shelfmark-research-archive

shelfmark-research-archive is a skill for Codex from Dankaro-projects/shelfmark. It costs 59 tokens per session (489 once invoked), scanned A, original, MIT.

A research workflow for finding evidence in a local Shelfmark document and email archive. Shelfmark is the archive it searches; the workflow keeps dates, authors, rights, and confidentiality visible.

In plain words
What is it for?
Use it to build source maps, timelines, project histories, prior-work searches, or evidence-based briefings.
Why use it?
It helps prevent research briefs from hiding where information came from or confusing file details with document contents.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to build source maps, timelines, project histories, prior-work searches, or evidence-based briefings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dankaro-projects/shelfmark/shelfmark-research-archive
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.

Any agent
npx skills add Dankaro-projects/shelfmark --skill shelfmark-research-archive
Clone the repo
git clone --depth 1 https://github.com/Dankaro-projects/shelfmark

Made for: Codex.

Wrote 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.

agentmods badge for shelfmark-research-archive

README.md
[![agentmods](https://agentmods.dev/badge/skills/dankaro-projects/shelfmark/shelfmark-research-archive.svg)](https://agentmods.dev/skills/dankaro-projects/shelfmark/shelfmark-research-archive)
Your own site
<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>
Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 489 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00059 $0.00489
Opus 5 $0.00030 $0.00244
Sonnet 5 $0.00012 $0.00098
Haiku 4.5 $0.00006 $0.00049

Measured 8d ago against content hash 4f8460093c49, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

plugins/shelfmark/skills/shelfmark-research-archive/SKILL.md · 36 lines

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

  1. Call corpus_stats() and record freshness, corpus coverage, available facets, and whether an email corpus exists.
  2. Break the question into a few concrete entities, identifiers, date ranges, and likely folders. Search documents with filename-style or title-style terms.
  3. Use browse_folder() to understand a promising project's surrounding files instead of treating isolated search hits as the whole archive.
  4. Refine search_docs() with known facets and dates. Call get_file() on leading results to capture exact path, author, authored date, rights, confidentiality, titles, duplicate copies, and residency.
  5. When an email corpus is available and correspondence is relevant, call search_emails() without bodies first. Narrow by sender domain or year, then use include_body=true only for the small set needed as evidence.
  6. 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.
  7. 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=true for document searches when the brief or source list may leave the machine. Do not include UNKNOWN, 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:

  1. Answer or executive summary.
  2. Timeline or grouped findings when useful.
  3. Sources with exact provenance and governance status.
  4. Gaps, conflicts, and searches that produced no evidence.

Read the full file on GitHub · 36 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. 8d ago First seen · 36 lines · 59 tokens per session scan A 4f8460093c49

Subscribe to this mod's changes

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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