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/dankaro-projects/shelfmark/shelfmark-find-contextnpx skills add Dankaro-projects/shelfmark --skill shelfmark-find-contextgit clone --depth 1 https://github.com/Dankaro-projects/shelfmarkWrote 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/dankaro-projects/shelfmark/shelfmark-find-context)<a href="https://agentmods.dev/skills/dankaro-projects/shelfmark/shelfmark-find-context"><img src="https://agentmods.dev/badge/skills/dankaro-projects/shelfmark/shelfmark-find-context.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 5.1 | $0.00057 | $0.00478 |
| Opus 5 | $0.00028 | $0.00239 |
| Sonnet 5 | $0.00011 | $0.00096 |
| Haiku 4.5 | $0.00006 | $0.00048 |
Grade A, and why
shelfmark-find-context 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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Find Shelfmark Context
Find the smallest useful set of documents while respecting catalogue freshness and sharing boundaries.
Search Workflow
- Call
corpus_stats()first. Note freshness, available roots, document types, context types, and the rights split before forming a query. - If the user names a folder or only describes a broad area, call
browse_folder()to learn the catalogue's actual structure and labels. - Call
search_docs()with filename-style or title-style terms. Shelfmark indexes paths, filenames, authors, company metadata, OOXML titles, and slide titles, not document body text. - Add only filters supported by the corpus summary or prior results. Use
root,client,doc_type,context_type, authored-year bounds, orpath_containsto narrow a large result set. - Call
get_file()on the strongest candidates to confirm rights, confidentiality, authorship, dates, slide titles, duplicate copies, and on-disk status. - Return a short ranked list with exact catalogue paths, why each result fits, and any governance or freshness caveat.
Apply Governance
- Set
shareable_only=truewhenever paths or derived material may leave the user's local environment. This excludes confidential and unreviewedUNKNOWNmaterial. - Set
own_only=trueonly when the user specifically needs material classified as authored by them. - Never infer or probe
RESTRICTEDmaterial. Shelfmark deliberately makes restricted paths indistinguishable from absent paths. - Do not describe metadata matches as proof of document-body content. Ask to open a selected file with a separately authorized file-reading tool when its contents are needed.
Recover From Weak Results
- Remove unsupported filters before broadening the query.
- Try filename, project, organization, author, or slide-title vocabulary rather than sentence fragments from a document.
- Use
browse_folder()when terminology is uncertain. - State that a true no-result search is recorded locally by Shelfmark as product feedback; it is not sent anywhere.
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.
- 5d ago First seen · 32 lines · 57 tokens per session scan A db2746cec42f
shelfmark-find-context is a skill published in the GitHub repository Dankaro-projects/shelfmark (0 stars, last pushed 21d ago), licensed MIT. It adds 57 tokens to every session and 478 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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