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/legendtkl/agentic-skill-router/skill-013npx skills add legendtkl/agentic-skill-router --skill skill-013git 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-013)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-013"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-013.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 | $0.00022 | $0.00451 |
| Opus 5 | $0.00011 | $0.00226 |
| Sonnet 5 | $0.00004 | $0.00090 |
| Haiku 4.5 | $0.00002 | $0.00045 |
Grade A, and why
skill-013 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 6d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enterprise Knowledge Discovery Skill (Insightful)
This skill focuses on automating the discovery of insights from extensive datasets across the enterprise, enabling informed strategic planning and decision-making.
It is designed for organizations with vast amounts of data that need to be analyzed to uncover valuable insights and trends.
When to Invoke This Skill
Invoke when ANY of the following is true:
- The user is looking to uncover hidden patterns in historical data for strategic decisions.
- Insights are needed from various types of data sources, such as documents, databases, and communication logs.
- There is a requirement to generate reports that summarize findings from the analysis.
Why Use This Skill?
Without this skill: manual analysis of data can lead to missed opportunities and inefficient strategies due to the sheer volume of information.
With this skill: a subagent:
- systematically analyzes data from multiple sources
- identifies trends, correlations, and insights
- generates comprehensive reports for stakeholders
Typical insight generation improvement: 25–50%.
Invocation
Use this format:
Task(subagent_type="enterprise-knowledge-discovery", prompt="""
Data sources: <list of data sources>
Objective: <define the objective for the discovery process>
Output requirements:
- Return key insights and patterns identified.
- Provide context and implications of these findings.
Constraints:
- Focus on actionable insights that relate to strategic planning.
- Avoid irrelevant data or findings that do not contribute to the objective.
""")
Core Procedure (Must Follow)
Step 0 — Define the discovery objective
- Extract:
- the core objective for the discovery process (e.g., market trends analysis)
Step 1 — Collect data
- Gather data from all specified sources relevant to the objective.
Step 2 — Analyze for insights
- Use analytical techniques to identify significant patterns and insights.
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.
- 6d ago First seen · 66 lines · 22 tokens per session scan A 43da18f239d5
skill-013 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 451 once invoked, about $0.0001 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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