Spike

Spike

Use this skill when the user wants to feel out an idea before committing to a real build — validating feasibility, comparing approaches, or surfacing unknowns that no amount of research will answer. Spikes are disposable by design. Throw them away once they've paid their debt.

Load this when the user says things like "let me try this", "I want to see if X works", "spike this out", "before I commit to Y", "quick prototype of Z", "is this even possible?", or "compare A vs B".

When NOT to use this

• The answer is knowable from docs or reading code — just do research, don't build
• The work is production path — use plan instead
• The idea is already validated — jump straight to implementation
• The question is "is this the right problem?" not "is this technically possible?" — use problem-framing-audit

Core method

Regardless of scale, every spike follows this loop:

decompose  →  research  →  build  →  verdict
   ↑__________________________________________↓
                  iterate on findings

1. Decompose

Break the user's idea into 2-5 independent feasibility questions. Each question is one spike. Present them as a table with Given/When/Then framing:

Spike types:

• standard — one approach answering one question
• comparison — same question, different approaches (shared number, letter suffix a/b/c)

Good spike questions: specific feasibility with observable output. Bad spike questions: too broad, no observable output, or just "read the docs about X".

Order by risk. The spike most likely to kill the idea runs first. No point prototyping the easy parts if the hard part doesn't work.

Skip decomposition only if the user already knows exactly what they want to spike and says so. Then take their idea as a single spike.

2. Align (for multi-spike ideas)

Present the spike table. Ask: "Build all in this order, or adjust?" Let the user drop, reorder, or re-frame before you write any code.

3. Research (per spike, before building)

Spikes are not research-free — you research enough to pick the right approach, then you build. Per spike:

1. Brief it. 2-3 sentences: what this spike is, why it matters, key risk.

2. Surface competing approaches if there's real choice:

   Approach	Tool/Library	Pros	Cons	Status
   ...	...	...	...	maintained / abandoned / beta

3. Pick one. State why. If 2+ are credible, build quick variants within the spike.

4. Skip research for pure logic with no external dependencies.

Use the workspace's web/search tools for the research step — find candidates, fetch docs, check installed versions in the project venv.

For libraries without docs pages, clone and read their README.md / examples/ directly. If the workspace has Microsoft Learn / Context7 / other MCP docs servers configured, use those.

4. Build

One directory per spike. Keep it standalone.

spikes/
├── 001-websocket-streaming/
│   ├── README.md
│   └── main.py
├── 002a-pdf-parse-pdfjs/
│   ├── README.md
│   └── parse.js
└── 002b-pdf-parse-camelot/
    ├── README.md
    └── parse.py

Bias toward something the user can interact with. Spikes fail when the only output is a log line that says "it works." The user wants to feel the spike working. Default choices, in order of preference:

1. A runnable CLI that takes input and prints observable output
2. A minimal HTML page that demonstrates the behavior
3. A small web server with one endpoint
4. A unit test that exercises the question with recognizable assertions

Depth over speed. Never declare "it works" after one happy-path run. Test edge cases. Follow surprising findings. The verdict is only trustworthy when the investigation was honest.

Avoid unless the spike specifically requires it: complex package management, build tools/bundlers, Docker, env files, config systems. Hardcode everything — it's a spike.

Parallel comparison spikes (002a / 002b) — delegate. When two approaches can run in parallel and both need real engineering (not 10-line prototypes), fan out to worker subagents if the session has any loaded. Each subagent returns its own verdict; you write the head-to-head.

5. Verdict

Each spike's README.md closes with:

## Verdict: VALIDATED | PARTIAL | INVALIDATED

### What worked
- ...

### What didn't
- ...

### Surprises
- ...

### Recommendation for the real build
- ...

VALIDATED = the core question was answered yes, with evidence. PARTIAL = it works under constraints X, Y, Z — document them. INVALIDATED = doesn't work, for this reason. This is a successful spike.

Comparison spikes

When two approaches answer the same question (002a / 002b), build them back to back, then do a head-to-head comparison at the end:

## Head-to-head: pdfjs vs camelot

| Dimension | pdfjs (002a) | camelot (002b) |
|---|---|---|
| Extraction quality | 9/10 structured | 7/10 table-only |
| Setup complexity | npm install, 1 line | pip + ghostscript |
| Perf on 100-page PDF | 3s | 18s |
| Handles rotated text | no | yes |

**Winner:** pdfjs for our use case. Camelot if we need table-first extraction later.

Frontier mode (picking what to spike next)

If spikes already exist and the user says "what should I spike next?", walk the existing directories and look for:

• Integration risks — two validated spikes that touch the same resource but were tested independently
• Data handoffs — spike A's output was assumed compatible with spike B's input; never proven
• Gaps in the vision — capabilities assumed but unproven
• Alternative approaches — different angles for PARTIAL or INVALIDATED spikes

Propose 2-4 candidates as Given/When/Then. Let the user pick.

Output

• Create spikes/ in the repo root
• One dir per spike: NNN-descriptive-name/
• README.md per spike captures question, approach, results, verdict
• Keep the code throwaway — a spike that takes 2 days to "clean up for production" was a bad spike

Related

• plan — once a spike validates, plan the real build
• problem-framing-audit — frame audit BEFORE spiking; spike answers feasibility, frame answers right-problem
• critical-thinking — the verdict honesty test (no "it works" without evidence) is critical thinking applied to spikes

Would Revise If

• Event-based: zero spikes/ directories created across the fleet within 90 days; OR all spike verdicts are PARTIAL/INVALIDATED with no VALIDATED instances (skill is being used but the decompose step is mis-sized — questions too ambitious). Sunset or revise the decompose-step guidance.
• Date-based: 2026-09-07 (90 days from adoption). If by then spike is invoked but heirs report the decomposition step (2-5 feasibility questions) consistently feels too heavy for their work shape, simplify the table-driven approach.
• Counter-evidence: if spikes graduate to production (the "throwaway" discipline is violated ≥3 times), tighten the "disposable by design" framing or carve out a "spike-to-production" sibling skill.

Attribution

Adapted from Hermes Agent (Nous Research, MIT), which adapted the workflow from the GSD (Get Shit Done) project (MIT © Lex Christopherson). The full GSD system offers persistent spike state and integration with a broader spec-driven development pipeline; this is the lightweight standalone version.