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-061Wrote 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-061)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-061"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-061/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-061"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-061.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 | $0.00027 | $0.00536 |
| Opus 5 | $0.00014 | $0.00268 |
| Sonnet 5 | $0.00005 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00054 |
Grade A, and why
skill-061 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Data Collection
Overview
Efficiently collect and manage research data using this skill. It focuses on the systematic gathering of qualitative and quantitative data from various sources, ensuring that researchers can easily organize and analyze their findings. This skill is essential for improving research workflows and maintaining data integrity.
When to Use This Skill
Use this skill when:
- You need to design a data collection strategy for surveys or experiments.
- Gathering data from various sources, including online surveys, interviews, and observations.
- Tracking and managing raw data from multiple research projects.
- Creating templates for structured data collection.
- Collaborating with participants to ensure data accuracy.
- Organizing collected data into scalable formats for analysis.
Data Collection Strategies
Surveys and Questionnaires
Using online tools like Google Forms or SurveyMonkey, you can create surveys to collect data from participants. This approach helps gather quantitative and qualitative insights efficiently.
Example of creating a survey template:
python scripts/create_survey.py --title "Research Data Collection Survey" --questions "1. What is your age?" "2. How satisfied are you with our service?"
Interviews and Focus Groups
Conduct interviews to gather qualitative data. This can be done through direct interaction or via virtual platforms like Zoom. Always ensure you record the sessions (with permission) for accurate data capture.
Recording interviews:
python scripts/record_interview.py --output recordings/interview_001.wav
Observational Data
Collect observational data during experiments or natural settings. Document findings in structured formats for future analysis.
Example of logging observational data:
python scripts/log_observation.py --entry "Participant A showed signs of fatigue during the task."
Data Organization Techniques
Proper organization of data is crucial for analysis. Use file structures and naming conventions to maintain clarity in your data storage.
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 · 73 lines · 27 tokens per session scan A 68a16fb5101e
skill-061 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 536 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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