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AD quantum-lab

Run the /home/bram/work/quantum_lab Python scripts and demos inside the existing venv ~/.venvs/qiskit. Use when asked (e.g., via Telegram/OpenClaw) to run quant_math_lab.py, qcqi_pure_math_playground.py, quantum_app.py subcommands, quantumapp.server, or notebooks under the repo.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files · 1 script body ≈ 450 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Run the /home/bram/work/quantumlab Python scripts and demos inside the existing venv ~/.venvs/qiskit. Use when asked (e.g., via Telegram/OpenClaw) to run…

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureTelegramSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
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: 3. 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 47/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (quantum-lab) differs from the folder (quantumlab)
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 100Steps. 19 steps
    • 100Execution cost. Instruction body is 450 tokens
    • 100Running it twice. No mutating operations
    • low The response is described with custom markup (18 tags): a typed call is more reliable

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 279: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 19 items
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.