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-110git 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-110)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-110"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-110/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-110"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-110.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 | $0.00034 | $0.00693 |
| Opus 5 | $0.00017 | $0.00347 |
| Sonnet 5 | $0.00007 | $0.00139 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
skill-110 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requirements for Outputs
General Guidelines
- PDDL files should be syntactically correct and adhere to general PDDL standards.
- Outputs must be in a usable format, but specifics can vary.
- Validation processes should ensure that outputs meet the specified goals.
PDDL Planning Tools
1. Load PDDL Files
load-pddl-files(domain_path, problem_path)
Description:
Loads any PDDL domain and problem files, making them accessible for further processing and planning activities.
Parameters:
domain_path(str): Path to the PDDL domain file.problem_path(str): Path to the PDDL problem file.
Returns:
files_loaded: A boolean indicating whether the files were successfully loaded.
Example:
files_loaded = load_pddl_files("domain.pddl", "task01.pddl")
Notes:
- Basic loading functionality that should accommodate different PDDL versions.
2. Generate Plans
generate-plans(problem)
Description: Generates plans based on the loaded problem, utilizing various planning strategies.
Parameters:
problem: The PDDL problem instance.
Returns:
plans: A list of possible plans generated.
Example:
plans = generate_plans(problem)
Notes:
- Supports multiple planning algorithms without specifying which ones are used.
3. Save Plans and Outputs
save-plans(plans, output_path)
Description: Saves generated plans to a specified file format, allowing for flexible output options.
Parameters:
plans: A list of generated plans.output_path(str): Path where plans should be saved.
Example:
save_plans(plans, "output.plans")
Notes:
- Output format details are not strictly defined; it could vary widely.
4. Validate Plans
validate-plans(problem, plans)
Description: Validates the generated plans against the specified problems to ensure that they are feasible.
Parameters:
problem: The original PDDL problem instance.plans: The generated plans.
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 · 118 lines · 34 tokens per session scan A 0b11d82d7940
skill-110 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 693 once invoked, about $0.0002 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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