ask-matt
免费Ask which skill or flow fits your situation. A router over the skills in this repo.
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Extract conversation skeleton or error signals from a single session file at a given path. Invoked by session-research agents after they have selected which sessions to deep-dive — not intended for direct user queries.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Agent-facing primitive. Extract filtered content from a single Claude Code, Codex, or Cursor session file — either a conversation skeleton or error signals.
This skill exists so that agents do not read multi-megabyte session files into context. The scripts under scripts/ own the JSONL shape knowledge and emit a narrative-readable digest.
Space-separated positional args:
<file> — absolute path to a session JSONL file (typically a file value returned by ce-session-inventory).<mode> — skeleton or errors.<limit> (optional) — head:N or tail:N to cap output at N lines (e.g., head:200). Omit to return full extraction.Skeleton mode — narrative of user messages, assistant text, and collapsed tool-call summaries:
cat <file> | python3 scripts/extract-skeleton.py
Errors mode — just error signals:
cat <file> | python3 scripts/extract-errors.py
If <limit> is head:N, pipe through head -n N. If tail:N, pipe through tail -n N. Apply the limit after the Python script, never before — the _meta line is emitted last and a head cap may drop it; that is acceptable when the caller asks for a head cap.
Return the raw stdout verbatim. Do not paraphrase, annotate, or synthesize — the caller does synthesis across multiple sessions.
Narrative output with one logical event per block, separated by ---:
[tools] 5x Read (file1, file2, +3 more) -> all ok)Ends with a _meta line: {"_meta": true, "lines": N, "parse_errors": N, "user": N, "assistant": N, "tool": N}.
One line per error, separated by ---:
is_error: trueexec_command_end events with non-zero exit or non-empty stderrEnds with a _meta line: {"_meta": true, "lines": N, "parse_errors": N, "errors_found": N}.
If the file cannot be read, let the error surface to the caller. If _meta reports parse_errors > 0, return the output as-is — partial extraction is still useful and the caller decides whether to widen the search or deep-dive further.
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Ask which skill or flow fits your situation. A router over the skills in this repo.
中文简介正在准备中。当前显示原文说明。
Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
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Run comprehensive agent-native architecture review with scored principles
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Explore requirements and approaches through collaborative dialogue before writing a right-sized requirements document and planning implementation. Use for feature ideas, problem framing, when the user says 'let's brainstorm', or when they want to think through options before deciding what to build. Also use when a user describes a vague or ambitious feature request, asks 'what should we build', 'help me think through X', presents a problem with multiple valid solutions, or seems unsure about scope or direction — even if they don't explicitly ask to brainstorm.
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Clean up local branches whose remote tracking branch is gone. Use when the user says "clean up branches", "delete gone branches", "prune local branches", "clean gone", or wants to remove stale local branches that no longer exist on the remote. Also handles removing associated worktrees for branches that have them.
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Structured code review using tiered persona agents, confidence-gated findings, and a merge/dedup pipeline. Use when reviewing code changes before creating a PR.
中文简介正在准备中。当前显示原文说明。