ask-matt
무료Ask which skill or flow fits your situation. A router over the skills in this repo.
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Discover session files for a repo across Claude Code, Codex, and Cursor, and extract session metadata (timestamps, branch, cwd, size, platform). Invoked by session-research agents — not intended for direct user queries.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Agent-facing primitive. Discover session files and emit session metadata as JSONL across Claude Code, Codex, and Cursor.
This skill exists so that agents researching session history do not need to know the layout of session stores on disk or the JSONL shapes of each platform. The scripts under scripts/ own that knowledge.
Space-separated positional args:
<repo> — repo folder name (e.g., my-project). Used for directory matching in Claude Code and Cursor, and as the CWD filter for Codex sessions.<days> — scan window in days (e.g., 7). Session files older than this are skipped.<platform> (optional) — one of claude, codex, cursor. Omit to search all three.--keyword K1[,K2,...] (optional) — filter to sessions whose full file content matches at least one of the comma-separated keywords (case-insensitive substring). Each emitted session line gains match_count and keyword_matches ({K: N, ...}) fields, and the _meta line gains files_matched. Use this instead of rolling per-file grep -l calls when ranking many sessions by topical relevance.Run the discovery-plus-metadata pipeline from the skill's own scripts/ directory:
bash scripts/discover-sessions.sh <repo> <days> [--platform <platform>] \
| tr '\n' '\0' \
| xargs -0 python3 scripts/extract-metadata.py --cwd-filter <repo>
To filter by keyword, append --keyword K1[,K2,...] to the extract-metadata.py invocation. Keyword scanning reads the full file (not just the head metadata window), so it costs more than a metadata-only run — use it when you need to rank candidates by topic across many sessions, not as a default.
Return the raw stdout verbatim — one JSON object per session, then a final _meta line. Callers parse the JSONL directly, so do not paraphrase, reformat, or summarize.
If discovery finds no files, the pipeline still emits a clean _meta line (files_processed: 0). Return that as-is.
Each session line is a JSON object. Common fields across platforms:
platform — claude, codex, or cursorfile — absolute path to the session JSONLsize — file size in bytests — session start timestamp (ISO 8601)session — session identifierPlatform-specific fields:
branch (git branch) and last_ts (last message timestamp).cwd (working directory), source, cli_version, model, last_ts.ts is derived from file mtime and session from the containing directory name.The final _meta line has files_processed, parse_errors, and optionally filtered_by_cwd (count of Codex sessions dropped by the CWD filter) and files_matched (count of sessions retained by the keyword filter, present only when --keyword was set).
When --keyword is set, each session line additionally carries:
match_count — total occurrences across all keywordskeyword_matches — per-keyword counts, e.g., {"middleware": 4, "auth": 12}Sessions with match_count: 0 are excluded from output.
If the discovery script errors (e.g., unreadable home directory, permission failure), let the error surface to the caller. Do not substitute git log, file listings, or other sources — this skill's contract is session metadata, nothing else.
If _meta reports parse_errors > 0, return the JSONL as-is. The caller decides how to handle partial data.
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Ask which skill or flow fits your situation. A router over the skills in this repo.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
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