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glebisglebis/claude-skills/session-finder/SKILL.md

session-finder

Skill410 starsChanged 5 months agoMITView on GitHub

Index and search Claude Code sessions using semantic embeddings (Gemini). Find past sessions by topic, relaunch the best match. Triggers on "find session", "which session did I", "relaunch the session where", "session about X".

3 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Follow-up user messages (condensed)

    line 37
  2. Distinctive:

    When user asks to find a session:

    line 47
  3. Very specific:

    Run search with their query

    line 48

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Session Finder

Semantic search across Claude Code sessions using Gemini embeddings.

Commands

Index sessions

python3 ~/.claude/skills/session-finder/scripts/session_finder.py index [--max-age-days 90]

Search

python3 ~/.claude/skills/session-finder/scripts/session_finder.py search "query" [--top 5]

Open best match directly

python3 ~/.claude/skills/session-finder/scripts/session_finder.py open "query"

Stats

python3 ~/.claude/skills/session-finder/scripts/session_finder.py stats

How it works

  1. Document extraction — deterministic, no LLM. Each session JSONL is parsed into a structured document:
    • away_summary events (pre-existing Claude recaps) if available
    • First user message (task description)
    • Follow-up user messages (condensed)
    • First assistant response
    • Tools used
    • Project name
  2. Embedding — documents are embedded with gemini-embedding-exp-03-07 via llm CLI
  3. Storage — SQLite at ~/.claude/session-finder.db
  4. Search — query is embedded, cosine similarity ranks all sessions, top match is the default to open

Workflow

When user asks to find a session:

  1. Run search with their query
  2. Present results with confidence scores
  3. Offer to resume the top match via claude --resume <id>

Download now (ZIP)

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