Safe resolution of ci-blob:// URIs — extract specific fields without dumping full payloads
원문 언어: 영어
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SkillsMP는 microsoft/amplifier-bundle-context-intelligence에서 11개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
수집된 skill 11개 중 11개를 표시합니다.
Safe resolution of ci-blob:// URIs — extract specific fields without dumping full payloads
원문 언어: 영어
Use when querying the context-intelligence property graph for session history, tool call traces, LLM iteration analysis, execution scale metrics, agent delegation trees, skill loading, and recipe orchestration. Covers all graph layers, cross-layer…
원문 언어: 영어
Use when an investigation needs MORE THAN ONE step — track the exploration as a hill climb in the todo tool so progress, dead leads, and findings leave an auditable trail. Governs HOW you track the climb, NOT how you query or extract (those stay in the…
원문 언어: 영어
Use when a graph query returns structurally-identical rows that hide different intent — many delegations all "none/conversation", many look-alike sessions, many identical tool calls — and the raw shape refuses to tell you WHY. Teaches how to derive an…
원문 언어: 영어
Use when an analysis over the context-intelligence graph is about to be written as naive Cypher — reach for Neo4j GDS (Graph Data Science) or APOC instead when the question is really a complex topology search: pathfinding, reachability, centrality/influence,…
원문 언어: 영어
Use when extracting session data directly from JSONL files — the baseline path when the graph server is unavailable or when operating outside graph-analyst
원문 언어: 영어
Reconstruct local Amplifier session files from the context-intelligence graph server — events.jsonl, transcript.jsonl, and metadata.json
원문 언어: 영어
Analyse failure and success patterns across many runs of a specific workflow using context-intelligence session data. Use when you want to answer: "How is <workflow> failing?", "What does a successful run look like vs a failing one?", "Which steps are the…
원문 언어: 영어
Use when designing evaluation scenarios for a context-intelligence tool signal — derives success criteria from domain-concepts.md and produces evaluation-scenarios.md entries and DTU profile templates.
원문 언어: 영어
Use when deciding how to measure a context-intelligence tool signal — metric design across quality/efficiency/efficacy axes, artifact-metric avoidance via precursor measurement, A/B and statistical-N discipline, and test-data fidelity.
원문 언어: 영어
Use when selecting a detection strategy and implementation primitive for a context-intelligence signal — classifies signals as deterministic/probabilistic/llm-evaluated/hybrid and applies the cheapest-sufficient-capability principle.
원문 언어: 영어