SkillCheck — Comprehensive Skill Testing
SkillCheck audits an entire skill directory, understands every moving part, and generates targeted tests to verify the skill works as advertised. It doesn't just check "does it roughly work" — it tests every aspect: triggering, input handling, output quality, script correctness, security, best practices, and resource integrity.
How it works
Three phases: Discover → Test → Report. Discovery is always required. Testing depth depends on what the user wants. Reporting adapts to what was found.
| Depth | What it covers | |---------------|---------------------------------------------------------------------------| | Quick | Static analysis — structure, syntax, integrity, security, best practices | | Standard | Static + script execution + happy-path I/O + eval regression | | Deep | Everything — edge cases, mock files, e2e workflows, stress |
If the user doesn't specify, default to Standard — it catches real functional problems without the time investment of Deep. If they seem in a hurry, go Quick. If they mention thoroughness, edge cases, or "test everything", go Deep.
For a worked example of a Standard Check from start to finish, see references/example-run.md.
Gotchas
- The
evals.jsonformat must match the skill-creator schema exactly (see
references/eval-schema.md). Don't invent a new format — compatibility with skill-creator's benchmarking tools is the whole point.
- Mock file generation for binary formats (DOCX, XLSX, PPTX, PDF, images) requires
third-party Python packages that may not be installed. Always check availability before generating and fall back to text-based equivalents if installation fails. See references/mock-files.md for the dependency list.
- Cross-reference mismatches between SKILL.md and scripts are often the root cause
of multiple downstream test failures (script execution fails, eval regression fails, I/O tests fail — all for the same reason). Identify and report the root cause rather than listing each symptom as a separate unrelated failure.
- Best practice scores are advisory and reported separately — do not add them to the
main pass/fail verdict percentage. A skill can be functionally perfect but structurally messy, and the report should reflect that distinction.
- Security review runs before script execution for a reason — if Critical security
issues are found in scripts, warn the user before executing them at Standard or Deep depth. Don't silently run code you've just flagged as dangerous.
- Incomplete skills (TODOs, stubs, placeholder text) need a progress report, not a
wall of failures. It's easy to mechanically run the standard flow and produce 15 failures that all say "this doesn't exist yet" — that's not useful. Read references/incomplete-skills.md and reframe.
- On Claude.ai without subagents, end-to-end tests run sequentially in shared context.
This means earlier test results can influence later tests — an agent that just discovered a cross-reference bug might "know" to use the correct arguments on the next test. Keep this in mind when interpreting e2e results.
- The frontmatter
namefield must match the parent directory name per the Agent
Skills spec. This is easy to miss because many platforms will still load the skill with a mismatch — but others won't, making it a silent portability bug.
- Report sections should be collapsed when empty — a Quick Check on a minimal skill
should produce 5 sections, not a 12-section skeleton full of "N/A". See references/report-format.md for collapsing rules.
Phase 1: Discover
Find the skill, scan it, understand what it contains, and decide how to test it.
Find the skill
If the user gives a specific path, use it. If not, read references/finding-the-skill.md to locate the skill from conversation context or common locations.
Scan the directory
Read the entire skill directory, not just SKILL.md. Categorize every file:
| Category | What to look for | |----------------|------------------------------------------------------------------------| | Core | SKILL.md — the main instructions file | | Scripts | scripts/ — executable code (Python, Bash, JS, etc.) | | References | references/ — documentation loaded into context as needed | | Assets | assets/ — templates, icons, fonts, images used in output | | Examples | Any example input/output files, sample data | | Config | Package files, requirements, dependency lists | | Evals | evals/ — existing test cases (if any) |
Record a manifest — you'll reference this throughout testing.
Parse the SKILL.md
Extract from the frontmatter and body:
- Name and description — what it does and when it should trigger
- Declared inputs — file types, formats, schemas (pay close attention to explicitly
named extensions, column names, data structures)
- Declared outputs — file types, formats, target paths, expected structures
- Step-by-step workflows — numbered or ordered procedures
- Tool/dependency requirements — libraries, CLIs, external tools
- Conditional logic — branching paths
- Constraints and rules — ALWAYS, NEVER, MUST directives
- References to bundled files — when to read other files in the skill
- Script invocations — exact commands including arguments and flags
Analyze supporting files
For each non-SKILL.md file:
- Scripts: Read the code. Identify arguments, outputs, hardcoded paths, error handling,
and dependencies. Pay special attention to argument parsing — you'll compare this against how SKILL.md invokes the script.
- References: Skim for structure and purpose. Note if SKILL.md actually references them.
- Assets: Note file types and whether they're referenced. Assets will be validated for
integrity during testing — see references/quick-check.md.
- Examples: Identify as input examples, output examples, or both — gold for test generation.
- Evals: Read
evals.jsonif present. Seereferences/eval-schema.mdfor the format.
Present findings
Show the user: the file manifest, what the skill claims to do, declared inputs/outputs, immediate red flags, and whether evals were found.
If the skill appears incomplete (TODOs, placeholder text, missing referenced files, stub scripts), read references/incomplete-skills.md and adjust your approach — reframe as a progress report rather than a failure report.
Then ask which depth level they'd like (if they haven't already said).
Phase 2: Test
Read the reference file for the chosen depth level. It defines exactly which tests to run, in what order, and how to evaluate results:
- Quick:
references/quick-check.md— static integrity, asset validation, SKILL.md
quality, cross-reference consistency, multi-skill dependencies, security review, and 22 best practice checks (references/best-practices.md)
- Standard:
references/standard-check.md— everything in Quick, plus eval regression
(references/eval-schema.md), script execution with mock inputs (references/mock-files.md), happy-path I/O contract validation, and workflow tests
- Deep:
references/deep-check.md— everything in Standard, plus edge case inputs,
end-to-end workflow simulation, triggering analysis, error recovery, and script robustness
Test plan (Standard and Deep only)
For Standard and Deep checks, present the test plan to the user before executing — they may know about domain-specific edge cases worth adding, or tests that aren't relevant. For Quick checks on simple ski