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 stark-ai-de/agent-skills --skill skill-authoring-reviewgit clone --depth 1 https://github.com/stark-ai-de/agent-skillsWrote 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/stark-ai-de/agent-skills/skill-authoring-review)<a href="https://agentmods.dev/skills/stark-ai-de/agent-skills/skill-authoring-review"><img src="https://agentmods.dev/badge/skills/stark-ai-de/agent-skills/skill-authoring-review/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/stark-ai-de/agent-skills/skill-authoring-review"><img src="https://agentmods.dev/badge/skills/stark-ai-de/agent-skills/skill-authoring-review.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 | $0.00060 | $0.00752 |
| Opus 5 | $0.00030 | $0.00376 |
| Sonnet 5 | $0.00012 | $0.00150 |
| Haiku 4.5 | $0.00006 | $0.00075 |
Grade A, and why
skill-authoring-review 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 11d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Authoring Review
Goal
Create or review an Agent Skill so another agent can use it reliably with minimal context, clear routing, safe bundled resources, and a concrete output contract.
When to use
- The user asks to create, rewrite, or review a skill.
- A public skill repository needs an installability or quality check.
- A skill has vague triggers, oversized instructions, unsafe scripts, or missing artifacts.
When not to use
- The user is asking to execute a domain workflow that an existing skill already covers.
- The task is ordinary repository review with no skill authoring surface.
- The user only needs installation help; use
skill-installation-support.
Inputs to inspect
SKILL.mdfrontmatter and body.references/,scripts/,assets/, andagents/folders.- README catalog entries and install commands when reviewing a repo.
- Validation output from
npm run validateor an equivalent checker.
Review rubric
Check that name matches the folder, uses lowercase hyphen-case, and appears with a clear description. The description must front-load trigger words because it is the routing surface. The body should state goal, scope, workflow, safety rules, output format, completion criteria, and failure modes.
Read references/skill-quality-rubric.md when doing a scored review. Read references/frontmatter-examples.md when rewriting descriptions.
Workflow
- Inspect the skill path and identify the intended workflow.
- Validate frontmatter syntax, name matching, and description routing quality.
- Check progressive disclosure: keep core steps in
SKILL.mdand move long examples or rubrics into references. - Review scripts for deterministic behavior, clear documentation, and safe defaults.
- Verify output artifacts are concrete enough for a future agent to act on.
- Run available validation commands when working in a repository.
- Return blocking issues first, followed by improvements and a concise rewrite plan.
What ships with it
2 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.
- 11d ago First seen · 93 lines · 60 tokens per session scan A 2a07b1b0831e
skill-authoring-review is a skill published in the GitHub repository stark-ai-de/agent-skills (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 60 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 2026-08-31.
Other skills, from other repositories
openlore-brainstorm
Transform a feature idea into an annotated story using a Domain Sketch or Constrained Option Tree. Use when asked to brainstorm, explore, or shape a feature before implementation.
openlore-execute-refactor
Apply a confirmed .openlore/refactor-plan.md with a test gate after each change. Use when asked to execute or continue an OpenLore refactoring plan.
openlore-plan-refactor
Identify a high-priority refactoring target, assess its blast radius, and write .openlore/refactor-plan.md without changing code. Use when asked to plan or prioritize a refactor.
openlore-debug
Debug with OpenLore structural context, an explicit root-cause hypothesis, and RED/GREEN verification. Use when a bug, failure, or regression needs diagnosis and repair.
openlore-implement-story
Implement a brownfield story with OpenLore orientation, risk checks, spec validation, tests, and drift detection. Use when asked to implement or continue a story in an existing codebase.
openlore-write-tests
Write and run real tests for a function or spec scenario after reading implementation and contract evidence. Use when asked to add, improve, or repair tests without stubs or placeholders.