{"slug":"scdenney-conjoint-diagnostics","name":"conjoint-diagnostics","description":"Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning.","long_description":"---\nname: conjoint-diagnostics\ndescription: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning.\n---\n\n# Conjoint Experiment Diagnostics\n\n## Instructions\n\nWork through each section below for the conjoint study under review. Assess whether the study addresses each item adequately, partially, or not at all. Flag items that pose threats to inference and prioritize recommendations by severity.\n\nBranch on input:\n- **If a paper or manuscript is provided**, proceed through Sections 1–5 sequentially and produce a verdict per section.\n- **If analysis code or data is provided**, verify the actual implementation rather than just what the paper claims: (a) confirm clustering specification, (b) confirm the estimand matches the reported quantity, (c) if IRR is unmeasured, compute within-respondent task-pair agreement as a function of attribute-level differences (Clayton et al. 2023 §3.3 method 2 / `projoint`).\n\nFor neighboring concerns, invoke sibling skills: `conjoint-design` (design choices), `conjoint-cleaning` (Qualtrics exports → long format), `hypothesis-building` (linking estimands to \"If-Then\" predictions), `methods-reporting` (full JARS/DA-RT compliance and replication archive), `cross-national-design` (multi-country / multilingual conjoints).\n\n---\n\n## 1. Design Diagnostics\n\n### 1.1 Attribute and Level Selection\n- Are attributes conceptually distinct and non-overlapping?\n- Are levels realistic and mutually exclusive within each attribute?\n- Is the number of attributes justified? (Bansak et al. 2021 PSRM \"Beyond the Breaking Point\": response quality is generally robust even at 35 filler attributes, with detectable but modest satisficing — use as a ceiling, not an endorsement of 35 attributes as optimal. Distinct from Bansak et al. 2018 on task counts below.)\n- For cross-national or multilingual designs, verify construct equivalence of attribute labels across languages (see `cross-national-design`).\n- Are there \"dominant\" attributes that might crowd out attention to others?\n- Do the attribute levels span a meaningful range of the construct of interest?\n\n### 1.2 Profile Restrictions\n- Are implausible or contradictory attribute combinations allowed?\n- If restrictions are imposed, are they documented and justified?\n- If no restrictions, does the paper acknowledge potential odd profiles? (Bansak & Jenke 2025: odd profiles have minimal impact on first-order inferences, but should be acknowledged)\n\n### 1.3 Number of Tasks and Satisficing\n- How many tasks per respondent? (Bansak et al. 2018: up to 30 tasks with limited satisficing)\n- Are attention checks embedded? Are pass rates reported by task position?\n- Is there evidence of survey fatigue or satisficing in later tasks? Look for: (a) response-time distributions by task position (not just means), (b) always-same-side / always-left or always-right rates, (c) random-choice / trembling-hand rates relative to Bansak et al. 2021 baselines, (d) attention-check failure rates by task position.\n- Have profile-order, carryover, and fatigue assumptions been tested empirically? (Ham, Imai & Janson 2024 §3.5: `CRTConjoint` provides conditional randomization tests for all three of Hainmueller-Hopkins-Yamamoto 2014's identifying assumptions.)\n\n### 1.4 Randomization\n- Is attribute-level assignment fully randomized (uniform distribution)?\n- If non-uniform, is this justified and documented?\n- Are profile-pair attributes independent or dependent? (Clayton et al. 2023 distinguish independent, dependent, and pair-level attributes)\n\n### 1.5 Sample and Power\n- Is the sample size justified with a power analysis? (Schuessler & Freitag 2020 cjpowR; Stefanelli & Lukac 2020)\n- What is the effective sample size (N respondents x T tasks)?\n- Are Type S (sign) and Type M (magnitude/exaggeration) errors considered?\n- For subgroup analyses, is there adequate power within each subgroup?\n\n---\n\n## 2. Estimation Diagnostics\n\n### 2.1 Estimand Clarity\n- Verify the estimand is clearly defined (AMCE, MM, AMIE, or pAMCE) and the choice is justified.\n- Check whether AMCEs are interpreted with awareness that they depend on the attribute distribution used for averaging (Hainmueller et al. 2014).\n- Check whether MMs are used where reference-category-free comparisons are needed (Leeper et al. 2020).\n\n### 2.2 Reference Levels\n- Are reference levels clearly specified and substantively meaningful?\n- Are AMCEs interpreted relative to the correct reference category?\n- For subgroup comparisons, is the reference category consistent across groups? (Leeper et al. 2020: conditional AMCEs are sensitive to reference choice)\n\n### 2.3 Subgroup Analysis\n- Are subgroups defined by pre-treatment characteristics (not post-treatment)?\n- **Use marginal means and diff-in-MMs for subgroup comparisons**, not conditional AMCEs. (Leeper, Hobolt & Tilley 2020: conditional AMCEs are reference-category dependent and can yield arbitrary subgroup differences)\n- Are diff-in-MMs presented alongside MMs for interpretability?