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Prismer Spike

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Prismer-AI
prismer-spike

Throwaway experiments to validate an idea before build.

Overview

PublisherPrismer-AI
RepositoryPrismerCloud
Skill nameprismer-spike
Stars
1.6K
Forks
17
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Prismer-AI on GitHub. Read the source before you install it.

Installation

Install the Prismer Spike AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/cloud/catalog/skills/prismer-spike
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Prismer-AI/PrismerCloud.git /tmp/PrismerCloud
mkdir -p .claude/skills
cp -r /tmp/PrismerCloud/sdk/cloud/catalog/skills/prismer-spike .claude/skills/prismer-spike
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prismer Spike in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prismer Spike on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prismer Spike is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Prismer execution contract

This is the uniquely named prismer-spike skill, adapted from Hermes. Use the actual tools exposed by the executing host; examples using terminal, process, delegate_task, vision_analyze or browser_* are not tool registrations. Missing dependencies do not hide this skill. Report command startup, version, account/permissions and task-specific live verification separately. Use task-owned artifact paths and existing user authorization; do not change shared accounts, Runtime/provider configuration, global security settings or unrelated work. See NOTICE.md and LICENSE for resource provenance. Runtime availability and upstream-entry suppression are owned by the integration layer.

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:

#SpikeValidates (Given/When/Then)Risk
001websocket-streamingGiven a WS connection, when LLM streams tokens, then client receives chunks < 100msHigh
002apdf-parse-pdfjsGiven a multi-page PDF, when parsed with pdfjs, then structured text is extractableMedium
002bpdf-parse-camelotGiven a multi-page PDF, when parsed with camelot, then structured text is extractableMedium

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:

    ApproachTool/LibraryProsConsStatus
    ............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 only non-sensitive synthetic fixtures; never credentials or production endpoints — 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 when scope is unclear. Otherwise execute the user's already-authorized experiment without asking again.

Output

  • Create a unique task-owned scratch directory outside production source; do not overwrite an existing spikes/ tree
  • 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). The full GSD system offers persistent spike state, MANIFEST tracking, and integration with a broader spec-driven development pipeline. No global GSD installation is required or authorized by this skill.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prismer Spike AI skill do?

Throwaway experiments to validate an idea before build.

Why use Prismer Spike on TypingMind?

Because you install it once and use it with any model. Prismer Spike is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prismer Spike in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/cloud/catalog/skills/prismer-spike. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Prismer Spike?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prismer Spike?

As many as you like. As long as a model supports skills, you can use Prismer Spike with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prismer Spike AI skill free?

Yes. It is published on GitHub by Prismer-AI under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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