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/catchen/knowledge-wiki-template/knowledge-wiki-mergenpx skills add CatChen/knowledge-wiki-template --skill knowledge-wiki-mergegit clone --depth 1 https://github.com/CatChen/knowledge-wiki-templateWrote 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/catchen/knowledge-wiki-template/knowledge-wiki-merge)<a href="https://agentmods.dev/skills/catchen/knowledge-wiki-template/knowledge-wiki-merge"><img src="https://agentmods.dev/badge/skills/catchen/knowledge-wiki-template/knowledge-wiki-merge.svg" alt="Measured on agentmods" 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 | $0.00051 | $0.02377 |
| Opus 5 | $0.00026 | $0.01189 |
| Sonnet 5 | $0.00010 | $0.00475 |
| Haiku 4.5 | $0.00005 | $0.00238 |
Grade A, and why
knowledge-wiki-merge 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 5d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Wiki Merge
Detect duplicate concept pairs and interactively merge them. Presents one pair at a time — you decide whether to merge, dismiss (never show again), or skip. Merging is destructive and irreversible, so each decision is confirmed before execution.
Steps
1. Establish the working directory
The knowledge base root is the Git repository root. Run git rev-parse --show-toplevel and store the result as KNOWLEDGE_PATH.
Use KNOWLEDGE_PATH for all subsequent steps.
2. Find and filter duplicate candidates
Structural candidates are pairs detected by shared source material — concept files that share two or more ## Sources entries. Run:
node {KNOWLEDGE_PATH}/scripts/wiki/candidates.mjs find-shared-source-concepts
This script automatically filters out previously dismissed pairs from Wiki/.state.json. Output is { "candidates": [...] } sorted by shared source count descending. Tag each as detection: "structural". Do not pipe through head or any other truncating command — every candidate must be evaluated.
LLM pre-filter (structural only): Before proceeding, review the structural candidates and eliminate any pair that is clearly about different topics despite sharing sources — pairs where the shared sources happen to cover two unrelated ideas (e.g. applescript and email-marketing appearing in the same AppleScript email tutorial). For each eliminated pair, call:
node {KNOWLEDGE_PATH}/scripts/wiki/wiki-state.mjs dismiss-pair knowledge-wiki-merge {pathA} {pathB}
where pathA and pathB are the full relative paths (e.g. Wiki/Concepts/applescript.md). Be conservative: only dismiss pairs you are confident are unrelated. A wrongly auto-dismissed pair is hidden from all future runs and requires manually editing Wiki/.state.json to recover.
Semantic candidates are pairs identified by conceptual overlap — synonyms, one being a strict subset of the other, or articles that would naturally be merged — without necessarily sharing sources. Perform this pass by running:
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.
- 5d ago First seen · 194 lines · 51 tokens per session scan A ba1507c56e2c
knowledge-wiki-merge is a skill published in the GitHub repository CatChen/knowledge-wiki-template (106 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 2,377 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 2026-08-30.
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