/spike

name: spike
description: "Throwaway experiments to validate an idea before build."
version: 1.0.0
author: Hermes Agent (adapted from gsd-build/get-shit-done)
license: MIT
metadata:
  hermes:
    tags: [spike, prototype, experiment, feasibility, throwaway, exploration, research, planning, mvp, proof-of-concept]
    related_skills: [sketch, writing-plans, subagent-driven-development, plan]
origin: adapted
source_repo: gsd-build/get-shit-done
source_url: https://github.com/gsd-build/get-shit-done
source_license: MIT
language: en

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 `writing-plans` / `plan` instead
• The idea is already validated — jump straight to implementation

If the user has the full GSD system installed

If `gsd-spike` shows up as a sibling skill (installed via `npx get-shit-done-cc --hermes`), prefer **`gsd-spike`** when the user wants the full GSD workflow: persistent `.planning/spikes/` state, MANIFEST tracking across sessions, Given/When/Then verdict format, and commit patterns that integrate with the rest of GSD. This skill is the lightweight standalone version for users who don't have (or don't want) the full system.

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 | Validates (Given/When/Then) | Risk | |---|-------|----------------------------|------| | 001 | websocket-streaming | Given a WS connection, when LLM streams tokens, then client receives chunks < 100ms | High | | 002a | pdf-parse-pdfjs | Given a multi-page PDF, when parsed with pdfjs, then structured text is extractable | Medium | | 002b | pdf-parse-camelot | Given a multi-page PDF, when parsed with camelot, then structured text is extractable | Medium |

**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 Hermes tools for the research step:

• `web_search("python websocket streaming libraries 2025")` — find candidates
• `web_extract(urls=["https://websockets.readthedocs.io/..."])` — read the actual docs (returns markdown)
• `terminal("pip show websockets | grep Version")` — check what's installed in the project's venv

For libraries without docs pages, clone and read their `README.md` / `examples/` via `read_file`. Context7 MCP (if the user has it configured) is also a good source — `mcp_*_resolve-library-id` then `mcp_*_query-docs`.

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.

**Building one spike** — a typical tool sequence:

terminal("mkdir -p spikes/001-websocket-streaming")
write_file("spikes/001-websocket-streaming/README.md", "# 001: websocket-streaming\n\n...")
write_file("spikes/001-websocket-streaming/main.py", "...")
terminal("cd spikes/001-websocket-streaming && python3 main.py")
# Observe output, iterate.

**Parallel comparison spi