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 commands/briannaworkman/commonplace/querygit clone --depth 1 https://github.com/briannaworkman/commonplaceWrote 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/commands/briannaworkman/commonplace/query)<a href="https://agentmods.dev/commands/briannaworkman/commonplace/query"><img src="https://agentmods.dev/badge/commands/briannaworkman/commonplace/query.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 | $0.00012 | $0.00305 |
| Opus 5 | $0.00006 | $0.00152 |
| Sonnet 5 | $0.00002 | $0.00061 |
| Haiku 4.5 | $0.00001 | $0.00030 |
Grade A, and why
query 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 4d 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.
What it actually says
Answer this question using only the knowledge base:
$ARGUMENTS
1. Walk the graph
Read wiki/INDEX.md and pick the articles that look relevant. Read them. Follow their
[[wikilinks]] outward to neighbouring articles as the question requires — the answer often
sits in an article the index summary didn't obviously point at.
Keep track of the order you read things in. That path is part of the output.
2. Answer only from what the wiki says
Ground every claim in an article you actually read.
If the wiki doesn't cover the question, say so plainly and name the source that would close the gap. If it covers the question only partly, answer the part it covers and mark the boundary. Never fill a gap from general knowledge without labelling it as outside the wiki — a system that admits what it doesn't know is the only kind you can trust when it says it does.
3. Save it
Write outputs/YYYY-MM-DD-<slug>.md containing:
- the question
- the answer
- the path taken through the graph — which articles you read, in order
- citations — the articles, and the
raw/sources underneath them
4. Report
Give the answer inline and link the saved file.
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.
- 4d ago First seen · 40 lines · 12 tokens per session scan A f29e995f89ea
query is a command published in the GitHub repository briannaworkman/commonplace (5 stars, last pushed 23d ago), licensed MIT. It adds 12 tokens to every session and 305 once invoked, about $0.0001 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.
Other commands, from other repositories
okf
Command "okf" from kimsanguine/llm-brain, covering 인자 파싱, 기본 private export, 🔴 share-ready gate (외부 공유 전 one-way door), legacy step 1: private dry-run 검토 and legacy step 2: private export.
query
Command "query" from kimsanguine/llm-brain, covering step 1: index.md 검색, step 2: persisted claim ledger 로드, step 3: wiki 기반 답변 and step 4: 연결 요약 출력.
curate
Command "curate" from kimsanguine/llm-brain, covering 인자 파싱, step 1: curate 스크립트 실행, step 2: distill 실행 (--distill 또는 --all 일 때), step 3: reweave 실행 (--reweave 일 때) and step 3.5: 모순 후보 화해 (wiki/contradictionqueue.md 존재 시).
doctor
Command "doctor" from kimsanguine/llm-brain, covering 인자, guided 프로필, 실행, 점검 항목 and 결과 해석.
ingest
Command "ingest" from kimsanguine/llm-brain, covering 인자 파싱, step 1: 스크립트 실행, url 수집, 파일 추가 and 텍스트 노트.
express
Command "express" from kimsanguine/llm-brain, covering 인자 파싱, step 1: express 스크립트 실행, step 2: 콘텐츠 합성 and step 3: 파일 경로 안내.