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 agentmods add skills/poorgramer-zack/copilot-cli-things/skill-creatornpx skills add Poorgramer-Zack/copilot-cli-things --skill skill-creatorgit clone --depth 1 https://github.com/Poorgramer-Zack/copilot-cli-thingsWrote 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/poorgramer-zack/copilot-cli-things/skill-creator)<a href="https://agentmods.dev/skills/poorgramer-zack/copilot-cli-things/skill-creator"><img src="https://agentmods.dev/badge/skills/poorgramer-zack/copilot-cli-things/skill-creator.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.06811 |
| Opus 5 | $0.00030 | $0.03406 |
| Sonnet 5 | $0.00012 | $0.01362 |
| Haiku 4.5 | $0.00006 | $0.00681 |
Grade A, and why
skill-creator 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 3d 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.
This is a copy
81% identical to skill-creator — 81 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 470 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Creator
Create, test, and iteratively improve skills.
The loop: draft skill → write test prompts → run with-skill and baseline → evaluate (qualitative via eval-viewer/generate_review.py + quantitative benchmarks) → rewrite based on feedback → repeat → expand test set.
Figure out where the user is in this process and help them progress. If they already have a draft, jump straight to eval/iterate. If they say "just vibe with me", skip the formal evals.
After the skill is done, run the description optimization script to improve triggering accuracy.
Cool? Cool.
Communicating with the user
Users range from non-technical to expert. Pay attention to context cues and adjust your language:
- "evaluation" and "benchmark" are borderline, but OK
- For "JSON" and "assertion", look for cues that the user knows these terms before using them without explanation
Briefly explain terms if in doubt.
Creating a skill
Capture Intent
Start by understanding the user's intent. The current conversation might already contain a workflow the user wants to capture (e.g., they say "turn this into a skill"). If so, extract answers from the conversation history first — the tools used, the sequence of steps, corrections the user made, input/output formats observed. The user may need to fill the gaps, and should confirm before proceeding to the next step.
- What should this skill enable Claude to do?
- When should this skill trigger? (what user phrases/contexts)
- What's the expected output format?
- Should we set up test cases to verify the skill works? Skills with objectively verifiable outputs (file transforms, data extraction, code generation, fixed workflow steps) benefit from test cases. Skills with subjective outputs (writing style, art) often don't need them. Suggest the appropriate default based on the skill type, but let the user decide.
Interview and Research
Proactively ask questions about edge cases, input/output formats, example files, success criteria, and dependencies. Wait to write test prompts until you've got this part ironed out.
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/analyzer.md 10 KB
- agents/comparator.md 7.1 KB
- agents/grader.md 8.8 KB
- assets/eval_review.html 6.9 KB
- eval-viewer/generate_review.py 16 KB runs code
- eval-viewer/viewer.html 44 KB
- LICENSE.txt 11 KB
- references/schemas.md 12 KB
- scripts/__init__.py 0 B runs code
- scripts/aggregate_benchmark.py 14 KB runs code
- scripts/generate_report.py 13 KB runs code
- scripts/improve_description.py 11 KB runs code
- scripts/package_skill.py 4.1 KB runs code
- scripts/quick_validate.py 3.9 KB runs code
- scripts/run_eval.py 11 KB runs code
- scripts/run_loop.py 13 KB runs code
- scripts/utils.py 1.6 KB runs code
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.
- 3d ago First seen · 470 lines · 61 tokens per session scan A 649601e86b84
skill-creator is a skill published in the GitHub repository Poorgramer-Zack/copilot-cli-things (2 stars, last pushed 5mo ago), licensed MIT. It adds 61 tokens to every session and 6,811 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 81% identical to skill-creator, differing in 81 lines, and is treated as a copy.
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Check the local environment for Spec Kit by running specify check (and specify version). USE FOR: verifying required tools are installed, diagnosing a broken spec-kit setup, reporting CLI version/feature capabilities. DO NOT USE FOR: installing or upgrading the CLI itself (use the speckit-self skill).