\n- Is heterogeneity detection systematic or ad hoc? (Robinson & Duch 2024 cjbart; Goplerud et al. 2025 FactorHet)\n\n### 2.4 Standard Errors and Clustering\n- Are standard errors clustered at the respondent level? Clustering is conventional with T > 1 tasks per respondent, though Schuessler & Freitag (2020) show the adjustment averages only ~2% across published conjoints and is not strictly necessary for sample causal effects. Flag clustering absent without justification, but do not auto-fail.\n- Is the clustering variable correctly specified?\n- Are confidence intervals reported? At what level? (Convention: 95%, some papers use 90% or dual CI bars)\n\n### 2.5 Multiple Testing\n- How many AMCEs/MMs are estimated in total?\n- Is there a correction for multiple comparisons? (Liu & Shiraito 2023: >90% chance of at least one spurious significant AMCE with no correction when no true effects exist)\n- If no correction, is this limitation acknowledged?\n- Consider: Bonferroni (conservative), Benjamini-Hochberg (FDR), adaptive shrinkage (recommended as default when limited prior knowledge)\n\n---\n\n## 3. Measurement Error Diagnostics (Clayton et al. 2023)\n\nWorry about measurement error whenever the study draws subgroup comparisons, reports small-to-moderate AMCEs/MMs near zero, or forgoes any IRR estimate. Conjoint responses carry **swapping error** (not classical noise): average Intra-Respondent Reliability is ~77% across Clayton et al.'s eight replications, biasing MMs toward 0.5 and AMCEs toward 0. For subgroup diff-in-AMCEs, the correction *increases* the estimated difference ~82% of the time, decreases it ~12%, and flips its sign ~5% -- uncorrected subgroup contrasts are usually understated, not overstated. Audit the study for (a) an IRR estimate or justified borrow (≈0.75 default), (b) bias correction via the `projoint` R package (Clayton et al.), and (c) sensitivity analysis across plausible IRR values if uncorrected.\n\n> **Detailed measurement-error workflow:** see [references/measurement-error.md](references/measurement-error.md).\n\n---\n\n## 4. External Validity Diagnostics\n\n### 4.1 Behavioral Benchmarking\n- Does the paper position its design against the Hainmueller-Hangartner-Yamamoto 2015 benchmark, or provide other behavioral validation? HHY 2015 compared conjoint and vignette estimates to a Swiss naturalization referendum natural experiment; the paired forced-choice conjoint recovered behavioral effects to within ~2 percentage points. Absent a direct behavioral benchmark, the paper should at minimum reference HHY 2015's evidence that paired forced-choice designs have strong external validity for comparable decision contexts.\n\n### 4.2 Profile Distribution\n- Does the uniform attribute distribution match real-world frequencies? (de la Cuesta et al. 2022)\n- If not, does the paper discuss how this affects interpretation?\n- Are pAMCEs computed for comparison?\n\n### 4.3 Forced Choice vs. Real-World Behavior\n- Does the forced-choice format accurately reflect the decision context? (Visconti & Yang 2024)\n- Should an abstention/neither option be offered? (Miller & Ziegler 2024: preferential abstention can produce different-sign AMCEs)\n- Is there a rating outcome alongside forced choice? (Treger 2025: forced-choice and rating elicit distinct preferences)\n- **Outcome-type robustness**: If both forced-choice and rating were collected, do AMCEs/MMs agree in sign and ranking across outcome types? Divergence is substantive, not a nuisance.\n\n### 4.4 Attention and Salience\n- Does the conjoint format artificially inflate attention to attributes that respondents would ignore in real decisions? (Fu & Li 2024)\n- Could this lead to effect magnitude amplification, sign reversal, or importance reversal?\n- What real-world decision process is the conjoint trying to simulate? State the target DGP explicitly — without it, \"attention inflation\" cannot be assessed.\n\n---\n\n## 5. Interpretation Diagnostics\n\n### 5.1 AMCE Interpretation\n- Does the paper correctly interpret AMCEs as average effects on choice probability, not majority preferences? (Abramson et al. 2022; Ganter 2023 sharpens this: AMCE identifies a choice-probability effect, not a parameter of the underlying preference distribution.)\n- Does the paper acknowledge that AMCEs depend on the distribution of other attributes? (Bansak et al. 2023 respond that AMCEs map to vote share changes)\n- Are AMCEs interpreted as causal effects or merely as preference rankings?\n- If the design relies on the SDB-mitigation argument for conjoints, is the claim hedged in light of Horiuchi, Markovich, and Yamamoto (2022)? Their direct test shows conjoints reduce SDB on some attributes but not others; the mitigation is not automatic.\n\n### 5.2 Lexicographic / Categorical Preferences\n- Could respondents be applying a categorical veto (always rejecting profiles with a given attribute level)?\n- If so, standard AMCEs and MMs may be misleading due to co-occurrence rates across task pairs.