SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill enterprise-artifact-searchgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/enterprise-artifact-search)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/enterprise-artifact-search"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/enterprise-artifact-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/enterprise-artifact-search"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/enterprise-artifact-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00047 | $0.02206 |
| Opus 5 | $0.00023 | $0.01103 |
| Sonnet 5 | $0.00009 | $0.00441 |
| Haiku 4.5 | $0.00005 | $0.00221 |
Grade A, and why
enterprise-artifact-search 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- enterprise-artifact-search — 100% identical, 0 lines differ
- skill-049 — 98% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enterprise Artifact Search Skill (Robust)
This skill delegates multi-hop artifact retrieval + structured entity extraction to a lightweight subagent, keeping the main agent’s context lean.
It is designed for datasets where a workspace contains many interlinked artifacts (documents, chat logs, meeting transcripts, PRs, URLs) plus reference metadata (employee/customer directories).
This version adds two critical upgrades:
- Product grounding & anti-distractor filtering (prevents mixing CoFoAIX/other products when asked about CoachForce).
- Key reviewer extraction rules (prevents “meeting participants == reviewers” mistake; prefers explicit reviewers, then evidence-based contributors).
When to Invoke This Skill
Invoke when ANY of the following is true:
- The question requires multi-hop evidence gathering (artifact → references → other artifacts).
- The answer must be retrieved from artifacts (IDs/names/dates/roles), not inferred.
- Evidence is scattered across multiple artifact types (docs + slack + meetings + PRs + URLs).
- You need precise pointers (doc_id/message_id/meeting_id/pr_id) to justify outputs.
- You must keep context lean and avoid loading large files into context.
Why Use This Skill?
Without this skill: you manually grep many files, risk missing cross-links, and often accept the first “looks right” report (common failure: wrong product).
With this skill: a subagent:
- locates candidate artifacts fast
- follows references across channels/meetings/docs/PRs
- extracts structured entities (employee IDs, doc IDs)
- verifies product scope to reject distractors
- returns a compact evidence map with artifact pointers
Typical context savings: 70–95%.
Invocation
Use this format:
Task(subagent_type="enterprise-artifact-search", prompt="""
Dataset root: /root/DATA
Question: <paste the question verbatim>
Output requirements:
- Return JSON-ready extracted entities (employee IDs, doc IDs, etc.).
- Provide evidence pointers: artifact_id(s) + short supporting snippets.
Constraints:
- Avoid oracle/label fields (ground_truth, gold answers).
- Prefer primary artifacts (docs/chat/meetings/PRs/URLs) over metadata-only shortcuts.
- MUST enforce product grounding: only accept artifacts proven to be about the target product.
""")
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 · 252 lines · 47 tokens per session scan A 00e31df6ee3a
enterprise-artifact-search is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 2,206 once invoked, about $0.0002 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 2026-09-03.
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