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-087git 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-087)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-087"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-087/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/legendtkl/agentic-skill-router/skill-087"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-087.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00025 | $0.00451 |
| Opus 5 | $0.00013 | $0.00226 |
| Sonnet 5 | $0.00005 | $0.00090 |
| Haiku 4.5 | $0.00003 | $0.00045 |
Grade A, and why
skill-087 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.
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 Data Query System Skill (Comprehensive)
This skill enables data querying across various enterprise sources, including databases, documents, and communication logs, facilitating data-driven decision-making.
It is designed to support a wide array of queries, from simple lookups to complex analytical requests across diverse data types.
When to Invoke This Skill
Invoke when ANY of the following is true:
- The user requires data from multiple sources to inform a decision.
- The task involves combining structured and unstructured data for insights.
- There is a need for generating reports or analytics based on various datasets.
Why Use This Skill?
Without this skill: users may struggle with disparate data sources, leading to incomplete analysis and poor decision-making.
With this skill: a subagent:
- fetches relevant data quickly from different systems
- processes and formats data for reporting purposes
- supports a variety of query types, enhancing flexibility
Typical data retrieval efficiency increase: 50–80%.
Invocation
Use this format:
Task(subagent_type="enterprise-data-query-system", prompt="""
Query: <describe your data query here>
Output requirements:
- Return the relevant data extracted from the specified sources.
- Include any necessary context or metadata.
Constraints:
- Ensure that data is recent and applicable to the query.
- Avoid irrelevant data that does not pertain to the query.
""")
Core Procedure (Must Follow)
Step 0 — Parse user query
- Extract:
- primary goal of the query (e.g., data retrieval, report generation)
- specific data sources mentioned (e.g., databases, documents)
Step 1 — Identify relevant data sources
- Determine which systems contain the necessary data for the query.
Step 2 — Execute data retrieval
- Query the identified sources and collate results effectively.
Step 3 — Structure results
- Format data into a clear and actionable structure for user interpretation.
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 · 66 lines · 25 tokens per session scan A 4498839fb687
skill-087 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 25 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 2026-09-03.
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