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-056git 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-056)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-056"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-056.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 | $0.00020 | $0.00482 |
| Opus 5 | $0.00010 | $0.00241 |
| Sonnet 5 | $0.00004 | $0.00096 |
| Haiku 4.5 | $0.00002 | $0.00048 |
Grade A, and why
skill-056 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enterprise Recommendation Engine Skill (Smart)
This skill offers dynamic content and product recommendations tailored to user interactions across enterprise platforms, enhancing user engagement and satisfaction.
It is designed for environments where user behavior data is abundant, including interactions with documents, chats, and meetings.
When to Invoke This Skill
Invoke when ANY of the following is true:
- The user needs personalized recommendations based on their previous interactions.
- The recommendations should be based on real-time data, adapting to user preferences continuously.
- The task involves suggesting relevant documents, tools, or resources to enhance productivity.
Why Use This Skill?
Without this skill: you rely on static recommendation lists that don’t account for user behavior, leading to irrelevant suggestions.
With this skill: a subagent:
- analyzes user interaction data to find patterns
- provides tailored recommendations that evolve over time
- enhances user experience by connecting them with the most relevant enterprise resources
Typical engagement increase: 15–30%.
Invocation
Use this format:
Task(subagent_type="enterprise-recommendation-engine", prompt="""
User ID: <user_id>
Context: <user_context>
Output requirements:
- Return a list of recommended documents and tools.
- Provide reasoning or evidence for each recommendation.
Constraints:
- Ensure recommendations are relevant to the user’s recent activity.
- Avoid recommending items that the user has already interacted with.
""")
Core Procedure (Must Follow)
Step 0 — Analyze user behavior
- Extract:
- user ID (e.g., “12345”)
- recent interactions (documents, conversations, tools used)
If user ID is missing, infer cautiously from existing session data.
Step 1 — Gather contextual data
- Retrieve user context (role, department, recent projects) to refine recommendations.
Step 2 — Generate recommendations
- Utilize collaborative filtering and content-based techniques to produce a ranked list of recommendations based on gathered data.
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 · 69 lines · 20 tokens per session scan A a06ef40bb390
skill-056 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 20 tokens to every session and 482 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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