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AB adu-targeted-page-viewer

Extracts construction plan PDFs into page PNGs, reads the sheet index to build a sheet-to-page manifest, and enables targeted viewing of specific sheets. This skill should be used when a corrections letter references specific plan sheets (e.g., "Sheet A3", "Detail 2/S3.1") and those sheets need to be located and analyzed within the PDF binder. Much faster than full plan extraction — builds the sheet manifest in under 2 minutes, then individual sheet lookups are instant. Triggers when a plan PDF needs to be navigated by sheet reference, or when the corrections interpreter needs to look at specific pages.

mikeOnBreeze/cc-crossbeam Agent Skills author: mikeOnBreeze MIT 4 files · 2 scripts body ≈ 1 625 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Extracts construction plan PDFs into page PNGs, reads the sheet index to build a sheet-to-page manifest, and enables targeted viewing of specific sheets.

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

ProcedureData 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
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Failures and branches w 10
55
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: 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

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 14 steps, 2 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 1625 tokens
    • 100Running it twice. No mutating operations

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 610: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 14 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.