review-knowledge

A knowledge-base review tool for finding outdated notes, disconnected entries, missing links, and topics that need summaries. It supports periodic review so stored knowledge stays useful.

In plain words
What is it for?
Use it to check the health of a knowledge base, review one topic, or find entries to fix after a project milestone.
Why use it?
It helps reveal notes that have gone stale or are hard to connect, instead of leaving gaps in a growing collection of information.

Skill for Claude CodeCodex

Part of the ccmemo plugin — 4 skills, 5 hooks shipped together

Install

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.

agentmods
npx agentmods add skills/levnas/ccmemo/review-knowledge
Any agent
npx skills add LevNas/ccmemo --skill review-knowledge
Clone the repo
git clone --depth 1 https://github.com/LevNas/ccmemo

Made for: Claude Code, Codex.

Or install ccmemo, the plugin that ships this one along with the rest of its 4 skills, 5 hooks.

Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 821 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00051 $0.00821
Opus 5 $0.00026 $0.00411
Sonnet 5 $0.00010 $0.00164
Haiku 4.5 $0.00005 $0.00082

Measured 2d ago against content hash 661e4ea3e266, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

review-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.

skills/review-knowledge/SKILL.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Review Knowledge

Goal

Maintain knowledge base health and surface entries for review, helping the user internalize accumulated knowledge through periodic reflection.

When to Use

  • User explicitly requests a knowledge review (e.g., "review knowledge", "check knowledge base")
  • At the end of a significant work phase or project milestone
  • When the user wants to understand the current state of knowledge on a topic
  • Periodically (e.g., weekly/monthly) to keep the knowledge base healthy

Execution

Delegate the review work to a Sonnet subagent to minimize main context consumption.

  1. Determine the review mode from user input:
    • No argument or "health" → health
    • topic:<keyword>topic with the specified keyword
    • fixfix
  2. Determine the knowledge base path (default: .claude/knowledge/entries/)
  3. Precompute deterministic graph facts from the MAIN agent's Bash (subagents run in a sandbox that blocks code execution, so these must run here):
python3 "{plugin_root}/scripts/kb_graph.py" --root .claude/knowledge/entries stats
python3 "{plugin_root}/scripts/kb_graph.py" --root .claude/knowledge/entries lint --json

Notes: pure stdlib, ~1s, no index needed. lint exiting 1 just means it found findings — capture stdout either way. If python3 is unavailable or the commands fail, set both outputs to (unavailable) and continue; the subagent then derives everything by reading files as before. 4. Spawn a subagent with the following configuration:

Agent(
  subagent_type: "general-purpose",
  model: "sonnet",
  description: "review knowledge base",
  prompt: |
    You are a knowledge base review agent. Read the procedure file and follow it precisely.

    ## Input
    ### mode
    {mode}

    ### topic_keyword (only for topic mode)
    {topic_keyword}

    ### current_date
    {current_date}

    ### graph_stats (deterministic, precomputed by kb_graph.py)
    {graph_stats}

    ### graph_lint (deterministic, precomputed by kb_graph.py)
    {graph_lint}

    ## Instructions
    1. Read the procedure file at: {plugin_root}/skills/review-knowledge/procedure.md
    2. Follow the procedure step by step for the specified mode
    3. The knowledge base is at: {project_root}/.claude/knowledge/
    4. Return the full review report in the output format specified in the procedure
)

Read the full file on GitHub · 88 lines

Files

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.

Changes

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.

  1. 2d ago First seen · 88 lines · 51 tokens per session scan A 661e4ea3e266

Subscribe to this mod's changes

review-knowledge is a skill published in the GitHub repository LevNas/ccmemo (3 stars, last pushed 17d ago), licensed MIT. It adds 51 tokens to every session and 821 once invoked, about $0.0003 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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