Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/legendtkl/agentic-skill-routernpx agentmods add skills/legendtkl/agentic-skill-router/skill-089Wrote 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-089)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-089"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-089/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-089"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-089.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 | $0.00067 | $0.00752 |
| Opus 5 | $0.00034 | $0.00376 |
| Sonnet 5 | $0.00013 | $0.00150 |
| Haiku 4.5 | $0.00007 | $0.00075 |
Grade A, and why
skill-089 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Initializer
Set up a project for long-running Claude agent implementation with environment scripts, feature tracking, and progress logging.
When to Use
- Starting a new implementation project
- Setting up an existing codebase for Claude-assisted development
- After spec2impl generates the implementation harness
- Beginning a feature implementation sprint
Input Modes
The Initializer works with three types of input:
1. Specification Documents
Input: docs/ directory with Markdown specs
Output: Feature list extracted from specifications
2. Existing Codebase
Input: Project with package.json, src/, etc.
Output: Feature list inferred from code structure
3. High-Level Prompt
Input: User description (e.g., "E-commerce site with user auth")
Output: Feature list generated from requirements
Workflow
Step 1: Analyze Input
Determine input mode and gather information:
// Check for specification docs
const hasSpecs = await Glob("docs/**/*.md")
// Check for existing project
const hasProject = await Glob("{package.json,requirements.txt,go.mod,Cargo.toml}")
// Determine tech stack
const techStack = detectTechStack(hasProject)
Step 2: Generate Feature List
Create docs/features.json with comprehensive feature tracking:
// See references/feature-list-format.md for schema
const features = {
project: { name: projectName, generatedAt: new Date() },
features: extractOrGenerateFeatures(input),
summary: { total: N, pending: N, ... }
}
Write("docs/features.json", JSON.stringify(features, null, 2))
Feature Granularity Guidelines:
- Small projects (< 10 features): Fine-grained, one feature per endpoint/component
- Medium projects (10-50 features): Group related functionality
- Large projects (50+ features): High-level feature groups, use subtasks for details
Step 3: Create Environment Files
Generate init.sh based on detected tech stack:
# Run the initialization script
python3 .claude/skills/spec2impl/initializer/scripts/init_project.py \
--tech-stack "${techStack}" \
--project-name "${projectName}"
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 · 124 lines · 67 tokens per session scan A 736c284a305c
skill-089 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 67 tokens to every session and 752 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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