Context Intelligence Session Navigation
microsoft/amplifier-bundle-context-intelligence/skills/context-intelligence-session-navigation
analysisResmi
Resmi Sağlayıcı Skill'iView repo
Use when extracting session data directly from JSONL files — the baseline path when the graph server is unavailable or when operating outside graph-analyst
Dosyalar1 dosya
SKILL.md303 satır
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Kurulum
ÖnerilenTek komut — ajanınız otomatik olarak devreye alır.
Kurulum komutunu görmek için yukarıdan bir AI aracı seçin.
veya
Manuel Kurulum
Daha fazla adımDosyayı indirin ve ajanınızın sistem istemine yapıştırın.
Skill detayları
Versiyonv1.0.0
YazarMicrosoft
Kategorianalysis
Skill IDmicrosoft/amplifier-bundle-context-intelligence/skills/context-intelligence-session-navigation
İlgili skill'ler
Blob ReadingSafe resolution of ci-blob:// URIs — extract specific fields without dumping full payloadsContext Intelligence Eval DesignUse 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.Context Intelligence Evaluation MethodologyUse 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.Context Intelligence Graph QueryUse 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 SOURCED_FROM joins, SST navigation, blob handling, and verified Cypher patterns.Context Intelligence Session ReconstructionReconstruct local Amplifier session files from the context-intelligence graph server — events.jsonl, transcript.jsonl, and metadata.jsonContext Intelligence Tool DesignUse 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.