{"eval":{"version":"openagentskill-skill-eval-v1","slug":"datadog-labs-agent-observability-eval-pipeline","name":"agent-observability-eval-pipeline","generated_at":"2026-09-05T12:28:09.222Z","task_input":"Evaluate agent-observability-eval-pipeline before installing it in an AI agent workflow","status":"failed","score":64,"risk_level":"high","decision":{"recommendation":"do_not_auto_install","reason":"Agent safety gate: This skill should not be selected by an agent without explicit human security review.","auto_install_allowed":false,"policy":"block","human_review_required":true},"task_fit":{"score":94,"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Navigate local resources","Run repeatable desktop actions"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"]},"install":{"command":"npx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipeline","ready":true,"policy":"block","safety_label":"Avoid automatic install","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 datadog-labs-agent-observability-eval-pipeline"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"agent-observability-eval-pipeline\" agent skill from https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline. 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: End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a \"continue\" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says \"run the eval pipeline\", \"go from traces to evals\", \"bootstrap evals end to end\", \"classify then RCA then bootstrap\", \"build an eval set from scratch\", \"onboard me to datasets and experiments\", \"walk me through experiments\", \"I have an ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from produ 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\":\"datadog-labs-agent-observability-eval-pipeline\",\"task\":\"Install agent-observability-eval-pipeline\",\"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 \"agent-observability-eval-pipeline\" as a Claude Code skill from https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline. 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: End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a \"continue\" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says \"run the eval pipeline\", \"go from traces to evals\", \"bootstrap evals end to end\", \"classify then RCA then bootstrap\", \"build an eval set from scratch\", \"onboard me to datasets and experiments\", \"walk me through experiments\", \"I have an ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from produ 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\":\"datadog-labs-agent-observability-eval-pipeline\",\"task\":\"Install agent-observability-eval-pipeline\",\"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 \"agent-observability-eval-pipeline\" from https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline 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: End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a \"continue\" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says \"run the eval pipeline\", \"go from traces to evals\", \"bootstrap evals end to end\", \"classify then RCA then bootstrap\", \"build an eval set from scratch\", \"onboard me to datasets and experiments\", \"walk me through experiments\", \"I have an ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from produ 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\":\"datadog-labs-agent-observability-eval-pipeline\",\"task\":\"Install agent-observability-eval-pipeline\",\"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."}]},"trust":{"score":64,"label":"Manual review","version":"trust-score-v4","evidence":{"stars":"158 GitHub stars","repoActivity":"158 stars, 25 forks","lastPushed":"9d since push","license":"MIT","repository":"https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline","install":"npx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipeline","installSafety":"dynamic command execution, standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"}},"audit":{"score":74,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Dependency or permission surface needs review","Permission surface may require sandboxing","The skill invokes sub-skills (agent-observability-session-classify, etc.) that are not included in this repository; they must be separately installed for the pipeline to work.","Phase 5 executes a generated Python script. While this is a core feature, ensure that any user-supplied data (e.g., dataset content) cannot inject unintended code or commands into the generated script.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 158 stars, 25 forks; issue activity unavailable in current metadata","Dependency/runtime risk: command execution surface, credential or environment access"]},"safety_gate":{"score":34,"tier":"blocked","label":"Blocked for auto-install","auto_install_policy":"block","blocked":true,"permission_hints":[{"id":"shell","label":"Shell or command execution","reason":"Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.","severity":"high"},{"id":"network","label":"Network access","reason":"Skill likely fetches remote pages, APIs, repositories, or external services.","severity":"medium"},{"id":"filesystem","label":"Filesystem access","reason":"Skill may read or write project files, documents, generated artifacts, or local workspace state.","severity":"medium"},{"id":"secrets","label":"Secrets or environment access","reason":"Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.","severity":"high"}],"policy_warnings":["High-risk permission hints: Shell or command execution, Secrets or environment access","Dependency or permission surface needs review"]},"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 agent-observability-eval-pipeline before installing it in an AI 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 datadog-labs/agent-skills --skill agent-observability-eval-pipeline"]},{"id":"install_safety","label":"Install command safety","status":"warn","score":68,"required_for_auto_install":true,"detail":"dynamic command execution, standard package or runtime install path","evidence":["npx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipeline"]},{"id":"trust_score","label":"Trust score","status":"warn","score":64,"required_for_auto_install":true,"detail":"Potentially useful, but at least one