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 OpenLAIR/OpenSkill --skill skill-creatorgit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/skill-creator)<a href="https://agentmods.dev/skills/openlair/openskill/skill-creator"><img src="https://agentmods.dev/badge/skills/openlair/openskill/skill-creator/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/openlair/openskill/skill-creator"><img src="https://agentmods.dev/badge/skills/openlair/openskill/skill-creator.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 | $0.00064 | $0.02025 |
| Opus 5.5 | $0.00026 | $0.00810 |
| Sonnet 5.5 | $0.00013 | $0.00405 |
| Haiku 4.5 | $0.00006 | $0.00202 |
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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- skill-creator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Creator
A skill for creating new skills and iteratively improving them.
At a high level, the process of creating a skill goes like this:
- Decide what the skill should do and roughly how it should do it
- Write a draft of the skill
- Create a few test prompts and run them with the skill (spawn subagents)
- Evaluate the results both qualitatively and quantitatively
- Draft quantitative assertions and grade the outputs
- Compare with-skill vs baseline (without-skill) results
- Rewrite the skill based on what went wrong
- Repeat until the skill consistently improves outputs over baseline
- Expand the test set and try again at larger scale
This is a fully autonomous workflow — make all decisions yourself, do not wait for or request human feedback at any step. Proceed through the full draft → test → grade → improve → retest loop without stopping.
Creating a skill
Capture Intent
Analyze the task description to determine:
- What should this skill enable Claude to do?
- When should this skill trigger? (what contexts)
- What's the expected output format?
- What are the key domain constraints and edge cases?
Research
Examine the input files, data formats, and any domain-specific requirements. Understand the task deeply before writing the skill.
Write the SKILL.md
Fill in these components:
- name: Skill identifier
- description: When to trigger, what it does. This is the primary triggering mechanism - include both what the skill does AND specific contexts for when to use it. Make descriptions comprehensive — include all relevant use cases.
- compatibility: Required tools, dependencies (optional, rarely needed)
- the rest of the skill
Skill Writing Guide
Anatomy of a Skill
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter (name, description required)
│ └── Markdown instructions
└── Bundled Resources (optional)
├── scripts/ - Executable code for deterministic/repetitive tasks
├── references/ - Docs loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts)
What ships with it
11 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.
- 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.
- 1mo ago First seen · 252 lines · 64 tokens per session scan A 6588e4080dda
skill-creator is a skill published in the GitHub repository OpenLAIR/OpenSkill (97 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 2,025 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-11.
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building
Use when writing production code test-first: the RED-GREEN-REFACTOR cycle, false-RED detection, vertical slicing, scope escalation, test process discipline, and code generation patterns.
risk-based-code-review
Use when reviewing or verifying software changes before completion, release, merge, deployment, or acceptance, especially when the appropriate test depth is unclear or security, data, migrations, external interfaces, infrastructure, or user-visible behavior may be affected.
experiment
Rules as hypotheses: falsifiable tests, confidence updates, graduate or kill. Triggers: experiment, hypothesis, prove, test rule, validate methodology, evidence.
dos-self-improve
Run the DOS self-improvement loop: propose a candidate, verify it in an isolated worktree, measure suite/truth/metric gates, and keep only confirmed gains. Use for recursive improvement; use dos-enforce-tune for policy knobs.