{"slug":"topoteretes-cognee","name":"Cognee","description":"Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.","long_description":"---\nname: cognee\ndescription: >\n  Use this skill whenever the user asks about Cognee, AI memory, persistent agent memory,\n  self-improving agents, agents learning from feednack, knowledge graphs, graph-based RAG,\n  long-term memory for agents, short-term memory for agents, personalization, personas,\n  temporal search,  temporal knowledge graphs, ontology-based extraction, ontology grounding,\n  feedback, Cypher search, natural-language graph search, chunk search, RAG search, cross-session memory,\n  session feedback, feedback loops, session based memory, redis based memory, knowledge promotion.\n  Also use when the user describes the workflow such as:\n  \"turn documents into a knowledge graph\", \"build memory from files\", \"search my graph\",\n  \"extract entities and relations\", \"sync data into a graph\", \"update graph memory\",\n  \"store memories for an agent\", \"help my agent learn over time\", \"visualize a knowledge\n  graph built from documents\", \"let the agent learn\", \"adaptive agents\", \"personalized agents\",\n  \"session based personalization\", \"find important ontologies\", \"find custom pydantic models\",\n  \"isolate agentic behaviour\", \"add permission control to retrieval\", \"reduce context bloating\".\n---\n\n# Cognee\n\nUse this skill for **Cognee-specific Python API help** and for mapping user goals to the right Cognee workflow.\n\n## When to apply this skill\n\nApply this skill whenever the user wants to do any of the following with Cognee:\n\n- ingest text, files, URLs, repos, or datasets\n- build or rebuild a knowledge graph\n- search documents, chunks, summaries, triplets, or graph context\n- choose a `SearchType`\n- enrich an existing graph with `memify`\n- define custom graph extraction models or `DataPoint` types\n- run custom task pipelines\n- configure LLM, graph DB, vector DB, or storage settings\n- tag and scope memory with `node_set` / NodeSets\n- build persistent memory for agents across sessions\n- create feedback loops or self-improving agent workflows\n- work with temporal extraction, ontologies, Cypher, or natural-language graph queries\n- manage datasets, sessions, feedback, pruning, updates, or visualization\n\nIf the user’s intent is “store information in memory and query it later,” prefer Cognee’s core flow:\n**add -> cognify -> search**\n\n## Core workflow\n\n```python\nimport cognee\nfrom cognee import SearchType\n\nawait cognee.add(\n    \"Your text, file path, URL, or list of inputs\",\n    dataset_name=\"main\",\n    node_set=[\"default_memory\"],\n)\nawait cognee.cognify(datasets=\"main\")\nresults = await cognee.search(\n    \"What are the key insights?\",\n    query_type=SearchType.GRAPH_COMPLETION,\n    datasets=\"main\",\n)\n```\n\n## Default guidance\n\nWhen helping with Cognee:\n\n1. Start with the **simplest working path** unless the user explicitly asks for advanced configuration.\n2. Prefer the standard workflow:\n   - `add(...)` to ingest\n   - `cognify(...)` to build the graph\n   - `search(...)` to query it\n3. Treat Cognee APIs as **async**.\n4. Use `dataset_name` / `datasets` to keep work organized when the user has multiple sources.\n5. Use `node_set` when the user wants lightweight tagging, project scoping, per-user memory buckets, or subgraph filtering.\n6. Recommend advanced features only when they match the task:\n   - `memify(...)` for enriching an existing graph\n   - `temporal_cognify=True` for time-aware extraction\n   - custom graph models or `DataPoint` types for domain-specific extraction\n   - custom pipelines for non-default task orchestration\n   - feedback loops for retrieval improvement\n   - visualization tools for graph inspection\n\n## Common tasks\n\n### Add data\n\nUse `cognee.add(...)` for text, files, URLs, or mixed inputs.\n\n```python\nawait cognee.add(\"notes.md\", dataset_name=\"research\")\nawait cognee.add(\"https://example.com\", dataset_name=\"research\")\nawait cognee.add([\"paper.pdf\", \"summary.txt\"], dataset_name=\"research\")\n```\n\nUse `node_set` when the user wants data grouped into logical memory buckets.\n\n```python\nawait cognee.add(\n    \"Customer prefers concise weekly summaries and Slack delivery.\",\n    dataset_name=\"customer_success\",\n    node_set=[\"preferences\", \"customer_123\", \"weekly_reports\"],\n)\n```\n\n### Build the graph\n\nUse `cognee.cognify(...)` after ingestion.\n\n```python\nawait cognee.cognify(datasets=\"research\")\n```\n\nUse these options when relevant:\n\n```python\nawait cognee.cognify(\n    datasets=\"research\",\n    temporal_cognify=True,\n    chunk_size=1024,\n    custom_prompt=\"Extract companies, products, and partnerships.\",\n)\n```\n\n### Search the graph\n\nUse `cognee.search(...)` and pick the search mode that matches the request.\n\n```python\nresults = await cognee.search(\n    \"What changed in Q1 2024?\",\n    query_type=SearchType.TEMPORAL,\n    datasets=\"research\",\n    top_k=10,\n)\n```\n\n### Scope search with NodeSets\n\nUse NodeSets when the user wants to search only a subset of memory such as one project, one customer, one user, or one workflow.