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BB ubiquitous-language

Maintain a project thesaurus (domain glossary) following DDD ubiquitous language principles. Use PROACTIVELY when naming anything: variables, functions, classes, modules, database fields, API endpoints, events, files, or directories. Also use when the user asks to "create thesaurus", "update glossary", "add term", "rename to match domain", "check naming consistency", "what should I call this", "domain language", "ubiquitous language", or "naming conventions". Ensures all names in the codebase are consistent, descriptive, and aligned with the shared domain vocabulary. Also mines git history to resolve naming ambiguities — when a name was born, which name replaced which, which spelling is dying. Not for general code style or linting — only for domain term consistency.

CodeAlive-AI/ai-driven-development Agent Skills author: CodeAlive-AI MIT 6 files · 1 script body ≈ 7 125 tokens Open the sourcegithub.com↗ analyzed 19 h ago

Maintain a project thesaurus (domain glossary) following DDD ubiquitous language principles.

As a process B 70/100 · Nearly there — weak spots: result and completion, inputs and preconditions

ReferenceSoftware developmenttype 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
93/100
safety, quality, tests
Safety 60%
95
Quality 40%
90
Run on models
none yet
Process rating
B
70/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
70
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Write Edit Grep Glob Bash Agent AskUserQuestion

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7125 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 37, 38, 85, 86, 98, 102): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 70/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 70Failures and branches. 5 branches
  • 70Execution cost. Instruction body is 7125 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 57 steps
  • 100When it triggers. States when to use and when not to
  • 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 12 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (4 tags): a typed call is more reliable

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

  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +4Description says when NOT to use the skill
  • +3Description length 776: enough signal without eating the budget
  • +4Structure: 33 headings
  • +3Step-by-step instructions: 57 items
  • +4Has examples (22 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.