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Implementing robust business logic, API integrations, data models, and reproducible AI image-generation code with type safety. Use for production implementation, Gemini image API pipelines, or interactive pair programming.

simota/agent-skills Agent Skills author: simota 26 files body ≈ 5 910 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Implementing robust business logic, API integrations, data models, and reproducible AI image-generation code with type safety.

As a process B 78/100 · Nearly there — weak spots: when it triggers

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
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
B
78/100
Nearly there
When it triggers w 12
20
Tools and files w 18
60
Inputs and preconditions w 11
70
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 · 0

✓ No critical or high findings

Files scanned: 26. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5910 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 145): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 78/100

  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5910 tokens
  • 100Steps. 124 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 222: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 124 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (10 of 20)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.