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AC megasquirt-tuner

Megasquirt ECU tuning and calibration using TunerStudio. Use when working with Megasquirt engine management systems for: (1) VE table tuning and fuel map optimization, (2) Ignition timing maps and spark advance, (3) Idle control and warmup enrichment, (4) AFR target tuning and closed-loop feedback, (5) Sensor calibration (TPS, MAP, CLT, IAT, O2), (6) Acceleration enrichment and deceleration fuel cut, (7) Boost control and launch control setup, (8) Datalog analysis and troubleshooting, (9) Base engine configuration and injector setup, (10) MSQ tune file analysis and safety review, (11) Any Megasquirt/TunerStudio ECU tuning tasks.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 2 959 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Megasquirt ECU tuning and calibration using TunerStudio.

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

ProcedureAI and agentsWriting and documentstype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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: 5. 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
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 142 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2959 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 636: enough signal without eating the budget
    • +4Structure: 36 headings
    • +3Step-by-step instructions: 142 items
    • +4Has examples (12 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.