running-skills-edd-cycle
Guides evaluation-driven development (EDD) process for agent skills. Use when setting up skill testing workflows, creating skill evaluation scenarios, or establishing Claude A/B feedback loops for skill validation. Provides development methodology, not content guidance.
Works with
---
name: running-skills-edd-cycle
description: Guides evaluation-driven development (EDD) process for agent skills. Use when setting up skill testing workflows, creating skill evaluation scenarios, or establishing Claude A/B feedback loops for skill validation. Provides development methodology, not content guidance.
license: Apache-2.0
---
# Running Skills EDD Cycle
Run evaluation-driven development cycle for agent skills.
## Workflow
### Step 1: Build Evaluations First
Create evaluations BEFORE writing documentation. This ensures skills solve real problems.
1. Run Claude on representative tasks WITHOUT the skill
2. Document specific failures or missing context
3. Create 3+ evaluation scenarios that test these gaps
Evaluation scenarios are saved to `tests/scenarios.md` as the final step of `/creating-effective-skills` workflow.
### Step 2: Establish Baseline
Measure Claude's performance WITHOUT the skill:
1. Run each evaluation scenario
2. Record: success/failure, missing context, wrong approaches
3. This becomes comparison baseline
### Step 3: Write Minimal Instructions
Create just enough content to address the gaps:
- Start with core workflow only
- Add detail only when tests fail
- Avoid over-explaining
**REQUIRED:** Use the Skill tool to invoke `creating-effective-skills` before writing any skill content. This ensures proper naming, description format, and structure from the start.
### Step 4: Evaluate with Multiple Models
> **Note:** This step requires Claude Code CLI. Skip if using Claude.ai.
**REQUIRED:** Use the Skill tool to invoke `evaluating-skills-with-models` with the skill path.
This will:
1. Auto-load scenarios from `tests/scenarios.md`
2. Execute with sub-agents across models (sonnet, opus, haiku)
3. Evaluate against expected behaviors
4. Determine recommended model (least capable with full compatibility)
**After evaluation:** Document recommended model in skill's metadata.
**REQUIRED:** Use the Skill tool to invoke `improving-skills` when observations reveal issues.
### Step 5: Final Review
Before considering the skill complete:
**REQUIRED:** Use the Skill tool to invoke `reviewing-skills` to verify compliance with best practices.
1. Address all compliance issues identified
2. Re-run evaluations after fixes
3. Repeat until skill passes review
### Step 6: User Validation Guide
After all reviews pass, output instructions for user to validate in a fresh session:
```
## Test Your Skill
Run this command in a new terminal to test with a fresh Claude session:
claude --model {recommended_model} "{evaluation_query}"
After testing, paste the output file or result back to this session for final confirmation.
```
Replace:
- `{recommended_model}`: Model determined in Step 4 (e.g., `sonnet`)
- `{evaluation_query}`: A representative query from your evaluations
## Quick Reference
### Cycle
```
Identify gaps -> Create evaluations -> Baseline -> Write minimal -> Model eval (sub-agents) -> Review -> User validation
```
### What Observations Indicate
| Observation | Indicates |
|-------------|-----------|
| Unexpected file reading order | Structure not intuitive |
| Missed references | Links need to be explicit |
| Repeated reads of same file | Move content to SKILL.md |
| Never accessed file | Unnecessary or poorly signaled |More Testing skills
tdd
mattpocock/skills
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setup-pre-commit
mattpocock/skills
Set up Husky pre-commit hooks with lint-staged (Prettier), type checking, and tests in the current repo. Use when user wants to add pre-commit hooks, set up Husky, configure lint-staged, or add commit-time formatting/typechecking/testing.
agent-browser
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Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