\n- Are nested marginal means used to detect attribute ranking? (Dill, Howlett & Müller-Crepon 2024; `cjRank` R package)\n- Does the paper test whether lower-ranked attributes matter conditional on the veto attribute being held constant?\n\n### 5.3 Magnitude Reporting\n- Are effect sizes reported in interpretable units (percentage points)?\n- Are substantive significance thresholds discussed alongside statistical significance?\n- Is there comparison to benchmark effect sizes in similar studies?\n- **Cross-attribute magnitude comparisons require caution.** Leeper et al. 2020 (fn 3): in forced-choice designs where both profiles can share a level, MMs are bounded by the co-occurrence probability (for 5 equally likely levels, MMs are bounded to ~(0.04, 0.96)); AMCEs for binary attributes are bounded to (−0.5, 0.5). Comparing raw AMCE magnitudes across attributes with different level counts conflates the effect with its mechanical bound.\n\n### 5.4 Interaction Effects\n- If interactions are examined, are AMIEs used rather than conditional AMCEs? (Egami & Imai 2019)\n- Are interaction coefficients from dummy-coded regressions interpreted? If so, flag: these are baseline-dependent artifacts (Egami & Imai 2019).\n- Is there a test for whether a factor matters at all? (Ham et al. 2024 CRTConjoint: conditional randomization test)\n- **Configural vs additive cl","tagline":"Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clust","category":"design-creative","tags":["agent-skill"],"author":"scdenney","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"recursive skill source sync","sourceDetail":"scdenney/open-science-skills","creatorName":"scdenney","creatorUrl":"https://github.com/scdenney","sourceUrl":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/scdenney-conjoint-diagnostics#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":52,"forks":3,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":35.17},"quality":{"score":64,"tier":"promising","label":"Promising","summary":"Useful candidate, but compare it with alternatives before adopting.","signals":[{"label":"GitHub stars","value":"52","tone":"neutral"},{"label":"Freshness","value":"4d ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"NOASSERTION","tone":"neutral"}],"warnings":["Repository license is NOASSERTION, lacking clear licensing for the skill content."]},"trust":{"version":"trust-score-v5","score":66,"base_score":74,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. 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require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata","No real agent outcome reports yet","Human review required before unattended installation"],"evidence":{"stars":"52 GitHub stars","repoActivity":"52 stars, 3 forks","lastPushed":"4d since push","license":"NOASSERTION","repository":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","install":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","installSafety":"standard package or runtime install path","permissionSurface":"no high-risk permission surface in public metadata","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"sandbox_only"},"installReadiness":{"ready":true,"command":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","policy":"sandbox_only","label":"Sandbox only","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","4d since push","Financial domain: human review is required before use in a live investment workflow.","Trust Score v5 requires review or sandbox-only use before install."]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars"]},"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":"sandbox_only","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 scdenney/open-science-skills --skill conjoint-diagnostics","trust_score":66,"trust_version":"trust-score-v5","risk_level":"medium","do_not_use_when":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace","Autonomous investment, trading, tax, or suitability decisions without a qualified human review","Live brokerage, exchange, wallet, or payment credentials outside an explicitly approved sandbox"],"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","Autonomous investment, trading, tax, or suitability decisions without a qualified human review","Live brokerage, exchange, wallet, or payment credentials outside an explicitly approved sandbox"],"knownRisks":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":74,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout."}}},"trust_score_v5":{"version":"trust-score-v5","score":66,"base_score":74,"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":"sandbox_only","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Compare alternatives before installing.","reasoning":["66/100 Trust Score v5","74/100 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":48,"weight":0.13,"status":"warn","detail":"52 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":43,"weight":0.08,"status":"warn","detail":"52 stars, 3 forks; 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issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"4d since push"},{"status":"pass","label":"License clarity","detail":"NOASSERTION"},{"status":"info","label":"README/SKILL.md completeness","detail":"Public metadata needs stronger README/SKILL.md context"},{"status":"pass","label":"Dependency/runtime risk","detail":"no major dependency risk hints in public metadata"},{"status":"pass","label":"Install availability","detail":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"pass","label":"Permission surface","detail":"no high-risk permission surface in public metadata"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics"},{"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":"info","label":"OpenAgentSkill usage","detail":"No local usage activity yet"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern","Outcome loop is ready but needs first real agent run"],"warnings":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata","No real agent outcome reports yet","Human review required before unattended installation"],"evidence":{"stars":"52 GitHub stars","repoActivity":"52 stars, 3 forks","lastPushed":"4d since push","license":"NOASSERTION","repository":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","install":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","installSafety":"standard package or runtime install path","permissionSurface":"no high-risk permission surface in public metadata","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"sandbox_only"},"installReadiness":{"ready":true,"command":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","policy":"sandbox_only","label":"Sandbox only","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","4d since push","Financial domain: human review is required before use in a live investment workflow.","Trust Score v5 requires review or sandbox-only use before install."]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars"]},"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":"sandbox_only","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 scdenney/open-science-skills --skill conjoint-diagnostics","trust_score":66,"trust_version":"trust-score-v5","risk_level":"medium","do_not_use_when":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace","Autonomous investment, trading, tax, or suitability decisions without a qualified human review","Live brokerage, exchange, wallet, or payment credentials outside an explicitly approved sandbox"],"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","Autonomous investment, trading, tax, or suitability decisions without a qualified human review","Live brokerage, exchange, wallet, or payment credentials outside an explicitly approved sandbox"],"knownRisks":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":74,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout."}}},"trust_score_v4":{"version":"trust-score-v4","score":74,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout.","recommendedAction":"Test in a sandbox workflow and compare its install path with close alternatives.","dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":48,"weight":0.13,"status":"warn","detail":"52 GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":43,"weight":0.08,"status":"warn","detail":"52 stars, 3 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"4d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"NOASSERTION"},{"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":90,"weight":0.12,"status":"pass","detail":"no major dependency risk hints in public metadata"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics"},{"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":100,"weight":0.07,"status":"pass","detail":"no high-risk permission surface in public metadata"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics"},{"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":"warn","label":"GitHub adoption","detail":"52 GitHub stars"},{"status":"warn","label":"Stars/forks activity","detail":"52 stars, 3 forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"4d since push"},{"status":"pass","label":"License clarity","detail":"NOASSERTION"},{"status":"info","label":"README/SKILL.md completeness","detail":"Public metadata needs stronger README/SKILL.md context"},{"status":"pass","label":"Dependency/runtime risk","detail":"no major dependency risk hints in public metadata"},{"status":"pass","label":"Install availability","detail":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"pass","label":"Permission surface","detail":"no high-risk permission surface in public metadata"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics"},{"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":"info","label":"OpenAgentSkill usage","detail":"No local usage activity yet"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Install command has no obvious high-risk pattern"],"warnings":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"],"evidence":{"stars":"52 GitHub stars","repoActivity":"52 stars, 3 forks","lastPushed":"4d since push","license":"NOASSERTION","repository":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","install":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","installSafety":"standard package or runtime install path","permissionSurface":"no high-risk permission surface in public metadata","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet"},"installReadiness":{"ready":true,"command":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","policy":"sandbox_only","label":"Sandbox only","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","4d since push","Financial domain: human review is required before use in a live investment workflow."]