SKILLEMALL.ai

BC nhtsa-vehicle

Synthesized reference notes from three US Government vehicle-safety sources: NHTSA Cybersecurity Best Practices for the Safety of Modern Vehicles (updated 2022, final), NHTSA Automated Driving Systems 2.0: A Vision for Safety (2017), and a 16-section selection of 49 CFR Part 571 (FMVSS) pinned at eCFR versioner date 2025-01-01. Use for voluntary vehicle cybersecurity practices, the ADS 2.0 twelve priority safety design elements, and FMVSS occupant protection, brakes/stability/lighting, and fuel/EV integrity requirements. Not ISO 26262, not ASPICE, not ISO/SAE 21434, not UNECE R155/R156, not SAE J3016, and not the full Part 571; the FMVSS material is a selection of vehicle-level minimum performance standards, not a process standard.

jgsystemsconsulting/jgs-se-knowledge-packs Agent Skills author: jgsystemsconsulting 9 files body ≈ 2 234 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Synthesized reference notes from three US Government vehicle-safety sources: NHTSA Cybersecurity Best Practices for the Safety of Modern Vehicles (updated…

As a process C 63/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

ReferenceAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
99
Quality 40%
81
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Risky intent intent-offensive-security chapters/ch02-nhtsa-cyber-practices.md:37
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Cybersecurity testing and vulnerability identification.** Evaluate off-the-shelf and open-source components against known vulnerabilities; run product cybersecurity testing (including penetration 

    Files scanned: 9. 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 63/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 45 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2234 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 741: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 45 items
    • +1License stated

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