Recall knowledge

Skill LevNas/ccmemo/skills/recall-knowledge

Install

npx -y skills add LevNas/ccmemo --skill recall-knowledge

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What its author says it does

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Recall knowledge base entries by meaning, not just keywords — hybrid search (lexical ripgrep + local vector embeddings + see-link graph) over `.claude/knowledge/entries/`. Use when looking for prior knowledge, decisions, pitfalls, or context that may be worded differently from the query (e.g. a Japanese query vs English identifiers, or synonyms the entry does not literally contain). Falls back to ripgrep-only when the vector index or its dependencies are absent. On-demand only — it is NOT wired into the per-prompt hook (that stays ripgrep for instant, model-free injection).

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.8 KB, as published. Nobody here has run it

Recall Knowledge

Goal

Surface the most relevant knowledge entries for a query by meaning — bridging synonyms and cross-language wording (e.g. Japanese ↔ English identifiers) that literal keyword search misses.

When to Use

Searching the knowledge base for prior art, decisions, pitfalls, or related context
The query may be worded differently than the entries (synonyms, JA query vs EN identifiers)
Before starting work on a topic, to pull related accumulated knowledge
NOT for per-prompt automatic injection — that stays ripgrep via the existing userpromptsubmit_knowledge_search.sh hook (instant, no model load)

Structure First for Multi-Hop Questions

When the recall looks like it needs several hops — tracing how a decision evolved, asking how two topics connect, or mapping everything around an entry — do NOT chain search → read → follow links → read again. Query the link graph first (kb_graph.py neighborhood / path), pick the endpoints from the structure (IDs + titles only), and Read just those entries. Details in the procedure file.

Execution (run directly — do NOT delegate to a subagent)

IMPORTANT: hybrid search executes code (uv run a Python script). Subagents run in a sandbox that blocks code execution, networking, and out-of-cwd writes, so this skill runs from the MAIN agent's Bash — do NOT spawn an Agent for the search itself.

Read the procedure file at: {plugin_root}/skills/recall-knowledge/procedure.md
Follow it: resolve paths, decide hybrid vs ripgrep-fallback, run the search, present the ranked results, and Read the top entries when their content is needed for the answer.

Paths:

Knowledge base: {project_root}/.claude/knowledge/
Search script: {plugin_root}/scripts/kb_search.py
Index builder: {plugin_root}/scripts/kb_index.py (only to advise building the index)
Graph CLI: {plugin_root}/scripts/kb_graph.py (pure stdlib — needs neither uv nor the index)

IMPORTANT: The procedure / script paths use the plugin's base directory, NOT the project directory. Read the "Base directory for this skill" line from the skill loading message to determine {plugin_root}.