Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/levnas/ccmemo/recall-knowledgenpx skills add LevNas/ccmemo --skill recall-knowledgegit clone --depth 1 https://github.com/LevNas/ccmemoWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00126 | $0.00636 |
| Opus 5 | $0.00063 | $0.00318 |
| Sonnet 5 | $0.00025 | $0.00127 |
| Haiku 4.5 | $0.00013 | $0.00064 |
Grade A, and why
recall-knowledge scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.shhook (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}.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 53 lines · 126 tokens per session scan A b0d7b6de356d
recall-knowledge is a skill published in the GitHub repository LevNas/ccmemo (3 stars, last pushed 16d ago), licensed MIT. It adds 126 tokens to every session and 636 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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