skillgrade-setup

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Repo stars 490
Author repo skillgrade

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Sets up and runs skillgrade evaluation pipelines for Agent Skills. Use when initializing eval configurations, running trials, re…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Procedures”, “Error Handling”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name skillgrade-setup directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Procedures / Error Handling” before expanding.

Boundaries And Review

Dependencies: Prepare OpenAI / Anthropic / Gemini API keys before running a full task.
Permissions: Declared permissions include read / write / env-read; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Skillgrade Evaluation Setup

Procedures

Step 1: Install Skillgrade

Verify Node.js 20+ and Docker are available.
Run npm i -g skillgrade to install the CLI globally.

Step 2: Initialize an Eval Configuration

Navigate to the skill directory (must contain a SKILL.md).
Set the appropriate API key environment variable (GEMINI_API_KEY, ANTHROPIC_API_KEY, or OPENAI_API_KEY).
Run skillgrade init to generate an eval.yaml with AI-powered tasks and graders.
If an eval.yaml already exists, pass --force to overwrite: skillgrade init --force.
Without an API key, a well-commented template is generated instead.

Step 3: Configure eval.yaml

Read references/eval-yaml-spec.md for the full configuration schema.
Define one or more tasks under the tasks: key. Each task requires:
- name: unique task identifier
- instruction: what the agent should accomplish
- workspace: files to copy into the evaluation container
- graders: one or more scoring mechanisms (see the skillgrade-graders skill)
Optionally configure defaults: for agent, provider, trials, timeout, and threshold.

Step 4: Run Evaluations

Select an appropriate preset based on the evaluation goal:
- --smoke (5 trials): Quick capability check.
- --reliable (15 trials): Reliable pass rate estimate.
- --regression (30 trials): High-confidence regression detection.
Run the evaluation: skillgrade --smoke.
Run a specific eval by name: skillgrade --eval=fix-linting.
Run multiple evals: skillgrade --eval=fix-linting,write-tests.
Run only deterministic graders (skip LLM calls): skillgrade --grader=deterministic.
Run only LLM rubric graders: skillgrade --grader=llm_rubric.
The agent is auto-detected from the API key. Override with --agent=gemini|claude|codex.
Override the provider with --provider=docker|local.

Step 5: Review Results

Run skillgrade preview for a CLI report.
Run skillgrade preview browser to open the web UI at http://localhost:3847.
Reports are saved to $TMPDIR/skillgrade/<skill-name>/results/. Override with --output=DIR.

Step 6: Integrate with CI

Add a GitHub Actions step that installs skillgrade, navigates to the skill directory, and runs with --regression --ci --provider=local.
Use --provider=local in CI — the runner is already an ephemeral sandbox, so Docker adds overhead without benefit.
The --ci flag causes a non-zero exit code if the pass rate falls below --threshold (default: 0.8).
Read references/ci-example.md for a complete workflow template.

Error Handling

If skillgrade init fails with "No SKILL.md found," verify the current directory contains a valid SKILL.md file.
If evaluation hangs, check Docker is running and the container has network access for API calls.
If all trials fail with "No API key," ensure the environment variable is exported, not just set inline for a different command.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Writing
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @mgechev · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Manual integration
External API key: Required · OpenAI / Anthropic / Gemini
Detected OS requirements: Docker
Runtime requirements: Node.js · Docker
Detected file/system behavior: Read-only
Write / modify
Env read
Detected network behavior: External requests
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview skillgrade-setup.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# Skillgrade Evaluation Setup

## Procedures

**Step 1: Install Skillgrade**
1. Verify Node.js 20+ and Docker are available.
2. Run `npm i -g skillgrade` to install the CLI globally.

**Step 2: Initialize an Eval Configuration**
1. Navigate to the skill directory (must contain a `SKILL.md`).
2. Set the appropriate API key environment variable (`GEMINI_API_KEY`, `ANTHROPIC_API_KEY`, or `OPENAI_API_KEY`).
3. Run `skillgrade init` to generate an `eval.yaml` with AI-powered tasks and graders.
4. If an `eval.yaml` already exists, pass `--force` to overwrite: `skillgrade init --force`.
5. Without an API key, a well-commented template is generated instead.

**Step 3: Configure eval.yaml**
1. Read `references/eval-yaml-spec.md` for the full configuration schema.
2. Define one or more tasks under the `tasks:` key. Each task requires:
   - `name`: unique task identifier
   - `instruction`: what the agent should accomplish
   - `workspace`: files to copy into the evaluation container
   - `graders`: one or more scoring mechanisms (see the `skillgrade-graders` skill)
3. Optionally configure `defaults:` for agent, provider, trials, timeout, and threshold.

**Step 4: Run Evaluations**
1. Select an appropriate preset based on the evaluation goal:
   - `--smoke` (5 trials): Quick capability check.
   - `--reliable` (15 trials): Reliable pass rate estimate.
   - `--regression` (30 trials): High-confidence regression detection.
2. Run the evaluation: `skillgrade --smoke`.
3. Run a specific eval by name: `skillgrade --eval=fix-linting`.
4. Run multiple evals: `skillgrade --eval=fix-linting,write-tests`.
5. Run only deterministic graders (skip LLM calls): `skillgrade --grader=deterministic`.
6. Run only LLM rubric graders: `skillgrade --grader=llm_rubric`.

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Procedures → Error Handling
terms    -> Step 1: Install Skillgrade · Step 2: Initialize an Eval Configuration · Step 3: Configure eval.yaml · Step 4: Run Evaluations · Step 5: Review Results · Step 6: Integrate with CI
files/cmd -> npm i -g skillgrade · GEMINIAPIKEY · ANTHROPICAPIKEY · OPENAIAPIKEY · skillgrade init · eval.yaml · --force · skillgrade init --force
body sha256 -> 16ebf8cdf65f