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-111git 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-111)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-111"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-111/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-111"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-111.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 | $0.00031 | $0.00752 |
| Opus 5 | $0.00015 | $0.00376 |
| Sonnet 5 | $0.00006 | $0.00150 |
| Haiku 4.5 | $0.00003 | $0.00075 |
Grade A, and why
skill-111 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requirements for Outputs
General Guidelines
PDDL Files
- Domain files must define actions that manipulate spatial states.
- Problem files should specify start and goal states clearly.
- Trajectories must adhere to sequential action constraints.
Planner Behavior
- Trajectory generation must complete within specified time.
- If no trajectory exists, return an empty trajectory or failure flag.
PDDL Trajectory Planner
1. Load Domain and Problem
load-trajectory-problem(domain_path, problem_path)
Description:
Loads a PDDL domain and problem file specific to trajectory planning, creating a planning problem instance.
Parameters:
domain_path(str): Path to the PDDL domain file.problem_path(str): Path to the PDDL problem file.
Returns:
trajectory_problem: A trajectory planning problem object.
Example:
trajectory_problem = load_trajectory_problem("domain.pddl", "navigation_task.pddl")
Notes:
- Uses unified_planning.io.PDDLReader.
- Throws an error if parsing is unsuccessful.
2. Generate Trajectory
generate-trajectory(trajectory_problem)
Description: Generates a series of actions (trajectory) for the specified planning problem.
Parameters:
trajectory_problem: A trajectory planning problem instance.
Returns:
trajectory: A list of sequential actions representing the trajectory.
Example:
trajectory = generate_trajectory(trajectory_problem)
Notes:
- Utilizes
unified_planning.shortcuts.TrajectoryPlanner. - If no trajectory can be formed, it returns None.
3. Save Trajectory
save-trajectory(trajectory, output_path)
Description: Writes a trajectory to a file in a defined format suitable for future use.
Parameters:
trajectory: A list of actions representing the trajectory.output_path(str): Path where the trajectory will be saved.
Example:
save_trajectory(trajectory, "navigation_solution.trajectory")
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 · 126 lines · 31 tokens per session scan A 4044dfd72251
skill-111 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 752 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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