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars"]},"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":"sandbox_only","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","Autonomous investment, trading, tax, or suitability decisions without a qualified human review","Live brokerage, exchange, wallet, or payment credentials outside an explicitly approved sandbox"],"knownRisks":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"]},"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":66,"level":"avoid_auto_install","label":"Avoid automatic install","safety_tier":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","summary":"This skill should not be selected by an agent without explicit human security review.","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","auto_install_policy":"block","reasons":["Audit risk exceeds the requested agent policy","Audit classified this skill as risky","Audit risk risky exceeds max_risk=medium"]},"auto_install_allowed":false,"human_review_required":true,"blocked":true,"audit_risk":"risky","permission_hints":[{"id":"network","label":"Network access","reason":"Skill likely fetches remote pages, APIs, repositories, or external services.","severity":"medium"}],"policy_warnings":["Audit risk risky exceeds max_risk=medium","Financial research output is not financial advice; require human review before any live investment decision"],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","badge":"BLOCKED","auto_install_policy":"block","auto_install_allowed":false,"blocked":true,"human_review_required":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","reasons":["Audit risk exceeds the requested agent policy","Audit classified this skill as risky","Audit risk risky exceeds max_risk=medium"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"failed","score":72,"risk_level":"high","decision":{"recommendation":"do_not_auto_install","reason":"Audit score: Risky","auto_install_allowed":false,"policy":"block","human_review_required":true},"blockers":["Audit score: Risky","Agent safety gate: This skill should not be selected by an agent without explicit human security review."],"warnings":["Trust score: Good trust signals with a few areas worth checking before rollout.","README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context","Audit risk risky exceeds max_risk=medium","Financial research output is not financial advice; require human review before any live investment decision","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"],"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":84,"required_for_auto_install":true,"detail":"Task wording matches this skill metadata.","evidence":["Evaluate conjoint-diagnostics before installing it in an agent workflow","design-creative","Coding 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 scdenney/open-science-skills --skill conjoint-diagnostics"]},{"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 scdenney/open-science-skills --skill conjoint-diagnostics"]},{"id":"trust_score","label":"Trust score","status":"warn","score":74,"required_for_auto_install":true,"detail":"Good trust signals with a few areas worth checking before rollout.","evidence":["Strong shortlist","52 GitHub stars","NOASSERTION"]},{"id":"audit_score","label":"Audit score","status":"fail","score":78,"required_for_auto_install":true,"detail":"Risky","evidence":["Financial research output is not financial advice; require human review before any live investment decision"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"fail","score":66,"required_for_auto_install":true,"detail":"This skill should not be selected by an agent without explicit human security review.","evidence":["Do not auto-install. Inspect the source, dependencies, and permission surface first.","Audit risk exceeds the requested agent policy"]},{"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":"NOASSERTION","evidence":["NOASSERTION"]},{"id":"recent_maintenance","label":"Recent maintenance","status":"pass","score":100,"required_for_auto_install":false,"detail":"4d since push","evidence":["4d since push"]},{"id":"permission_surface","label":"Permission surface","status":"pass","score":100,"required_for_auto_install":true,"detail":"no high-risk permission surface in public metadata","evidence":["Network 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/scdenney-conjoint-diagnostics/evals","api":"/api/agent/evals?slug=scdenney-conjoint-diagnostics","text":"/api/agent/evals?slug=scdenney-conjoint-diagnostics&format=text"}},"agent_readable_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"scdenney-conjoint-diagnostics","name":"conjoint-diagnostics","description":"Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning.","category":"design-creative","url":"https://www.openagentskill.com/skills/scdenney-conjoint-diagnostics","repository":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","github_repo":"scdenney/open-science-skills"},"suited_tasks":["Coding agents workflows","Claude Code teams","builders willing to evaluate younger projects","Inspect source files","Explain architecture","Patch bugs and verify changes","Chunk documents","Create embeddings"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","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 scdenney-conjoint-diagnostics"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"conjoint-diagnostics\" agent skill from https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics. 