trust signal needs human inspection.","evidence":["Manual review","158 GitHub stars","MIT"]},{"id":"audit_score","label":"Audit score","status":"warn","score":74,"required_for_auto_install":true,"detail":"Needs review","evidence":["Dependency or permission surface needs review"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"fail","score":34,"required_for_auto_install":true,"detail":"This skill should not be selected by an agent without explicit human security review.","evidence":["Do not auto-install. Inspect the source, dependencies, and permission surface first.","Metadata combines secrets access with shell or command execution"]},{"id":"readme_skillmd_completeness","label":"README/SKILL.md completeness","status":"pass","score":86,"required_for_auto_install":false,"detail":"Metadata includes enough usage and workflow context","evidence":["Strong README/SKILL.md context"]},{"id":"license_clarity","label":"License clarity","status":"pass","score":86,"required_for_auto_install":true,"detail":"MIT","evidence":["MIT"]},{"id":"recent_maintenance","label":"Recent maintenance","status":"pass","score":100,"required_for_auto_install":false,"detail":"9d since push","evidence":["9d since push"]},{"id":"permission_surface","label":"Permission surface","status":"fail","score":22,"required_for_auto_install":true,"detail":"secrets or environment access, shell or command execution","evidence":["Shell or command execution: high","Network access: medium","Filesystem access: medium"]},{"id":"alternatives","label":"Alternatives available","status":"pass","score":82,"required_for_auto_install":false,"detail":"Alternative skills are available for comparison.","evidence":["anthropic-frontend-design","design-taste-frontend","anthropic-canvas-design","anthropic-brand-guidelines"]}],"blockers":["Agent safety gate: This skill should not be selected by an agent without explicit human security review.","Permission surface: secrets or environment access, shell or command execution"],"warnings":["Install command safety: dynamic command execution, standard package or runtime install path","Trust score: Potentially useful, but at least one trust signal needs human inspection.","Audit score: Needs review","High-risk permission hints: Shell or command execution, Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","The skill invokes sub-skills (agent-observability-session-classify, etc.) that are not included in this repository; they must be separately installed for the pipeline to work.","Phase 5 executes a generated Python script. While this is a core feature, ensure that any user-supplied data (e.g., dataset content) cannot inject unintended code or commands into the generated script.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 158 stars, 25 forks; issue activity unavailable in current metadata","Dependency/runtime risk: command execution surface, credential or environment access"],"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."],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","The skill invokes sub-skills (agent-observability-session-classify, etc.) that are not included in this repository; they must be separately installed for the pipeline to work.","No OpenAgentSkill engagement data yet","High-risk permission hints: Shell or command execution, Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","Phase 5 executes a generated Python script. While this is a core feature, ensure that any user-supplied data (e.g., dataset content) cannot inject unintended code or commands into the generated script."],"alternatives":[{"slug":"anthropic-frontend-design","name":"Frontend Design","url":"https://www.openagentskill.com/skills/anthropic-frontend-design","stars":174186,"install_command":"npx skills add anthropics/skills --skill frontend-design","trust_score":91,"audit_score":93},{"slug":"design-taste-frontend","name":"Taste Skill: Anti-Slop Frontend","url":"https://www.openagentskill.com/skills/design-taste-frontend","stars":84391,"install_command":"npx skills add Leonxlnx/taste-skill --skill design-taste-frontend","trust_score":94,"audit_score":96},{"slug":"anthropic-canvas-design","name":"Canvas Design","url":"https://www.openagentskill.com/skills/anthropic-canvas-design","stars":174186,"install_command":"npx skills add anthropics/skills --skill canvas-design","trust_score":90,"audit_score":93},{"slug":"anthropic-brand-guidelines","name":"Anthropic Brand Guidelines","url":"https://www.openagentskill.com/skills/anthropic-brand-guidelines","stars":174186,"install_command":"npx skills add anthropics/skills --skill brand-guidelines","trust_score":91,"audit_score":93}],"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"datadog-labs-agent-observability-eval-pipeline","name":"agent-observability-eval-pipeline","description":"End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a \"continue\" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says \"run the eval pipeline\", \"go from traces to evals\", \"bootstrap evals end to end\", \"classify then RCA then bootstrap\", \"build an eval set from scratch\", \"onboard me to datasets and experiments\", \"walk me through experiments\", \"I have an ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from produ","category":"design-creative","url":"https://www.openagentskill.com/skills/datadog-labs-agent-observability-eval-pipeline","repository":"https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline","github_repo":"datadog-labs/agent-skills"},"suited_tasks":["Research agents workflows","Claude Code teams","builders willing to evaluate younger projects","Search sources","Extract claims","Synthesize findings","Navigate local resources","Run repeatable desktop actions"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","CLI"],"install":{"command":"npx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipeline","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 datadog-labs-agent-observability-eval-pipeline"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"agent-observability-eval-pipeline\" agent skill from https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline. 