spike — скилл для Claude Code: что делает и как установить
Скилл для одноразовых экспериментов, которые проверяют идею до начала основной разработки. Строит прототипы, сравнивает подходы и документирует выводы.
npx skills add NousResearch/hermes-agent --skill spike -a claude-codeЧто делает spike
Spike разбивает идею на 2–5 вопросов осуществимости, упорядочивает их по риску и создаёт отдельную папку для каждого эксперимента внутри spikes/. Для каждого прототипа скилл проводит исследование библиотек и инструментов, затем пишет runnable CLI, минимальную HTML-страницу или unit-тест, чтобы пощупать поведение вживую.
Спайк завершается вердиктом: VALIDATED, PARTIAL или INVALIDATED — с перечислением того, что сработало, что нет, и рекомендаций для настоящей сборки. Для параллельного сравнения подходов (например, pdfjs против camelot) делегирует задачи субагентам. Скилл работает в Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI и OpenCode. Hardcode всё: никаких Docker, env-файлов и сборщиков.
Какие задачи решает
- проверить, осуществима ли идея, прежде чем писать production-код
- сравнить два конкурирующих подхода лицом к лицу
- выявить неизвестные, на которые документация не отвечает
- потестить крайние случаи в контролируемой среде
- задокументировать находки и рекомендации для финальной сборки
Примеры запросов агенту
- Сделай спайк: проверь, можно ли стримить WebSocket-ответы быстрее 100 мс
- Сравни pdfjs и camelot для парсинга многостраничного PDF
- Покажи, возможно ли это вообще, прежде чем я возьмусь строить
Как установить spike
1Через Skills CLI (Vercel)
npx skills add NousResearch/hermes-agent --skill spike -a claude-code2Вручную: положить файлы
~/.claude/skills/spike/SKILL.mdПапка скилла целиком; для одного проекта — .claude/skills/spike/
1Через Skills CLI (Vercel)
npx skills add NousResearch/hermes-agent --skill spike -a codex2Вручную: положить файлы
.agents/skills/spike/SKILL.mdДля всех проектов — ~/.agents/skills/
1Через Skills CLI (Vercel)
npx skills add NousResearch/hermes-agent --skill spike -a cursor2Вручную: положить файлы
.cursor/skills/spike/SKILL.mdCursor читает и .agents/skills/, и .claude/skills/
1GitHub CLI
gh skill install NousResearch/hermes-agent spike2Вручную: положить файлы
.github/skills/spike/SKILL.md1Через Skills CLI (Vercel)
npx skills add NousResearch/hermes-agent --skill spike -a gemini-cli2Командой платформы
gemini skills install https://github.com/NousResearch/hermes-agent --path skills/software-development/spikeВручную: положить файлы
.devin/skills/spike/SKILL.mdСтарый путь .windsurf/skills/ тоже читается
Через Skills CLI (Vercel)
npx skills add NousResearch/hermes-agent --skill spike -a opencodeClaude.ai
Упакуйте папку в zip и загрузите в Settings → Capabilities → Skills
Из того же набора42
Весь набор →Похожие
Вопросы о spike
Как установить spike в Claude Code?
Запусти npx skills add NousResearch/hermes-agent --skill spike -a claude-code. Скилл появится в ~/.claude/skills/spike/SKILL.md.
Что делает spike?
Spike строит одноразовые прототипы, которые проверяют осуществимость идеи. Разбивает задачу на вопросы, исследует подходы, пишет код и выдаёт вердикт с рекомендациями для production-сборки.
Когда использовать spike, а когда сразу писать код?
Spike нужен, когда ответ нельзя найти в документации и риск провала высок. Если идея уже проверена или задача точно решаема — пиши сразу. Для production-пути используй скилл plan.
Исходный текст
SKILL.md · MIT
---
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
platforms: [linux, macos, windows]
metadata:
hermes:
tags: [spike, prototype, experiment, feasibility, throwaway, exploration, research, planning, mvp, proof-of-concept]
related_skills: [sketch, subagent-driven-development]
---
# 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 the `plan` skill 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 && python main.py")
# Observe output, iterate.
```
**Parallel comparison spikes (002a / 002b) — delegate.** When two approaches can run in parallel and both need real engineering (not 10-line prototypes), fan out with `delegate_task`:
```
delegate_task(tasks=[
{"goal": "Build 002a-pdf-parse-pdfjs: ...", "toolsets": ["terminal", "file", "web"]},
{"goal": "Build 002b-pdf-parse-camelot: ...", "toolsets": ["terminal", "file", "web"]},
])
```
Each subagent returns its own verdict; you write the head-to-head.
### 5. Verdict
Each spike's `README.md` closes with:
```markdown
## 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:
```markdown
## 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/` (or `.planning/spikes/` if the user is using GSD conventions) 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
## Attribution
Adapted from the GSD (Get Shit Done) project's `/gsd-spike` workflow — MIT © 2025 Lex Christopherson ([gsd-build/get-shit-done](https://github.com/gsd-build/get-shit-done)). The full GSD system offers persistent spike state, MANIFEST tracking, and integration with a broader spec-driven development pipeline; install with `npx get-shit-done-cc --hermes --global`.
© Hermes Agent (adapted from gsd-build/get-shit-done) · MIT · NousResearch/hermes-agent