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: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning. 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\":\"scdenney-conjoint-diagnostics\",\"task\":\"Install conjoint-diagnostics\",\"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 \"conjoint-diagnostics\" as a Claude Code skill from https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics. 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: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning. 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\":\"scdenney-conjoint-diagnostics\",\"task\":\"Install conjoint-diagnostics\",\"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 \"conjoint-diagnostics\" from https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics 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: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning. 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\":\"scdenney-conjoint-diagnostics\",\"task\":\"Install conjoint-diagnostics\",\"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/scdenney-conjoint-diagnostics/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/scdenney-conjoint-diagnostics"},"trust":{"score":74,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"sandbox_only","evidence":{"stars":"52 GitHub stars","repoActivity":"52 stars, 3 forks","lastPushed":"4d since push","license":"NOASSERTION","repository":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","install":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","installSafety":"standard package or runtime install path","permissionSurface":"no high-risk permission surface in public metadata","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":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"]},"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":"risky","risk_label":"Risky","warnings":["Financial research output is not financial advice; require human review before any live investment decision","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"]},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","auto_install_policy":"block","auto_install_allowed":false,"human_review_required":true,"blocked":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first."},"quality":{"score":64,"label":"Promising"},"supply":{"track":"Research and knowledge work","scenario":"RAG and knowledge","maintenance":"4d since push","risk":"Risky"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","Repository license is NOASSERTION, lacking clear licensing for the skill content.","No OpenAgentSkill engagement data yet","Audit risk risky exceeds max_risk=medium","Financial research output is not financial advice; require human review before any live investment decision","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","Financial research output is not financial advice; require human review before any live investment decision."],"agent_contract":{"task_input":"Use conjoint-diagnostics in an agent workflow","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","install_policy":"block","minimum_review_before_use":["Trust: 74/100 Strong shortlist","Audit: 78/100 Risky","Safety: 66/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"scdenney-conjoint-diagnostics (conjoint-diagnostics)","install_command":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","risk_summary":"Risky; Blocked for auto-install; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"scdenney-conjoint-diagnostics","task":"Use conjoint-diagnostics 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/scdenney-conjoint-diagnostics","api":"https://www.openagentskill.com/api/agent/skills/scdenney-conjoint-diagnostics","audit":"https://www.openagentskill.com/skills/scdenney-conjoint-diagnostics/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=scdenney-conjoint-diagnostics&task=Use%20conjoint-diagnostics%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20conjoint-diagnostics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20conjoint-diagnostics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/scdenney-conjoint-diagnostics/install","manifest":"https://www.openagentskill.com/api/registry/manifest/scdenney-conjoint-diagnostics"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"scdenney-conjoint-diagnostics","name":"conjoint-diagnostics","description":"Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning.","category":"design-creative","url":"https://www.openagentskill.com/skills/scdenney-conjoint-diagnostics","repository":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","github_repo":"scdenney/open-science-skills"},"suited_tasks":["Coding agents workflows","Claude Code teams","builders willing to evaluate younger projects","Inspect source files","Explain architecture","Patch bugs and verify changes","Chunk documents","Create embeddings"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","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 scdenney-conjoint-diagnostics"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"conjoint-diagnostics\" agent skill from https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics. 