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/cloud99277/kitclaw/skill-lintnpx skills add cloud99277/KitClaw --skill skill-lintgit clone --depth 1 https://github.com/cloud99277/KitClawWhat 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 | $0.00052 | $0.00623 |
| Opus 5 | $0.00026 | $0.00311 |
| Sonnet 5 | $0.00010 | $0.00125 |
| Haiku 4.5 | $0.00005 | $0.00062 |
Grade A, and why
skill-lint scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Skill Lint
Run deterministic lint checks across a centralized skills repository.
This skill wraps the repository-wide lint_skills.py checker and gives agents a stable entry point for validating skill frontmatter, routing metadata, and a small set of high-risk body inconsistencies.
When to Use
- After editing many skills in one pass
- Before committing or reviewing changes in a shared skills repo
- When checking whether
SKILL.mdfrontmatter follows repository conventions - When checking whether
descriptionlines still match the routing template - When you want a machine-readable lint report for CI or batch cleanup
Script Directory
Use the wrapper script in this skill:
python3 "${SKILL_DIR}/scripts/run_lint.py" [path] [options]
The wrapper delegates to the repository-level checker at:
.system/skill-creator/scripts/lint_skills.py
Common Commands
Run across the whole centralized repository:
python3 "${SKILL_DIR}/scripts/run_lint.py"
Run in strict mode:
python3 "${SKILL_DIR}/scripts/run_lint.py" --strict
Get JSON output:
python3 "${SKILL_DIR}/scripts/run_lint.py" --format json
Fail on warnings:
python3 "${SKILL_DIR}/scripts/run_lint.py" --fail-on-warnings
Only show structural errors:
python3 "${SKILL_DIR}/scripts/run_lint.py" --errors-only
Lint a specific root:
python3 "${SKILL_DIR}/scripts/run_lint.py" "/path/to/skills/root"
What It Checks
- Frontmatter structure and required keys
nameformat and lengthdescriptionshape, old phrases, and routing-template drift- Known high-risk body inconsistencies such as legacy commands or outdated trigger wording
Output Interpretation
OK: no issues found for that skillWARN: semantic or routing-quality issuesERROR: structural issues that should block acceptance
Default Behavior
- Default mode is compatibility mode
- Body checks are enabled by default
--strictis best for newly-created or fully normalized skill repos--format jsonis best for CI or batch post-processing
What ships with it
3 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.
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.
- 2d ago First seen · 100 lines · 52 tokens per session scan A e5eddb2ed59d
skill-lint is a skill published in the GitHub repository cloud99277/KitClaw (5 stars, last pushed 4mo ago), licensed MIT. It adds 52 tokens to every session and 623 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 2026-08-31.
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