\n\n```python\nresults = await cognee.search(\n    query_text=\"What are this customer's reporting preferences?\",\n    query_type=SearchType.GRAPH_COMPLETION,\n    datasets=\"customer_success\",\n    node_name=[\"preferences\", \"customer_123\"],\n)\n```\n\n### Enrich an existing graph\n\nUse `memify(...)` when the user wants to improve or extend an already-built graph without restarting the full workflow.\n\n```python\nawait cognee.memify(dataset=\"research\")\n```\n\n### Create domain-specific structures\n\nUse custom models when the user wants extraction shaped around a schema.\n\n```python\nfrom typing import Any\nfrom pydantic import SkipValidation\nfrom cognee.infrastructure.engine import DataPoint\nfrom cognee.tasks.storage import add_data_points\n\nclass ScientificPaper(DataPoint):\n    title: str\n    authors: list[str]\n    methodology: str\n    findings: list[str]\n    cites: SkipValidation[Any] = None\n    metadata: dict = {\"index_fields\": [\"title\", \"findings\"]}\n\npaper = ScientificPaper(\n    title=\"Graph Memory for Agents\",\n    authors=[\"A. Researcher\"],\n    methodology=\"Knowledge graph + vector retrieval\",\n    findings=[\"Improved cross-session recall\", \"Better multi-hop retrieval\"],\n)\n\nawait add_data_points([paper])\n```\n\n### Run custom pipelines\n\nUse `run_custom_pipeline(...)` when the user needs explicit sequential task control.\n\n```python\nfrom cognee.modules.pipelines.tasks.task import Task\n\nasync def my_task(data):\n    return data\n\nawait cognee.run_custom_pipeline(\n    tasks=[Task(my_task)],\n    data=\"input\",\n    dataset=\"research\",\n)\n```\n\n## DataPoints\n\nA `DataPoint` is the **atomic unit of knowledge** in Cognee.\n\nUse this concept whenever the user asks how Cognee represents structured data internally or how to insert graph objects directly.\n\nKey ideas:\n\n- A `DataPoint` is a Pydantic model that represents one meaningful unit of information.\n- It can carry both **content** and **context**, including indexing hints and relationship fields.\n- When inserted directly, DataPoints can become graph nodes and edges while also contributing searchable vector fields.\n- `metadata = {\"index_fields\": [...]}` controls which fields should be embedded for semantic search.\n- Relationship fields can point to other DataPoints, letting you define graph structure programmatically.\n- DataPoints are ideal when the user already has structured objects and does **not** want to rely only on text extraction.\n\nUse `DataPoint` when the user wants:\n\n- schema-shaped memory\n- exact control over graph structure\n- programmatic relationship creation\n- custom domain entities such as papers, customers, incidents, policies, products, or workflows\n\nPrefer plain `add(...) -> cognify(...)` for unstructured documents.\nPrefer `DataPoint` models plus `add_data_points(...)` when the user already has structured Python objects and wants direct graph insertion.\n\n## NodeSets\n\nUse NodeSets when the user wants a lightweight way to **tag, group, and scope memory**.\n\nA NodeSet starts as a simple list of tags passed through `node_set=[...]` during `add(...)`, but after `cognify()` those tags become first-class graph nodes that help organize retrieval.\n\n### Why NodeSets matter\n\n- They let the user organize memory by project, team, customer, workflow, topic, or environment.\n- They make it easy to search only a relevant subgraph instead of the full dataset.\n- They are especially useful in agent systems where one memory store contains many users, jobs, or tasks.\n\n### Good NodeSet patterns\n\n- per customer: `[\"customer_123\"]`\n- per workflow: `[\"support_bot\", \"refund_flow\"]`\n- per topic: `[\"contracts\", \"vendor_risk\"]`\n- per environment: `[\"prod\", \"staging\"]`\n- per user memory: `[\"user_42\", \"preferences\"]`\n\n### Example\n\n```python\nawait cognee.add(\n    [\n        \"Alice prefers terse answers and email follow-ups.\",\n        \"Alice escalates billing issues to finance first.\",\n        \"Bob prefers detailed technical explanations.\"\n    ],\n    dataset_name=\"agent_memory\",\n    node_set=[\"crm\", \"user_profiles\"],\n)\n\nawait cognee.cognify(datasets=\"agent_memory\")\n\nresults = await cognee.search(\n    query_text=\"How should I respond to Alice?\",\n    datasets=\"agent_memory\",\n    node_name=[\"crm\", \"user_profiles\"],\n)\n```\n\nUse NodeSets by default whenever the user says things like:\n\n- “scope memory by customer”\n- “separate projects without making separate databases”\n- “let the agent search only its own memories”\n- “group facts by workflow or team”\n\n## SearchType selection guide\n\nUse these defaults:\n\n- `GRAPH_COMPLETION`: best default for graph-aware Q&A\n- `RAG_COMPLETION`: traditional RAG over document chunks\n- `CHUNKS`: fast semantic retrieval without completion\n- `CHUNKS_LEXICAL`: exact-term / keyword matching\n- `SUMMARIES`: overview of documents\n- `TRIPLET_COMPLETION`: subject-predicate-object style graph Q&A\n- `GRAPH_SUMMARY_COMPLETION`: graph + summary-based answers\n- `GRAPH_COMPLETION_COT`: deeper reasoning over graph context\n- `GRAPH_COMPLETION_CONTEXT_EXTENSION`: broader graph context retrieval\n- `CYPHER`: raw Cypher queries when enabled\n- `NATURAL_LANGUAGE`: natural language to graph query\n- `TEMPORAL`: time-aware graph search\n- `CODING_RULES`: code rules and patterns\n- `CODE`: deterministic code fact lookup, graph traversal, paths, and impact analysis\n- `FEELING_LUCKY`: let Cognee choose automatically\n- `FEEDBACK`: apply feedback to improve later retrieval behavior\n\n## Agentic workflows and feedback-driven improvement\n\nUse Cognee as the **memory layer for agent systems** that need to improve over time through better recall, better reuse of prior work, and better retrieval of successful past behavior.