Capability Experiments
SkillAI & modelsExperiment with model capabilities — HTML reports, embedded questionnaires, proactive research, multi-step reasoning
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Capability Experiments skill
What this skill tells your AI
The instructions your AI receives, as published by jellydn/my-ai-tools in skills/capability-experiments/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 120
- Forks
- 13
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
capability-experiments- Source
- github.com/jellydn/my-ai-tools