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BC doe-o-413-3

Knowledge base from the DOE Order on Program and Project Management for the Acquisition of Capital Assets (DOE O 413.3 series). Use for Critical Decisions CD-0–CD-4, performance baselines, acquisition strategy and IPTs, cost/schedule/EVMS/PARS controls, risk-informed governance, reviews, and contractor requirements. Built from O 413.3C (2026-08-05), which cancels O 413.3B Chg 7 (2023-06-21); does not replace site-specific procedures, nuclear safety rules detail, or full estimating standards.

jgsystemsconsulting/jgs-se-knowledge-packs Agent Skills author: jgsystemsconsulting 12 files body ≈ 1 109 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Knowledge base from the DOE Order on Program and Project Management for the Acquisition of Capital Assets (DOE O 413.3 series). Use for Critical Decisions…

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ReferenceOperations and projectsData and analyticsAI and agentstype 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
58/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
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 58/100

    • 0Result and completion. Does not say what the result is
    • 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. 2 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 23 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1109 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 496: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 23 items
    • +1License stated

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