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BB jsp-912

Knowledge pack synthesized from JSP 912 Human Factors Integration in Defence Systems, Part 1 Directive and Part 2 Guidance, V3.0 (March 2024, Ministry of Defence, Open Government Licence v3.0): Human Factors Integration in Defence acquisition. SCOPE LIMITS: both parts in one pack; MOD policy, not statute; not Defence SMS (use jsp-815) and not acquisition-safety policy (use jsp-376); not dependable-AI direction (use jsp-936); JSP 375, JSP 536, JSP 815, JSP 822, and DEF-STANs named, not quoted; reconstructed reference notes, not MOD guidance, and no official status.

jgsystemsconsulting/jgs-se-knowledge-packs Agent Skills author: jgsystemsconsulting 14 files body ≈ 2 191 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Knowledge pack synthesized from JSP 912 Human Factors Integration in Defence Systems, Part 1 Directive and Part 2 Guidance, V3.0 (March 2024, Ministry of…

As a process B 67/100 · Nearly there — weak spots: result and completion, when it triggers, failures and branches

IntegrationSoftware developmentLearningWriting 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
B
67/100
Nearly there
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: 14. 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

    • 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
    • 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. 36 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2191 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

    • +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 570: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 36 items
    • +1License stated

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