security issues detected; the skill is advisory and uses standard file tools only.","High-risk permission hints: Shell or command execution","The SKILL.md excerpt is truncated in the review material, but the provided content is coherent and complete enough for assessment.","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"],"agent_contract":{"task_input":"Use design-ai-benchmarking in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 70/100 Manual review","Audit: 78/100 Needs review","Safety: 46/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"aperivue-design-ai-benchmarking (design-ai-benchmarking)","install_command":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"aperivue-design-ai-benchmarking","task":"Use design-ai-benchmarking 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/aperivue-design-ai-benchmarking","api":"https://www.openagentskill.com/api/agent/skills/aperivue-design-ai-benchmarking","audit":"https://www.openagentskill.com/skills/aperivue-design-ai-benchmarking/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=aperivue-design-ai-benchmarking&task=Use%20design-ai-benchmarking%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20design-ai-benchmarking%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20design-ai-benchmarking%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/aperivue-design-ai-benchmarking/install","manifest":"https://www.openagentskill.com/api/registry/manifest/aperivue-design-ai-benchmarking"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"aperivue-design-ai-benchmarking","name":"design-ai-benchmarking","description":"Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation.","category":"design-creative","url":"https://www.openagentskill.com/skills/aperivue-design-ai-benchmarking","repository":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","github_repo":"Aperivue/medsci-skills"},"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Navigate pages","Click and type safely"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","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 aperivue-design-ai-benchmarking"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"design-ai-benchmarking\" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking. 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: Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation. 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\":\"aperivue-design-ai-benchmarking\",\"task\":\"Install design-ai-benchmarking\",\"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 \"design-ai-benchmarking\" as a Claude Code skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking. 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: Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation. 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\":\"aperivue-design-ai-benchmarking\",\"task\":\"Install design-ai-benchmarking\",\"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 \"design-ai-benchmarking\" from https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking 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: Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation. 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\":\"aperivue-design-ai-benchmarking\",\"task\":\"Install design-ai-benchmarking\",\"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/aperivue-design-ai-benchmarking/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/aperivue-design-ai-benchmarking"},"trust":{"score":70,"label":"Manual review","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"283 GitHub stars","repoActivity":"283 stars, 69 forks","lastPushed":"Pushed today","license":"MIT","repository":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","install":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","installSafety":"standard package or runtime install path","permissionSurface":"shell or command execution, database 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":["design-creative","agent-skill"],"known_risks":["No critical security issues detected; the skill is advisory and uses standard file tools only.","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":78,"risk_level":"needs_review","risk_label":"Needs review","warnings":["No critical security issues detected; the skill is advisory and uses standard file tools only.","The SKILL.md excerpt is truncated in the review material, but the provided content is coherent and complete enough for assessment.","Quality score needs review"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":71,"label":"Strong"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"Pushed today","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","No critical security issues detected; the skill is advisory and uses standard file tools only.","High-risk permission hints: Shell or command execution","The SKILL.md excerpt is truncated in the review material, but the provided content is coherent and complete enough for assessment.","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"],"agent_contract":{"task_input":"Use design-ai-benchmarking in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 70/100 Manual review","Audit: 78/100 Needs review","Safety: 46/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"aperivue-design-ai-benchmarking (design-ai-benchmarking)","install_command":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"aperivue-design-ai-benchmarking","task":"Use design-ai-benchmarking 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/aperivue-design-ai-benchmarking","api":"https://www.openagentskill.com/api/agent/skills/aperivue-design-ai-benchmarking","audit":"https://www.openagentskill.com/skills/aperivue-design-ai-benchmarking/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=aperivue-design-ai-benchmarking&task=Use%20design-ai-benchmarking%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20design-ai-benchmarking%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20design-ai-benchmarking%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/aperivue-design-ai-benchmarking/install","manifest":"https://www.openagentskill.com/api/registry/manifest/aperivue-design-ai-benchmarking"}},"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":"local-desktop","title":"Local desktop"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":283,"starsLabel":"283","forks":69,"license":"MIT","qualityScore":71,"trustScore":70,"auditScore":78},"maintenance":{"status":"fresh","label":"Pushed today","daysSincePush":0,"lastPushedAt":"2026-09-06T01:48:58+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["No critical security issues detected; the skill is advisory and uses standard file tools only.","The SKILL.md excerpt is truncated in the review material, but the provided content is coherent and complete enough for assessment.","Quality score needs review","Needs review"]},"coverageTags":["Research","Research agents","design-creative","agent-skill"]},"audit":{"audit_score":78,"risk_level":"needs_review","risk_label":"Needs review","quality_score":71,"trust_score":70,"maintenance_score":100,"security_score":77,"install_score":92,"warnings":["No critical security issues detected; the skill is advisory and uses standard file tools only.","The SKILL.md excerpt is truncated in the review material, but the provided content is coherent and complete enough for assessment.","Quality score needs review"]},"quality_signals":{"model":"v2","star_score":17.17,"usage_score":0,"review_score":5.4,"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":"local-desktop","title":"Local desktop","url":"https://www.openagentskill.com/use-cases/local-desktop"},{"slug":"rag-knowledge","title":"RAG and knowledge","url":"https://www.openagentskill.com/use-cases/rag-knowledge"}],"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":"frontend-product-ui","title":"Frontend and UI","url":"https://www.openagentskill.com/collections/frontend-product-ui"}],"install":"npx skills add Aperivue/medsci-skills --skill design-ai-benchmarking","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 aperivue-design-ai-benchmarking","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 \"design-ai-benchmarking\" agent skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking. 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: Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation. 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\":\"aperivue-design-ai-benchmarking\",\"task\":\"Install design-ai-benchmarking\",\"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 \"design-ai-benchmarking\" as a Claude Code skill from https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking. 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: Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation. 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\":\"aperivue-design-ai-benchmarking\",\"task\":\"Install design-ai-benchmarking\",\"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 \"design-ai-benchmarking\" from https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking 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: Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation. 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\":\"aperivue-design-ai-benchmarking\",\"task\":\"Install design-ai-benchmarking\",\"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/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","github_repo":"Aperivue/medsci-skills","version":"1.0.0","license":"MIT","urls":{"web":"https://www.openagentskill.com/skills/aperivue-design-ai-benchmarking","repository":"https://github.com/Aperivue/medsci-skills/tree/main/skills/design-ai-benchmarking","api":"/api/agent/skills/aperivue-design-ai-benchmarking","install_api":"/api/skills/aperivue-design-ai-benchmarking/install"},"meta":{"created_at":"2026-09-06T01:41:04.946252+00:00","updated_at":"2026-09-06T02:30:18.059975+00:00","agent_friendly":true}}