\n\nThe key idea is simple:\n\n- keep the **agent workflow itself constant**\n- keep the **prompt and tools constant**\n- change only what the agent can remember and retrieve\n\nThis means “improvement” comes from **memory reuse and retrieval quality**, not from changing the model or retraining it.\n\n### What Cognee gives agentic workflows\n\nCognee helps agent systems:\n\n- store observations, decisions, outcomes, and learned patterns as memory\n- retrieve graph-aware context instead of relying only on flat chunk search\n- reuse prior investigations, plans, and successful resolutions\n- preserve short-term context through sessions\n- consolidate useful session history into long-term knowledge\n- scope memory by user, customer, workflow, team, or environment with datasets and NodeSets\n- improve future behavior through feedback loops and memory enrichment\n\n### The general feedback pattern\n\nA strong way to explain Cognee in agent systems is:\n\n1. **Baseline condition**\n   The agent searches the existing knowledge graph and acts using only current stored knowledge.\n\n2. **Feedback-enabled condition**\n   The agent uses the same prompt and the same tools, but now benefits from:\n   - **short-term memory** from cached or sessionized interactions\n   - **long-term memory** created by periodically persisting useful sessions back into the graph\n\n3. **Improvement mechanism**\n   Future runs become faster or better because the agent can retrieve:\n   - similar prior cases\n   - successful resolutions\n","tagline":"Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.","category":"rag-knowledge","commerce":{"type":"unknown","billing":"unknown","amount":null,"currency":null,"sourceUrl":null,"checkedAt":null,"runtime":"unknown","purchaseUrl":null,"checkout":"external","purchaseRequiresUserConsent":true},"tags":["vector-database","retrieval","knowledge","agent-memory","agent-skills","ai","ai-agents","ai-memory","cognitive-architecture","cognitive-memory"],"author":"topoteretes","verified":true,"attribution":{"status":"community_indexed","statusLabel":"Community indexed","shortLabel":"COMMUNITY INDEXED","sourceLabel":"GitHub star discovery","sourceDetail":"topoteretes/cognee","creatorName":"topoteretes","creatorUrl":"https://github.com/topoteretes","sourceUrl":"https://github.com/topoteretes/cognee/tree/main/cognee","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/topoteretes-cognee#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":30192,"forks":2952,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":74.56},"quality":{"score":100,"tier":"excellent","label":"Excellent","summary":"High-confidence pick with strong adoption and healthy maintenance signals.","signals":[{"label":"GitHub stars","value":"30K","tone":"positive"},{"label":"Freshness","value":"2mo ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"Apache-2.0","tone":"neutral"}],"warnings":[]},"trust":{"version":"trust-score-v5","score":83,"base_score":91,"outcome_confidence":0,"tier":"strong","label":"Review then install","summary":"Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.","recommendedAction":"Use as the primary candidate after human or sandbox review.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Ask for approval or run a sandbox-only trial before installing.","reasoning":["83/100 Trust Score v5","91/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is missing","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":100,"weight":0.13,"status":"pass","detail":"30K GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":97,"weight":0.08,"status":"pass","detail":"30K stars, 3.0K forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":88,"weight":0.14,"status":"pass","detail":"2mo since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"Apache-2.0"},{"id":"documentation","label":"README/SKILL.md completeness","score":100,"weight":0.14,"status":"pass","detail":"Metadata includes enough usage and workflow context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":74,"weight":0.12,"status":"info","detail":"network or browser surface, database surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add topoteretes/cognee"},{"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":60,"weight":0.07,"status":"warn","detail":"filesystem or document access, network or browser access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/topoteretes/cognee/tree/main/cognee"},{"id":"review_status","label":"Review