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-131git 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-131)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-131"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-131/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-131"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-131.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 | $0.00021 | $0.00715 |
| Opus 5 | $0.00010 | $0.00358 |
| Sonnet 5 | $0.00004 | $0.00143 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
skill-131 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requirements for Outputs
General Guidelines
- PDDL domain files must meet optimization standards.
- Problem files must adhere to the transformations specified.
- Outputs should clearly indicate optimization metrics.
Optimization Behavior
- Optimization processes should complete within a predefined time.
- If no optimization is possible, return an indication of failure.
PDDL Optimization Utilities
1. Load and Transform Domain
load-and-transform-domain(domain_path)
Description:
Loads a PDDL domain file and applies transformations to optimize the state representation.
Parameters:
domain_path(str): Path to the PDDL domain file.
Returns:
optimized_domain: A transformed domain for better efficiency.
Example:
optimized_domain = load_and_transform_domain("domain.pddl")
Notes:
- Utilizes
unified_planning.io.PDDLTransformer. - Throws an error if transformation fails.
2. Optimize Problem
optimize-problem(problem_path, optimized_domain)
Description: Optimizes the specified problem based on the optimized domain loaded previously.
Parameters:
problem_path(str): Path to the PDDL problem file.optimized_domain: The transformed domain.
Returns:
optimized_problem: An optimized problem object ready for planning.
Example:
optimized_problem = optimize_problem("task01.pddl", optimized_domain)
Notes:
- Uses
unified_planning.shortcuts.ProblemOptimizer. - Returns None if no optimization is possible.
3. Save Optimized Problem
save-optimized-problem(optimized_problem, output_path)
Description: Writes the optimized problem to disk in standard PDDL format.
Parameters:
optimized_problem: A PDDL problem that has been optimized.output_path(str): Output file path.
Example:
save_optimized_problem(optimized_problem, "optimized_task01.pddl")
Notes:
- Uses
unified_planning.io.PDDLWriterfor output. - Outputs a text file with optimized specifications.
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 · 124 lines · 21 tokens per session scan A 32ac11e52f84
skill-131 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 715 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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