SKILLEMALL.ai
GitHub ↗ RU Sign in

AC operational-design

Design and improve operational processes, controls, metrics, vendors, and scaling models through bounded pilots and evidence. Do not use for engineering delivery, financial modeling, technology evaluation, or legal advice. design, operational metrics, compliance and audit, vendor management, and team topology. Covers value stream mapping, BPMN, bottleneck analysis, scaling from 10 to 100 to 1000 people, KPI design, balanced scorecard, SOC 2, ISO 27001, GDPR readiness, RFP processes, SLA design, vendor scorecards, team topologies, Conway's Law, and Dunbar's Number. Do not use for engineering delivery, financial modeling, or technology evaluation.

magnus919/agent-skills Agent Skills author: magnus919 MIT 9 files body ≈ 1 131 tokens Open the sourcegithub.com↗ analyzed 25 h ago

Design and improve operational processes, controls, metrics, vendors, and scaling models through bounded pilots and evidence.

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerProcurementData and analyticstype 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
A
97/100
safety, quality, tests
Safety 60%
99
Quality 40%
93
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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 references/compliance.md:176
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | **Testing results** | Penetration test reports, DR test results | Scheduled external testing |

    Files scanned: 8. 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 57/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1131 tokens
    • 100Running it twice. No mutating operations
    • medium 5 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 653: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.