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BC dau-se-guidebook

Knowledge base from the DoD Systems Engineering Guidebook (OUSD R&E, February 2022). Use for the DoD SE process model — the 16 SE processes (8 technical management + 8 technical) mapped to ISO/IEC/IEEE 15288, the event-driven technical reviews and audits across the Major Capability Acquisition life cycle (ASR, SRR, SFR, PDR, CDR, SVR/FCA, PRR, PCA), the Systems Engineering Plan (SEP), risk/issue/opportunity management, TPMs and leading indicators, system-of-systems and digital engineering, and the 24 design considerations (RAM, system safety, HSI, system security engineering, MOSA, supportability, etc.). Acquisition-program oriented: thin on detailed cost estimating, contracting law, and non-DoD or commercial life cycles.

jgsystemsconsulting/jgs-se-knowledge-packs Agent Skills author: jgsystemsconsulting 12 files body ≈ 3 575 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Knowledge base from the DoD Systems Engineering Guidebook (OUSD R&E, February 2022).

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

ReferenceAI and agentsData and analyticsOperations and projectstype 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
    • 40Result and completion. Does not say what the result is
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 50 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3575 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 731: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 50 items
    • +1License stated

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