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BC fhwa-its-se

Knowledge pack synthesized from Systems Engineering for ITS, fourth generation (December 2022, Federal Highway Administration, US Government work, 17 U.S.C. § 105): ITS project systems engineering for transportation agencies. SCOPE LIMITS: single-source (December 2022 FHWA PDF); not construction specifications, not FEMA, not device model-document libraries, not a general SE textbook; INCOSE, ISO/IEC/IEEE 15288, NTCIP, IEEE, and SAE named, not quoted; reconstructed reference notes, not FHWA guidance, and no FHWA endorsement.

jgsystemsconsulting/jgs-se-knowledge-packs Agent Skills author: jgsystemsconsulting 12 files body ≈ 2 291 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Knowledge pack synthesized from Systems Engineering for ITS, fourth generation (December 2022, Federal Highway Administration, US Government work, 17 U.S.C. §…

As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

GeneratorWriting and documentstype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
64/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

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: 12. 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 64/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 3 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 34 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2291 tokens

    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)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +2Single-language instructions
    • +3Description length 529: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 34 items
    • +1License stated

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