agentleFS
jellydnjellydn/my-ai-tools/skills/capability-experiments/SKILL.md

capability-experiments

Skill127 starsChanged 26 days agoMITView on GitHub

Build an interactive report or experiment when the user asks to explore model capabilities.

The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 9 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈9

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when:

    line 14
  2. Distinctive:

    Use HTML reports when:

    line 53
  3. Distinctive:

    Use HTML/CSS to visualize decision processes:

    line 84
  4. Distinctive:

    Instead of asking the user what to look at, proactively scan the codebase:

    line 109
  5. Distinctive:

    Before starting work, proactively look for gotchas:

    line 120
  6. Distinctive:

    When faced with a complex task, probe what's possible:

    line 132
  7. Distinctive:

    spec-interview: Use embedded questionnaires for richer interviews

    line 257
  8. Distinctive:

    doc-search: Search for past HTML report patterns

    line 261
  9. Distinctive:

    Fall back gracefully: If HTML doesn't render well, plaintext always works

    line 269

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Capability Experiments

When to Use

Use this skill when:

  • You need to present complex analysis in a readable format
  • Interactive questionnaires would improve user interaction
  • Exploring what's possible with next-generation model capabilities
  • Multi-step reasoning is needed for architectural decisions
  • You want to generate rich outputs (HTML, tables, interactive elements)
  • Standard text output isn't sufficient for the task

What It Does

Teaches techniques for leveraging advanced model capabilities — HTML generation, embedded interactive elements, proactive research, and multi-step reasoning. These patterns showcase what's newly possible with next-generation models and should be used freely.

HTML Report Generation

Advanced models can generate rich, self-contained HTML. This is useful for:

Analysis Reports

Generate structured HTML reports for complex findings:

<!DOCTYPE html>
<html>
<head><style>
  body { font-family: system-ui; max-width: 800px; margin: 2rem auto; }
  .finding { border-left: 4px solid #e74c3c; padding: 1rem; margin: 1rem 0; }
  .finding.fixed { border-color: #2ecc71; }
  .severity { font-weight: 600; font-size: 0.85rem; }
</style></head>
<body>
  <h1>Code Review: PR #288</h1>
  <div class="finding">
    <span class="severity">🔴 Critical</span>
    <p>Hardcoded path in config...</p>
  </div>
  ...
</body></html>

Use HTML reports when:

  • Comparing multiple options or decisions
  • Presenting structured analysis with severity levels
  • Creating interactive documentation
  • Showing progress or status dashboards

Embedded Questionnaires

Generate HTML questionnaires for spec interviews and quizzes:

<form id="quiz">
  <div class="question">
    <p>1. Why did we choose GitHub App Installation flow?</p>
    <label><input type="radio" name="q1" value="a"> OAuth is deprecated</label>
    <label><input type="radio" name="q1" value="b"> Org-level access ✓</label>
  </div>
  <button type="button" onclick="checkAnswers()">Check</button>
</form>
<script>
function checkAnswers() {
  const correct = { q1: 'b', q2: 'c' };
  // ... scoring logic
}
</script>

These work well with Fable's ability to render and execute embedded HTML in responses.

Decision Trees and Flowcharts

Use HTML/CSS to visualize decision processes:

<div class="decision-tree">
  <div class="node root">Feature Change</div>
  <div class="branch">
    <div class="node">Familiar code?</div>
    <div class="yes">→ Standard pattern</div>
    <div class="no">→ /blindspots first</div>
  </div>
</div>
<style>
  .decision-tree { font-family: monospace; }
  .node { background: #f0f0f0; padding: 8px; margin: 4px; }
  .yes { color: #2ecc71; }
  .no { color: #e74c3c; }
</style>

Proactive Research

Let the agent self-direct exploration rather than waiting for instructions:

Pattern: "Investigate and Report"

Instead of asking the user what to look at, proactively scan the codebase:

1. Scan recent changes with `fff` and `git log`
2. Identify areas of concern or interest
3. Analyze patterns and potential issues
4. Report findings with actionable recommendations

Pattern: "Unknown Unknowns Scan"

Before starting work, proactively look for gotchas:

1. Search git history for past bugs in related areas
2. Check qmd for relevant learnings
3. Search ctx for past agent discussions
4. Review existing ADRs for architectural context
5. Report findings before proposing implementation

Pattern: "Capability Probe"

When faced with a complex task, probe what's possible:

1. Generate multiple approaches (not just the obvious one)
2. For each approach, assess feasibility
3. Consider approaches that were hard with older models
4. Recommend the best approach with rationale

Multi-Step Reasoning

Break complex decisions into structured analysis:

Step 1: Decompose the Problem

## Analysis: Authentication Strategy

### Dimensions to Consider
1. Security requirements (OAuth 2.0, org-level access)
2. User experience (login flow, token management)
3. Maintenance (token refresh, error handling)
4. Scalability (multiple orgs, rate limits)

### Trade-offs
| Approach | Security | UX | Maintenance | Scalability |
|----------|----------|----|-------------|-------------|
| OAuth App | Medium | High | Low | Low |
| GitHub App | High | Medium | Medium | High |
| PAT | Low | Low | High | Medium |

Step 2: Research and Validate

For each promising approach, gather evidence:

## Research: GitHub App Installation

### Findings
- ✓ Org-level repo access (required)
- ✓ Webhook-based events
- ✓ Token caching supported
- ✗ More complex setup
- ✗ Requires webhook endpoint

### Past Context
- Previous PR #123 attempted similar approach
- ADR-005 discusses webhook infrastructure
- qmd has learnings about token caching

Step 3: Recommend with Evidence

## Recommendation

**Approach**: GitHub App Installation Flow

**Rationale**:
1. Required for org-level access (blocker for OAuth App)
2. Token caching addresses UX concerns
3. Existing webhook infrastructure from PR #123
4. ADR-005 confirms architectural fit

**Risks**:
- Webhook endpoint needs high availability
- Token refresh handling adds complexity

Embedded Questionnaires

Interactive questionnaires improve spec interviews and knowledge verification:

For Spec Interviews

Generate structured questions with HTML forms:

<h3>Architecture Questions</h3>
<div class="question">
  <p><strong>Q1:</strong> Should we use a single shared database or per-tenant databases?</p>
  <details>
    <summary>Context</summary>
    <p>Current system uses shared DB. Per-tenant improves isolation but adds operational complexity.</p>
  </details>
  <textarea rows="2" placeholder="Your thinking..."></textarea>
</div>

For Quizzes

Generate self-assessment quizzes:

<h3>Implementation Quiz</h3>
<form id="quiz">
  <div class="q">
    <p>1. What caching strategy did we use for installation tokens?</p>
    <label><input type="radio" name="q1" value="r"> Redis with TTL</label>
    <label><input type="radio" name="q1" value="w"> In-memory cache</label>
  </div>
  <button type="button" onclick="grade()">Check Understanding</button>
</form>

Proactive Patterns Summary

When to Use Each Pattern:

┌──────────────────────────────┬──────────────────────────┐
│ Situation                     │ Use Pattern              │
├──────────────────────────────┼──────────────────────────┤
│ Complex analysis to present  │ HTML report              │
│ Spec is vague                │ Embedded questionnaire   │
│ Large, unfamiliar codebase   │ Proactive research scan  │
│ Hard architectural decision  │ Multi-step reasoning     │
│ After implementation         │ Embedded quiz            │
│ Exploring options            │ Capability probe         │
└──────────────────────────────┴──────────────────────────┘

Integration with Other Skills

  • spec-interview: Use embedded questionnaires for richer interviews
  • quiz-me: Generate HTML quizzes instead of plaintext
  • blindspot-pass: Present findings as structured HTML reports
  • implementation-logger: Generate HTML reports from logs
  • doc-search: Search for past HTML report patterns

Tips

  • Use HTML for structure, not flash: Clean, readable HTML is better than complex designs
  • Self-contained is key: Include styles inline so reports render anywhere
  • Interactive when useful: Forms and buttons engage the user; plain text is fine for simple cases
  • Probe before committing: Try one capability at a time, verify it works
  • Fall back gracefully: If HTML doesn't render well, plaintext always works
  • Document what works: New capabilities discovered should be added to this skill

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.