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: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning. 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\":\"scdenney-conjoint-diagnostics\",\"task\":\"Install conjoint-diagnostics\",\"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 \"conjoint-diagnostics\" as a Claude Code skill from https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics. 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: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning. 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\":\"scdenney-conjoint-diagnostics\",\"task\":\"Install conjoint-diagnostics\",\"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 \"conjoint-diagnostics\" from https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics 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: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning. 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\":\"scdenney-conjoint-diagnostics\",\"task\":\"Install conjoint-diagnostics\",\"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/scdenney-conjoint-diagnostics/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/scdenney-conjoint-diagnostics"},"trust":{"score":74,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"sandbox_only","evidence":{"stars":"52 GitHub stars","repoActivity":"52 stars, 3 forks","lastPushed":"4d since push","license":"NOASSERTION","repository":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","install":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","installSafety":"standard package or runtime install path","permissionSurface":"no high-risk permission surface in public metadata","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":["Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"]},"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":"risky","risk_label":"Risky","warnings":["Financial research output is not financial advice; require human review before any live investment decision","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"]},"safety_gate":{"tier":"blocked","label":"Blocked for auto-install","auto_install_policy":"block","auto_install_allowed":false,"human_review_required":true,"blocked":true,"recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first."},"quality":{"score":64,"label":"Promising"},"supply":{"track":"Research and knowledge work","scenario":"RAG and knowledge","maintenance":"4d since push","risk":"Risky"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","Repository license is NOASSERTION, lacking clear licensing for the skill content.","No OpenAgentSkill engagement data yet","Audit risk risky exceeds max_risk=medium","Financial research output is not financial advice; require human review before any live investment decision","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","Financial research output is not financial advice; require human review before any live investment decision."],"agent_contract":{"task_input":"Use conjoint-diagnostics in an agent workflow","recommended_action":"Do not auto-install. Inspect the source, dependencies, and permission surface first.","install_policy":"block","minimum_review_before_use":["Trust: 74/100 Strong shortlist","Audit: 78/100 Risky","Safety: 66/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"scdenney-conjoint-diagnostics (conjoint-diagnostics)","install_command":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","risk_summary":"Risky; Blocked for auto-install; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"scdenney-conjoint-diagnostics","task":"Use conjoint-diagnostics 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/scdenney-conjoint-diagnostics","api":"https://www.openagentskill.com/api/agent/skills/scdenney-conjoint-diagnostics","audit":"https://www.openagentskill.com/skills/scdenney-conjoint-diagnostics/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=scdenney-conjoint-diagnostics&task=Use%20conjoint-diagnostics%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20conjoint-diagnostics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20conjoint-diagnostics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/scdenney-conjoint-diagnostics/install","manifest":"https://www.openagentskill.com/api/registry/manifest/scdenney-conjoint-diagnostics"}},"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":"RAG and knowledge","description":"I need my agent to build a RAG workflow over documents and retrieve reliable context.","useCases":[{"slug":"coding-agents","title":"Coding agents"},{"slug":"rag-knowledge","title":"RAG and knowledge"},{"slug":"browser-automation","title":"Browser automation"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":52,"starsLabel":"52","forks":3,"license":"NOASSERTION","qualityScore":64,"trustScore":74,"auditScore":78},"maintenance":{"status":"fresh","label":"4d since push","daysSincePush":4,"lastPushedAt":"2026-09-02T08:01:46+00:00"},"risk":{"level":"risky","label":"Risky","requiresReview":true,"notes":["Financial research output is not financial advice; require human review before any live investment decision","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval."]