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 skills add LevNas/ccmemo --skill recall-knowledgegit clone --depth 1 https://github.com/LevNas/ccmemoWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/levnas/ccmemo/recall-knowledge)<a href="https://agentmods.dev/skills/levnas/ccmemo/recall-knowledge"><img src="https://agentmods.dev/badge/skills/levnas/ccmemo/recall-knowledge/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/levnas/ccmemo/recall-knowledge"><img src="https://agentmods.dev/badge/skills/levnas/ccmemo/recall-knowledge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00126 | $0.00709 |
| Opus 5.5 | $0.00050 | $0.00284 |
| Sonnet 5.5 | $0.00025 | $0.00142 |
| Haiku 4.5 | $0.00013 | $0.00071 |
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 25d 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 — 59 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)
Summary First, Then One Entry
Run the search with --summary: each hit shows its title, its description
(the trigger condition — when to open it) and its typed links with the labels
their authors wrote. Choose the single entry to Read from that; do not open
several candidates to compare them. Details in the procedure file.
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)
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.
- 25d ago Changed · +6 lines 28f46c04316b
- 1mo ago First seen · 53 lines · 126 tokens per session scan A b0d7b6de356d
recall-knowledge is a skill published in the GitHub repository LevNas/ccmemo (4 stars, last pushed 11d ago), licensed MIT. It adds 126 tokens to every session and 709 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
learn-from-fix
Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.
assigns-audit
Inspect LiveView socket assigns for memory bloat — missing temporaryassigns, unused assigns, unbounded lists needing streams, memory estimates. Use when LiveView memory grows or you need to add temporaryassigns.
recall
Recall prior work from past sessions — how a bug was fixed, what was decided, where a pattern lives. Use when asked 'have we done this before' or 'how did I fix X' in Elixir/Phoenix work. ccrider MCP when available, else git + solution docs.
compound-docs
Searchable Elixir/Phoenix/Ecto solution documentation system with YAML frontmatter. Builds institutional knowledge from solved problems. Use when consulting past solutions before investigating new issues.
handoff
Create a structured session handoff when context is running low or work is pausing. Deep context mining, self-validation, multi-file splitting. Captures everything the next session needs.
import
Import a self-contained NeatContext context bundle shared by another person, or reconcile a newer copy of a context already on this machine, leaving the source bundle unchanged. Use only when the user explicitly invokes this skill or asks to import a NeatContext bundle.