status","score":88,"weight":0.05,"status":"pass","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"pass","label":"GitHub adoption","detail":"30K GitHub stars"},{"status":"pass","label":"Stars/forks activity","detail":"30K stars, 3.0K forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"2mo since push"},{"status":"pass","label":"License clarity","detail":"Apache-2.0"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"info","label":"Dependency/runtime risk","detail":"network or browser surface, database surface"},{"status":"pass","label":"Install availability","detail":"npx skills add topoteretes/cognee"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"warn","label":"Permission surface","detail":"filesystem or document access, network or browser access"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/topoteretes/cognee/tree/main/cognee"},{"status":"pass","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"pass","label":"Ownership","detail":"Listing manually verified"},{"status":"pass","label":"OpenAgentSkill usage","detail":"85 views, 2 install copies"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["Manually verified listing","Legacy review approval recorded","Install path is available","Repository evidence is available","Recently maintained repository","Large GitHub adoption signal","Install command has no obvious high-risk pattern","OpenAgentSkill usage activity detected","Outcome loop is ready but needs first real agent run"],"warnings":["Financial research output is not financial advice; require human review before any live investment decision.","Permission surface needs review: filesystem or document access, network or browser access","Permission surface: filesystem or document access, network or browser access","No real agent outcome reports yet","Human review required before unattended installation"],"evidence":{"stars":"30K GitHub stars","repoActivity":"30K stars, 3.0K forks","lastPushed":"2mo since push","license":"Apache-2.0","repository":"https://github.com/topoteretes/cognee/tree/main/cognee","install":"The tracked source changed or could not be synchronized. Review the current source before installing.","installSafety":"standard package or runtime install path","permissionSurface":"filesystem or document access, network or browser access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"human_review_before_install"},"installReadiness":{"ready":false,"command":null,"policy":"human_review_before_install","label":"Human review before install","notes":["The tracked source changed or could not be synchronized. Review the current source before installing.","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","2mo since push","Financial domain: human review is required before use in a live investment workflow.","Trust Score v5 requires review or sandbox-only use before install."]},"agentCompatibility":["Python","Vector Search","Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["Financial research output is not financial advice; require human review before any live investment decision.","Permission surface needs review: filesystem or document access, network or browser access","Permission surface: filesystem or document access, network or browser access"]},"outcomeEvidence":{"total":0,"successes":0,"failures":0,"notRelevant":0,"successRate":null,"installAttempts":0,"riskBlocked":0,"setupRequired":0,"installSuccessRate":null,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"recentSuccessRate":null,"recentFailureRate":null,"uniqueAgents":0,"agentProvenScore":0,"agentProvenLabel":"Needs first agent run","lastOutcomeAt":null,"label":"No agent outcome data yet"},"autoInstall":{"allowed":false,"sandboxRequired":true,"policy":"human_review_before_install","reason":"Ask for approval or run a sandbox-only trial 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":["rag-knowledge","vector-database","retrieval","knowledge","agent-memory","agent-skills"],"suited_agents":["Python","Vector Search","Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"install_command":null,"trust_score":83,"trust_version":"trust-score-v5","risk_level":"medium","do_not_use_when":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace","Autonomous investment, trading, tax, or suitability decisions without a qualified human review"],"before_install":["Read the audit page and machine-readable metadata.","Confirm the install command, license, and permission surface fit the workspace.","Get explicit human approval or choose an alternative before installing."],"after_run":["Report the outcome to /api/agent/outcome using the resolve event id.","Include output_quality, workspace, human_review_required, and evidence_url when available.","Re-resolve before broad production rollout."]