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: End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a \"continue\" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says \"run the eval pipeline\", \"go from traces to evals\", \"bootstrap evals end to end\", \"classify then RCA then bootstrap\", \"build an eval set from scratch\", \"onboard me to datasets and experiments\", \"walk me through experiments\", \"I have an ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from produ 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\":\"datadog-labs-agent-observability-eval-pipeline\",\"task\":\"Install agent-observability-eval-pipeline\",\"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 \"agent-observability-eval-pipeline\" as a Claude Code skill from https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline. 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: End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a \"continue\" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says \"run the eval pipeline\", \"go from traces to evals\", \"bootstrap evals end to end\", \"classify then RCA then bootstrap\", \"build an eval set from scratch\", \"onboard me to datasets and experiments\", \"walk me through experiments\", \"I have an ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from produ 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\":\"datadog-labs-agent-observability-eval-pipeline\",\"task\":\"Install agent-observability-eval-pipeline\",\"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 \"agent-observability-eval-pipeline\" from https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline 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: End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a \"continue\" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says \"run the eval pipeline\", \"go from traces to evals\", \"bootstrap evals end to end\", \"classify then RCA then bootstrap\", \"build an eval set from scratch\", \"onboard me to datasets and experiments\", \"walk me through experiments\", \"I have an ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from produ 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\":\"datadog-labs-agent-observability-eval-pipeline\",\"task\":\"Install agent-observability-eval-pipeline\",\"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/datadog-labs-agent-observability-eval-pipeline/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/datadog-labs-agent-observability-eval-pipeline"},"trust":{"score":64,"label":"Manual review","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"158 GitHub stars","repoActivity":"158 stars, 25 forks","lastPushed":"9d since push","license":"MIT","repository":"https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline","install":"npx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipeline","installSafety":"dynamic command execution, standard package or runtime install path","permissionSurface":"secrets or environment access, shell or command execution","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["design-creative","agent-skill"],"known_risks":["The skill invokes sub-skills (agent-observability-session-classify, etc.) that are not included in this repository; they must be separately installed for the pipeline to work.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 158 stars, 25 forks; issue activity unavailable in current metadata","Dependency/runtime risk: command execution surface, credential or environment access","Permission surface: secrets or environment access, shell or command execution"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":74,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Dependency or permission surface needs review","Permission surface may require sandboxing","The skill invokes sub-skills (agent-observability-session-classify, etc.) that are not included in this repository; they must be separately installed for the pipeline to work.","Phase 5 executes a generated Python script. While this is a core feature, ensure that any user-supplied data (e.g., dataset content) cannot inject unintended code or commands into the generated script.","Quality score needs review","Permission surface needs review: secrets or environment access, shell or command execution","Stars/forks activity: 158 stars, 25 forks; issue activity unavailable in current metadata","Dependency/runtime risk: command execution surface, credential or environment access"]},"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":69,"label":"Promising"},"supply":{"track":"Research and knowledge work","scenario":"Research agents","maintenance":"9d since push","risk":"Needs review"},"alternative_skills":[{"slug":"anthropic-frontend-design","name":"Frontend Design","url":"https://www.openagentskill.com/skills/anthropic-frontend-design","stars":174186,"install_command":"npx skills add anthropics/skills --skill frontend-design","trust_score":91,"audit_score":93},{"slug":"design-taste-frontend","name":"Taste Skill: Anti-Slop Frontend","url":"https://www.openagentskill.com/skills/design-taste-frontend","stars":84391,"install_command":"npx skills add Leonxlnx/taste-skill --skill design-taste-frontend","trust_score":94,"audit_score":96},{"slug":"anthropic-canvas-design","name":"Canvas Design","url":"https://www.openagentskill.com/skills/anthropic-canvas-design","stars":174186,"install_command":"npx skills add anthropics/skills --skill canvas-design","trust_score":90,"audit_score":93},{"slug":"anthropic-brand-guidelines","name":"Anthropic Brand Guidelines","url":"https://www.openagentskill.com/skills/anthropic-brand-guidelines","stars":174186,"install_command":"npx skills add anthropics/skills --skill brand-guidelines","trust_score":91,"audit_score":93}],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","The skill invokes sub-skills (agent-observability-session-classify, etc.) that are not included in this repository; they must be separately installed for the pipeline to work.","No OpenAgentSkill engagement data yet","High-risk permission hints: Shell or command execution, Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","Phase 5 executes a generated Python script. While this is a core feature, ensure that any user-supplied data (e.g., dataset content) cannot inject unintended code or commands into the generated script."],"agent_contract":{"task_input":"Evaluate agent-observability-eval-pipeline before installing it in an AI agent workflow","recommended_action":"Do not auto-install. 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