Spike Prototype
SkillDocs & knowledgeRuns quick throw-away prototypes so your agent can test an idea and write down what it learns.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Spike Prototype skill
About this skill
Throw-away prototype for unknown problem spaces. Output is learning notes in specs/archive/spikes/SPIKE-<name>.md, not production code. Use when the domain or technology is unexplored, when estimates are impossible without experimentation, or when user says "spike", "prototype", or "proof of concept
What this skill tells your AI
The instructions your AI receives, as published by danielvm-git/bigpowers in skills/spike-prototype/SKILL.md and read by ahel’s review.
HARD GATE — HARD GATE — Spikes are time-boxed experiments, not shipping code. Results must be throwaway or clearly isolated. Do NOT merge a spike without a plan to integrate it or replace it with a proper implementation.
A spike is a time-boxed experiment to answer a specific question. The code is thrown away. The learning is kept in specs/archive/spikes/SPIKE-<name>.md.
The spike produces learning, not code to ship. If you find yourself cleaning up spike code for production, stop — run plan-work and develop-tdd instead with the insights you gained.
When to spike
- The technology is unfamiliar (new library, API, infrastructure)
- The approach is uncertain (multiple solutions exist; none has been tried)
- Estimates are impossible without seeing how the thing actually behaves
- A key assumption needs to be validated before committing to a design
Process
1. Define the question
Before writing a single line, state the question the spike must answer:
"Can we [specific thing] using [specific approach] within [constraint]?"
Examples:
- "Can we stream large files from S3 to the client without buffering in memory?"
- "Does the Stripe webhook SDK handle signature verification correctly in our edge runtime?"
- "Can we achieve < 100ms p99 response time for the search endpoint with a naive Postgres full-text search?"
A spike with no question is just unplanned coding. Refuse to start if the question isn't clear.
2. Set a timebox
Agree on a timebox with the user: 30 minutes, 1 hour, 2 hours. When time is up, stop — even if the question isn't fully answered. Partial learning is still learning.
3. Experiment
Write the simplest code that could answer the question. Ignore:
- Error handling
- Test coverage
- Code quality
- Production concerns
Focus entirely on answering the question.
4. Write specs/archive/spikes/SPIKE-.md
Save the learning to specs/archive/spikes/SPIKE-<name>.md. Create the specs/ directory if it doesn't exist.
Spike: [name]
Question
[The specific question this spike was answering]
Result
[Answered / Partially answered / Not answered]
Findings
[What you learned — concrete observations, not opinions]
Evidence
[Code snippet, benchmark result, API response, or screenshot that proves the finding]
Implications for the plan
[How this changes the approach, the design, or the estimate]
What was NOT explored
[Known gaps — things the spike didn't validate]
Recommendation
[Should we proceed with this approach? If yes, what does plan-work need to account for?]
5. Delete the spike code
After writing the findings, delete or discard the spike code. It is not meant to ship.
6. Feed back into plan-work
The spike findings are the input to plan-work. Call plan-work next, informed by specs/archive/spikes/SPIKE-<name>.md.
Signals
- GitHub stars
- 248
- Forks
- 19
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
spike-prototype- Source
- github.com/danielvm-git/bigpowers