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 Dankaro-projects/shelfmark --skill shelfmark-research-archivegit 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-research-archive)<a href="https://agentmods.dev/skills/dankaro-projects/shelfmark/shelfmark-research-archive"><img src="https://agentmods.dev/badge/skills/dankaro-projects/shelfmark/shelfmark-research-archive.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 | $0.00059 | $0.00489 |
| Opus 5 | $0.00030 | $0.00244 |
| Sonnet 5 | $0.00012 | $0.00098 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
shelfmark-research-archive 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 8d 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research a Shelfmark Archive
Build an evidence trail before writing conclusions, and distinguish metadata evidence from content evidence.
Research Workflow
- Call
corpus_stats()and record freshness, corpus coverage, available facets, and whether an email corpus exists. - Break the question into a few concrete entities, identifiers, date ranges, and likely folders. Search documents with filename-style or title-style terms.
- Use
browse_folder()to understand a promising project's surrounding files instead of treating isolated search hits as the whole archive. - Refine
search_docs()with known facets and dates. Callget_file()on leading results to capture exact path, author, authored date, rights, confidentiality, titles, duplicate copies, and residency. - When an email corpus is available and correspondence is relevant, call
search_emails()without bodies first. Narrow by sender domain or year, then useinclude_body=trueonly for the small set needed as evidence. - If document-body evidence is required, ask to open selected paths with a separately authorized local file-reading tool. Shelfmark document search and
get_file()expose metadata, not document bodies. - Produce the requested output with a source list that preserves exact paths or email headers, dates, and governance labels.
Evidence Rules
- Treat document metadata, filenames, and slide titles as discovery evidence, not proof of claims contained in a file.
- Treat returned email excerpts as partial content and mark truncation when Shelfmark does.
- Label conclusions as confirmed, inferred, or unresolved. Tie confirmed claims to content actually read.
- Use
shareable_only=truefor document searches when the brief or source list may leave the machine. Do not includeUNKNOWN, confidential, or restricted material in an external-facing output. - Mention stale-index warnings and result truncation because both limit completeness.
Output Shape
Keep the result compact:
- Answer or executive summary.
- Timeline or grouped findings when useful.
- Sources with exact provenance and governance status.
- Gaps, conflicts, and searches that produced no evidence.
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.
- 8d ago First seen · 36 lines · 59 tokens per session scan A 4f8460093c49
shelfmark-research-archive is a skill published in the GitHub repository Dankaro-projects/shelfmark (0 stars, last pushed 24d ago), licensed MIT. It adds 59 tokens to every session and 489 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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