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BC adu-corrections-flow

Analyzes ADU permit corrections letters — the first half of the corrections pipeline. Reads the corrections letter, builds a sheet manifest from the plan binder, researches state and city codes, views referenced plan sheets, categorizes each correction item, and generates informed contractor questions. This skill should be used when a contractor receives a city corrections letter for an ADU permit. It coordinates three sub-skills (california-adu for state law, adu-city-research for city rules, adu-targeted-page-viewer for plan sheet navigation) to produce research artifacts and a UI-ready questions JSON. Does NOT generate the final response package — that is handled by adu-corrections-complete after the contractor answers questions. Triggers when a corrections letter PDF/PNG is provided along with the plan binder PDF.

mikeOnBreeze/cc-crossbeam Agent Skills author: mikeOnBreeze MIT 3 files body ≈ 2 865 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Analyzes ADU permit corrections letters — the first half of the corrections pipeline.

As a process C 59/100 · Has gaps — weak spots: result and completion, progress reporting

AnalyzerSoftware developmentInfrastructuretype 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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Failures and branches w 10
50
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: adu-corrections-flow (mikeOnBreeze/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: 3. 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 59/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 60Steps. 43 steps, 4 vague phrases
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2865 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)
    • +3Description length 829: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 43 items
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.