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Found 25 Skills
Per-conversation cost view — list every session in cost-tracking with started-at, message count, top model, and total cost
Analyzes Claude Code session transcripts (JSONL files) to reveal context window content, token usage patterns, and decision-making processes using view_session_context.py tool. Use when debugging Claude behavior, investigating token patterns, tracking agent delegation, or analyzing context exhaustion. Triggers on "why did Claude do X", "analyze session", "check session logs", "context window exhaustion", or "track agent delegation".
Analyze the current session and propose improvements to skills. **Proactively invoke this skill** when you notice user corrections after skill usage, or at the end of skill-heavy sessions. Also use when user says "reflect", "improve skill", or "learn from this".
Pattern extraction and skill generation for mobile development sessions. Automatically learns from your coding patterns.
Data Cloud 360° view of a single Agentforce session. Pulls 24 STDM + GenAI DMO rows via the DC Query REST API, assembles a hierarchical session tree (Interaction → Step → Generation → GatewayRequest), renders a human-readable summary with transcript + per-turn topic/action invocations + LLM generations + tool calls + audit chain. TRIGGER when user asks to trace, inspect, summarize, or describe a specific Agentforce session by session id (Agent Session UUID `019d…` or MessagingSession id `0Mw…`). Also triggers on session discovery — find/list/search sessions by time, agent, channel, outcome, or conversation text — when the user has no session id yet. DO NOT TRIGGER for design-time architecture questions (use investigating-agentforce-architecture instead) or for runtime perf/latency/SLO questions that require platform telemetry beyond Data Cloud.
Analyze OpenCode conversation history to identify themes and patterns in user messages. Use when asked to analyze conversations, find themes, review how a user steers agents, or extract insights from session history.
Analyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation, review session or chat history, find where a multi-turn chat went wrong, or analyze patterns across turns. Triggers on "analyze this session", "what happened in this conversation", "debug session", "review chat history", "where did this chat go wrong", "session traces", "analyze chat", "debug this chat".
Data Cloud 360° view of a single Agentforce session. TRIGGER when user asks to trace, inspect, summarize, or describe a specific Agentforce session by session id (Agent Session UUID `019d…` or MessagingSession id `0Mw…`). Also triggers on session discovery — find/list/search sessions by time, agent, channel, outcome, or conversation text — when the user has no session id yet. DO NOT TRIGGER for design-time architecture questions (use agentforce-architecture-analyze instead) or for runtime perf/latency/SLO questions that require platform telemetry beyond Data Cloud.
V2 instinct-based observational learning. Analyzes sessions to extract reusable mobile development patterns across time.
This skill should be used when the user asks to "analyze session", "세션 분석", "evaluate skill execution", "스킬 실행 검증", "check session logs", "로그 분석", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.
Analyze development sessions, capture learnings, and improve Claude Code instructions. Use when the user wants to reflect on a session, improve CLAUDE.md, extract learnings, or optimize AI-human collaboration. Supports two modes: quick (default) focuses on CLAUDE.md improvements, deep mode performs comprehensive session analysis with learning capture.
Show detailed token ROI report across all tracked sessions