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 legendtkl/agentic-skill-router --skill skill-049git clone --depth 1 https://github.com/legendtkl/agentic-skill-routerWrote 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/legendtkl/agentic-skill-router/skill-049)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-049"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-049.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 | $0.00046 | $0.02205 |
| Opus 5 | $0.00023 | $0.01103 |
| Sonnet 5 | $0.00009 | $0.00441 |
| Haiku 4.5 | $0.00005 | $0.00220 |
Grade A, and why
skill-049 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 7d 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.
This is a copy
98% identical to enterprise-artifact-search — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 251 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.
- 7d ago First seen · 251 lines · 46 tokens per session scan A c6e1da777e8e
skill-049 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 2,205 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to enterprise-artifact-search, differing in 3 lines, and is treated as a copy.
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