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BC se-standards-signpost

Signpost (not a knowledge pack) for the systems-engineering standards landscape — ISO/IEC/IEEE 15288, 24748, 29148, 42010, 15026, 24765; INCOSE SE Handbook & Vision 2035; OMG SysML; SAE/EIA 1001/649/748; ECSS space standards; NATO AAP-48; CMMI; NIST SP 800-160; and more. Contains NO source content: it lists each standard's designation, title, purpose, owning SDO, whether it's redistributable, and where to get it. Use when you need to identify or locate an SE standard — most are paywalled/all-rights-reserved and cannot be packaged, so this points you to the owner (and to the open packs where one exists).

jgsystemsconsulting/jgs-se-knowledge-packs Agent Skills author: jgsystemsconsulting 2 files body ≈ 3 047 tokens Open the sourcegithub.com↗ analyzed 2 h ago

Signpost (not a knowledge pack) for the systems-engineering standards landscape — ISO/IEC/IEEE 15288, 24748, 29148, 42010, 15026, 24765; INCOSE SE Handbook &…

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

ReferenceResearchOperations 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "kind"

    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. 1 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 16 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3047 tokens
    • low 12 top-level sections: this looks like several domains in one skill

    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
    • -255 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 610: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 16 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.