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-084git 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-084)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-084"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-084/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-084"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-084.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 | $0.00061 | $0.01755 |
| Opus 5 | $0.00030 | $0.00877 |
| Sonnet 5 | $0.00012 | $0.00351 |
| Haiku 4.5 | $0.00006 | $0.00176 |
Grade A, and why
skill-084 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.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompting Pattern Library
Version 1.0 | October 2025 | Tested with Claude 3.5/4, GPT-4/4o, Gemini 1.5 Pro
Navigation
📖 Full Documentation: See README.md for complete navigation, use case index, and version notes.
Quick access by need:
- Learning prompting: Start here, then read Prompt Patterns
- Debugging prompts: Jump to Failure Modes
- Building agents: See Orchestration Patterns
- Model optimization: Review Model Quirks
Overview
This skill provides a comprehensive library of prompting patterns, anti-patterns, and model-specific guidance for effective LLM interactions. Use this when creating educational content about prompting, analyzing prompt quality, or explaining prompting techniques to technical and non-technical audiences.
What's included:
- 25+ proven prompting patterns with "why it works" analysis
- Common failure modes with diagnosis and fixes
- Model-specific guidance (Claude, GPT-4, Gemini)
- Advanced orchestration patterns for agent systems
- Cross-references throughout for deep-dive learning
Quick Reference: Common Prompting Patterns
Structural Patterns
Role Prompting: Assign a specific role or persona to frame the response Chain-of-Thought (CoT): Request step-by-step reasoning before final answer Few-Shot Learning: Provide examples of desired input-output pairs Zero-Shot with Instructions: Detailed task description without examples Tree of Thoughts: Explore multiple reasoning paths before choosing best
Output Control Patterns
Structured Output: Request specific formats (JSON, XML, tables, lists) Delimiters: Use clear separators for inputs, examples, and instructions Length Control: Specify desired output length explicitly Style Constraints: Define tone, formality, audience level
Reasoning Enhancement Patterns
Self-Consistency: Generate multiple solutions and select most common Reflection: Ask model to critique its own output Decomposition: Break complex tasks into smaller sub-tasks Analogical Reasoning: Request analogies or comparisons
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 · 189 lines · 61 tokens per session scan A 4d362f6d67eb
skill-084 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 61 tokens to every session and 1,755 once invoked, about $0.0003 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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