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AB adu-city-research

Researches city-level ADU regulations, municipal codes, and standard details for any California city. This skill supports three research modes — Discovery (WebSearch to find key URLs), Targeted Extraction (WebFetch to pull content from discovered URLs), and Browser Fallback (Chrome MCP for cities with difficult websites). When used standalone, run all three modes sequentially. When invoked by an orchestrator (e.g., adu-corrections-flow), run in the specified mode only. Triggers on city-specific ADU questions, corrections letter items referencing municipal code, or when the California ADU state-level skill indicates a question requires local jurisdiction rules.

mikeOnBreeze/cc-crossbeam Agent Skills author: mikeOnBreeze MIT 4 files body ≈ 3 694 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Researches city-level ADU regulations, municipal codes, and standard details for any California city.

As a process B 67/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

ReferenceAI and agentsSoftware 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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
67/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Failures and branches w 10
50
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: cc-crossbeam, cc-crossbeam

How to improve

    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: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 67/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 31 steps, 3 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3694 tokens
    • 100Running it twice. Mutating operations check current state

    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 668: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 31 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (1 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.