},"bestFor":["rag-knowledge","vector-database","retrieval","knowledge","agent-memory","agent-skills"],"doNotUseFor":["Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface","Automatic installation in a production workspace","Autonomous investment, trading, tax, or suitability decisions without a qualified human review"],"knownRisks":["Financial research output is not financial advice; require human review before any live investment decision.","Permission surface needs review: filesystem or document access, network or browser access","Permission surface: filesystem or document access, network or browser access"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":91,"tier":"production","label":"Production candidate","summary":"Strong OpenAgentSkill Trust Score across adoption, recent maintenance, license clarity, documentation, dependency/runtime risk, install safety, permission surface, and install availability."}}},"trust_score_v5":{"version":"trust-score-v5","score":83,"base_score":91,"outcome_confidence":0,"tier":"strong","label":"Review then install","summary":"Good shortlist signal, but the agent should review audit notes, install policy, and outcome evidence before running it.","recommendedAction":"Use as the primary candidate after human or sandbox review.","decision":{"install_policy":"human_review_before_install","auto_install_allowed":false,"human_review_required":true,"sandbox_first":true,"agent_action":"Ask for approval or run a sandbox-only trial before installing.","reasoning":["83/100 Trust Score v5","91/100 Trust Score v4 baseline","Needs more real agent outcomes before unattended install","Install path is missing","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":100,"weight":0.13,"status":"pass","detail":"30K GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":97,"weight":0.08,"status":"pass","detail":"30K stars, 3.0K forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":88,"weight":0.14,"status":"pass","detail":"2mo since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"Apache-2.0"},{"id":"documentation","label":"README/SKILL.md completeness","score":100,"weight":0.14,"status":"pass","detail":"Metadata includes enough usage and workflow context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":74,"weight":0.12,"status":"info","detail":"network or browser surface, database surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add topoteretes/cognee"},{"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":60,"weight":0.07,"status":"warn","detail":"filesystem or document access, network or browser access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/topoteretes/cognee/tree/main/cognee"},{"id":"review_status","label":"Review status","score":88,"weight":0.05,"status":"pass","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"pass","label":"GitHub adoption","detail":"30K GitHub stars"},{"status":"pass","label":"Stars/forks activity","detail":"30K stars, 3.0K forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"2mo since push"},{"status":"pass","label":"License clarity","detail":"Apache-2.0"},{"status":"pass","label":"README/SKILL.md completeness","detail":"Metadata includes enough usage and workflow context"},{"status":"info","label":"Dependency/runtime risk","detail":"network or browser surface, database surface"},{"status":"pass","label":"Install availability","detail":"npx skills add topoteretes/cognee"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"warn","label":"Permission surface","detail":"filesystem or document access, network or browser access"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/topoteretes/cognee/tree/main/cognee"},{"status":"pass","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"pass","label":"Ownership","detail":"Listing manually verified"},{"status":"pass","label":"OpenAgentSkill usage","detail":"85 views, 2 install copies"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["Manually verified listing","Legacy review approval recorded","Install path is available","Repository evidence is available","Recently maintained repository","Large GitHub adoption signal","Install command has no obvious high-risk pattern","OpenAgentSkill usage activity detected","Outcome loop is ready but needs first real agent run"],"warnings":["Financial research output is not financial advice; require human review before any live investment decision.","Permission surface needs review: filesystem or document access, network or browser access","Permission surface: filesystem or document access, network or browser access","No real agent outcome reports yet","Human review required before unattended installation"],"evidence":{"stars":"30K GitHub stars","repoActivity":"30K stars, 3.0K forks","lastPushed":"2mo since push","license":"Apache-2.0","repository":"https://github.com/topoteretes/cognee/tree/main/cognee","install":"The tracked source changed or could not be synchronized. Review the current source before installing.","installSafety":"standard package or runtime install path","permissionSurface":"filesystem or document access, network or browser access","documentation":"Strong README/SKILL.md context","agentOutcomes":"No agent outcome data yet","agentProvenScore":0,"outcomeConfidence":"0%","installPolicy":"human_review_before_install"},"installReadiness":{"ready":false,"command":null,"policy":"human_review_before_install","label":"Human review before install","notes":["The tracked source changed or could not be synchronized. 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