tma1-ai/tma1/tma1-search

Claude Code and agent skill by tma1-ai/tma1

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Search past agent sessions on this project by keyword, and read back any session's conversation. Invoke when the answer lives in an earlier session — how something was fixed before, what was decided, what an agent tried last week — or when the user hands you a session id. Trigger phrases: \"what did we decide about\", \"how did we fix this before\", \"search my sessions\", \"find that session\", \"read session <id>\", \"之前是怎么解决的\", \"上次讨论过\", \"找一下以前的 session\", \"/tma1-search\".

Install

npx vskill@latest install tma1-ai/tma1/tma1-search

SKILL.md

TMA1 Session Search

TMA1 keeps every agent session on this machine — yours and other agents' — in a local database. This skill is the read path: find the session, then read it.

Two entry points

/tma1-search retry backoff # keyword search, this project, last 7 days
/tma1-search flaky test --agent codex # only Codex's sessions
/tma1-search migration --days 30 # widen the window
/tma1-search pipeline --all-projects # every project, not just this one
/tma1-search --session 3d8f1d0a # read one session directly

--session takes a full id or a prefix of 6+ characters — the abbreviated id in the <tma1-context> block works. Do not infer a session id from a bare token: commit hashes look the same, and cp: ids don't look like hex at all. Anything that isn't behind --session is search text.

Workflow

  1. Search — mcp__tma1__search_sessions with:
  • query: the keyword text (everything that isn't a flag)
  • agent_source: --agent value if given (codex /

copilot / claude / self); omit for every agent

  • since_min: --days N × 1440, default omitted (7 days)
  • project: "*" when --all-projects is given; otherwise omit
  1. Report the matching sessions with their snippets, so the user can

pick. One obvious hit and a clear question → go straight to step 3.

  1. Read — mcp__tma1__get_session_transcript with the session_id

from step 1. has_more: true means older messages exist; call again with the returned next_offset if the answer isn't in this page.

  1. Answer with quotes. The value here is the earlier session's actual

words, not your summary of them.

Reading the search response

  • sessions[] — session_id, agent_source, last_activity_ago,

hit_count, and snippets[] (each a window around a match).

  • match_mode — term is the indexed pass: whole words, case-sensitive.

substring means that pass found nothing, so the search widened to a case-folded substring scan; results may include noise, say so.

  • scope_truncated: true — this project has more sessions in the window

than the search scanned. Narrow with --agent or --days.

  • note — present only when nothing matched. Say so plainly; don't invent

a plausible past session.

Search behaviour worth knowing

The indexed pass matches whole words and is case-sensitive, so Cwd does not find peerCwdFilter and neither does peercwdfilter. Search for the identifier as it is written in the code. When that pass finds nothing the search widens to a case-folded substring scan and tells you so with match_mode: substring — that is the mode that rescues a mistyped case, at the cost of noisier hits.

Related

  • Browsing by agent rather than keyword → /tma1-peer.
  • Aggregates over the raw tables (cost by model, tool failure rates) →

the tma1 skill's exec_query.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

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Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
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  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
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