Agent skill

manual-testing

Guide users step-by-step through manually testing whatever is currently being worked on. Use when asked to "test this", "verify it works", "let's test", "manual testing", "QA this", "check if it works", or after implementing a feature that needs verification before proceeding.

Stars 3
Forks 1

Install this agent skill to your Project

npx add-skill https://github.com/petekp/agent-skills/tree/main/skills/manual-testing

SKILL.md

Manual Testing

Verify current work through automated testing first, falling back to user verification only when necessary.

Core Principle

Automate everything possible. Only ask the user to manually verify what Claude cannot verify through tools.

Workflow

1. Analyze Current Context

Examine recent work to identify what needs testing:

  • Review recent file changes and conversation history
  • Identify the feature, fix, or change to verify
  • Determine testable behaviors and expected outcomes

2. Classify Each Verification Step

For each thing to verify, determine if Claude can test it automatically:

Claude CAN verify (do these automatically):

  • Code compiles/builds: npm run build, cargo build, go build, etc.
  • Tests pass: npm test, pytest, cargo test, etc.
  • Linting/type checking: eslint, tsc --noEmit, mypy, etc.
  • API responses: curl, httpie, or scripted requests
  • File contents: Read files, grep for expected patterns
  • CLI tool output: Run commands and check output
  • Server starts: Start server, check for errors, verify endpoints respond
  • Database state: Query databases, check records exist
  • Log output: Tail logs, grep for expected/unexpected messages
  • Process behavior: Check exit codes, stdout/stderr content
  • File existence/permissions: ls, stat, test -f
  • JSON/config validity: Parse and validate structure
  • Port availability: lsof, netstat, curl localhost
  • Git state: Check diffs, commits, branch state

Claude CANNOT verify (ask user):

  • Visual appearance (colors, layout, spacing, alignment)
  • Animations and transitions
  • User experience feel (responsiveness, intuition)
  • Cross-browser rendering
  • Mobile device behavior
  • Physical hardware interaction
  • Third-party service UIs (OAuth flows, payment forms)
  • Accessibility with actual screen readers
  • Performance perception (feels fast/slow)

3. Execute Automated Verifications

Run all automatable checks first. Be thorough:

bash
# Example: Testing a web feature
npm run build          # Compiles?
npm run lint           # No lint errors?
npm test               # Tests pass?
npm run dev &          # Server starts?
sleep 3
curl localhost:3000/api/endpoint  # API responds correctly?

Report results as you go. If automated tests fail, stop and address before asking user to verify anything.

4. User Verification (Only When Necessary)

For steps Claude cannot automate, present them sequentially with selectable outcomes:

Step N of M: [Brief description]

**Action:** [Specific instruction - what to do]

**Expected:** [What should happen if working correctly]

Then use AskUserQuestion with predicted outcomes:

  • 2-4 most likely outcomes as selectable options
  • First option: expected/success outcome
  • Remaining options: common failure modes
  • Free-text "Other" option is provided automatically

Example:

json
{
  "questions": [{
    "question": "How does the button look?",
    "header": "Visual check",
    "options": [
      {"label": "Looks correct", "description": "Blue button, proper spacing, readable text"},
      {"label": "Wrong color/style", "description": "Button exists but styling is off"},
      {"label": "Layout broken", "description": "Elements overlapping or misaligned"},
      {"label": "Not visible", "description": "Button missing or hidden"}
    ],
    "multiSelect": false
  }]
}

5. Handle Results

Automated test fails: Stop and fix before proceeding.

User reports issue: Note it, ask if they want to investigate now or continue testing.

6. Summarize

After all steps complete:

  • List what was verified automatically (with pass/fail)
  • List what user verified (with results)
  • Summarize any issues found
  • Recommend next actions

Guidelines

  • Run automated checks in parallel when possible
  • Be creative with verification—most things can be tested programmatically
  • If unsure whether something can be automated, try it first
  • Keep user verification steps minimal and focused on truly visual/experiential checks

Expand your agent's capabilities with these related and highly-rated skills.

petekp/agent-skills

multi-model-meta-analysis

Synthesize outputs from multiple AI models into a comprehensive, verified assessment. Use when: (1) User pastes feedback/analysis from multiple LLMs (Claude, GPT, Gemini, etc.) about code or a project, (2) User wants to consolidate model outputs into a single reliable document, (3) User needs conflicting model claims resolved against actual source code. This skill verifies model claims against the codebase, resolves contradictions with evidence, and produces a more reliable assessment than any single model.

3 1
Explore
petekp/agent-skills

capture-learning

Analyze recent conversation context and capture learnings to project knowledge files (for project-specific insights) or skills/commands/subagents (for cross-project patterns). Use when the user asks to "capture this learning", "update the docs with this", "remember this for next time", "document this issue", "add this to CLAUDE.md", "save this knowledge", or "update project knowledge". Also triggers after resolving build/setup issues, discovering non-obvious patterns, or completing debugging sessions with valuable insights.

3 1
Explore
petekp/agent-skills

optimize-agent-docs

Build a retrieval-optimized knowledge layer over agent documentation in dotfiles (.claude, .codex, .cursor, .aider). Use when asked to "optimize docs", "improve agent knowledge", "make docs more efficient", or when documentation has accumulated and retrieval feels inefficient. Generates a manifest mapping task-contexts to knowledge chunks, optimizes information density, and creates compiled artifacts for efficient agent consumption.

3 1
Explore
petekp/agent-skills

agent-changelog

Compile an agent-optimized changelog by cross-referencing git history with plans and documentation. Use when asked to "update changelog", "compile history", "document project evolution", or proactively after major milestones, architectural changes, or when stale/deprecated information is detected that could confuse coding agents.

3 1
Explore
petekp/agent-skills

literate-guide

Create a narrative guide to a codebase or feature in the style of Knuth's Literate Programming — code and prose interwoven as a single essay, ordered for human understanding rather than compiler needs. Use when the user asks to 'explain this codebase as a story', 'write a literate guide', 'create a narrative walkthrough', 'tell the story of this code', 'Knuth-style documentation', 'weave a guide for this feature', or when they want deep, readable documentation that treats the program as literature. Also trigger when someone wants a document that a thoughtful reader could follow from start to finish and come away understanding both WHAT the code does and WHY every design choice was made.

3 1
Explore
petekp/agent-skills

autonomous-agent-readiness

Assess a codebase's readiness for autonomous agent development and provide tailored recommendations. Use when asked to evaluate how well a project supports unattended agent execution, assess development practices for agent autonomy, audit infrastructure for agent reliability, or improve a codebase for autonomous agent workflows. Triggers on requests like "assess this project for agent readiness", "how autonomous-ready is this codebase", "evaluate agent infrastructure", or "improve development practices for agents".

3 1
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results