},"coverageTags":["Research","RAG and knowledge","design-creative","agent-skill"]},"audit":{"audit_score":78,"risk_level":"risky","risk_label":"Risky","quality_score":64,"trust_score":74,"maintenance_score":100,"security_score":83,"install_score":92,"warnings":["Financial research output is not financial advice; require human review before any live investment decision","Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required","Repository license is NOASSERTION, lacking clear licensing for the skill content.","Financial research output is not financial advice; require human review before any live investment decision.","This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.","Quality score needs review","GitHub adoption: 52 GitHub stars","Stars/forks activity: 52 stars, 3 forks; issue activity unavailable in current metadata"]},"quality_signals":{"model":"v2","star_score":12.07,"usage_score":0,"review_score":5.1,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code"],"use_cases":[{"slug":"coding-agents","title":"Coding agents","url":"https://www.openagentskill.com/use-cases/coding-agents"},{"slug":"rag-knowledge","title":"RAG and knowledge","url":"https://www.openagentskill.com/use-cases/rag-knowledge"},{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"},{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"}],"stacks":[{"slug":"coding-review-agent","title":"Coding review agent","url":"https://www.openagentskill.com/collections/coding-review-agent"},{"slug":"rag-knowledge-base","title":"RAG knowledge base","url":"https://www.openagentskill.com/collections/rag-knowledge-base"},{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"}],"install":"npx skills add scdenney/open-science-skills --skill conjoint-diagnostics","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 scdenney-conjoint-diagnostics","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 \"conjoint-diagnostics\" agent skill from https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics. 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: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning. 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\":\"scdenney-conjoint-diagnostics\",\"task\":\"Install conjoint-diagnostics\",\"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 \"conjoint-diagnostics\" as a Claude Code skill from https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics. 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: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning. 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\":\"scdenney-conjoint-diagnostics\",\"task\":\"Install conjoint-diagnostics\",\"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 \"conjoint-diagnostics\" from https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics 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: Reviews an existing conjoint study for threats to inference and returns prioritized findings across five areas — design integrity (attributes, profile restrictions, task count and satisficing, randomization, power), estimation (estimand clarity, reference levels, subgroups, clustered standard errors, multiple testing), measurement error, external validity and behavioral benchmarking, and interpretation, including the guard-rail against reading an AMCE as a majority preference. Use when the user asks whether a conjoint design or analysis holds up, has referee comments on a conjoint, or wants a second opinion on estimation and interpretation choices. Building a design from scratch goes to conjoint-design, reshaping the data to conjoint-cleaning. 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\":\"scdenney-conjoint-diagnostics\",\"task\":\"Install conjoint-diagnostics\",\"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/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","github_repo":"scdenney/open-science-skills","version":"1.0.0","license":"NOASSERTION","urls":{"web":"https://www.openagentskill.com/skills/scdenney-conjoint-diagnostics","repository":"https://github.com/scdenney/open-science-skills/tree/main/codex/conjoint-diagnostics","api":"/api/agent/skills/scdenney-conjoint-diagnostics","install_api":"/api/skills/scdenney-conjoint-diagnostics/install"},"meta":{"created_at":"2026-09-02T11:47:25.301066+00:00","updated_at":"2026-09-02T11:47:25.414065+00:00","agent_friendly":true}}