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Designing and reviewing APIs: OpenAPI spec generation, versioning strategy, breaking change detection, REST/GraphQL best practices. Use for API design or OpenAPI specs.

simota/agent-skills Agent Skills author: simota 22 files body ≈ 5 867 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Designing and reviewing APIs: OpenAPI spec generation, versioning strategy, breaking change detection, REST/GraphQL best practices.

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

IntegrationSoftware developmentInfrastructureData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
96
Quality 40%
77
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 4

✓ No critical or high findings

Medium and low: 4
  • low Risky intent intent-offensive-security reference/api-security-anti-patterns.md:29
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | **API5** | Broken Function Level Authorization | Insufficient authorization at the function level | Direct calls to admin APIs, privilege escalation via method changes |
  • low Secrets in code secret-high-entropy-token reference/webhook-design.md:23
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - `webhook-id: msg_…f4W` — stable event ID for idempotency.
    quoted
  • low Risky intent intent-offensive-security SKILL.md:76
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - Security audit beyond API layer (threat modeling, penetration testing): `Sentinel`
  • low Risky intent intent-offensive-security SKILL.md:221
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    | Sentinel | API-layer security design (OAuth scope, rate limiting, CORS headers) | Broad security audit, threat modeling, penetration testing |

Files scanned: 22. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5867 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 80, 83, 116): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 57/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 5867 tokens
  • 100Steps. 90 steps
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 13 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 168: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 90 items
  • +3Output format is stated explicitly
  • +4Reference files are cited in the instructions (18 of 18)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.