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Propose Hypotheses

OrganizationPopular
NeoLabHQ
propose-hypotheses

Execute complete FPF cycle from hypothesis generation to decision

Overview

PublisherNeoLabHQ
Repositorycontext-engineering-kit
Skill namepropose-hypotheses
Stars
1.7K
Forks
159
Bundled files
Instructions only
LicenseGPL-3.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by NeoLabHQ on GitHub. Read the source before you install it.

Installation

Install the Propose Hypotheses 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.
  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/NeoLabHQ/context-engineering-kit.git /tmp/context-engineering-kit
mkdir -p .claude/skills
cp -r /tmp/context-engineering-kit/antigravity/skills/propose-hypotheses .claude/skills/propose-hypotheses
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Propose Hypotheses 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 Propose Hypotheses 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 Propose Hypotheses 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.

Propose Hypotheses Workflow

Execute the First Principles Framework (FPF) cycle: generate competing hypotheses, verify logic, validate evidence, audit trust, and produce a decision.

User Input

text
Problem Statement: $ARGUMENTS

Workflow Execution

Step 1a: Create Directory Structure (Main Agent)

Create .fpf/ directory structure if it does not exist:

bash
mkdir -p .fpf/{evidence,decisions,sessions,knowledge/{L0,L1,L2,invalid}}
touch .fpf/{evidence,decisions,sessions,knowledge/{L0,L1,L2,invalid}}/.gitkeep

Postcondition: .fpf/ directory scaffold exists.


Step 1b: Initialize Context (FPF Agent)

Launch fpf-agent with sonnet[1m] model:

  • Description: "Initialize FPF context"
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/init-context.md and execute.
    
    Problem Statement: $ARGUMENTS
    
    **Write**: Context summary to `.fpf/context.md`**

Step 2: Generate Hypotheses (FPF Agent)

Launch fpf-agent with sonnet[1m] model:

  • Description: "Generate L0 hypotheses"
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/generate-hypotheses.md and execute.
    
    Problem Statement: $ARGUMENTS
    Context: <summary from Step 1b>
    
    **Write**: List of hypothesis IDs and titles to `.fpf/knowledge/L0/`
    
    Reply with summary table in markdown format:
    
      | ID | Title | Kind | Scope |
      |----|-------|------|-------|
      | ... | ... | ... | ... |

Step 3: Present Summary (Main Agent)

  1. Read all L0 hypothesis files from .fpf/knowledge/L0/
  2. Present summary table from agent response.
  3. Ask user: "Would you like to add any hypotheses of your own? (yes/no)"

Step 4: Add User Hypothesis (FPF Agent, Conditional Loop)

Condition: User says yes to adding hypotheses.

Launch fpf-agent with sonnet[1m] model:

  • Description: "Add user hypothesis"
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/add-user-hypothesis.md and execute.
    
    User Hypothesis Description: <get from user>
    
    **Write**: User hypothesis to `.fpf/knowledge/L0/`

Loop: Return to Step 3 after hypothesis is added.

Exit: When user says no or declines to add more.


Step 5: Verify Logic (Parallel Sub-Agents)

Condition: User finished adding hypotheses.

For EACH L0 hypothesis file in .fpf/knowledge/L0/, launch parallel fpf-agent with sonnet[1m] model:

  • Description: "Verify hypothesis: "
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/verify-logic.md and execute.
    
    Hypothesis ID: <hypothesis-id>
    Hypothesis File: .fpf/knowledge/L0/<hypothesis-id>.md
    
    **Move**: After you complete verification, move the file to `.fpf/knowledge/L1/` or `.fpf/knowledge/invalid/`.

Wait for all agents, then check that files are moved to .fpf/knowledge/L1/ or .fpf/knowledge/invalid/.


Step 6: Validate Evidence (Parallel Sub-Agents)

For EACH L1 hypothesis file in .fpf/knowledge/L1/, launch parallel fpf-agent with sonnet[1m] model:

  • Description: "Validate hypothesis: "
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/validate-evidence.md and execute.
    
    Hypothesis ID: <hypothesis-id>
    Hypothesis File: .fpf/knowledge/L1/<hypothesis-id>.md
    
    **Move**: After you complete validation, move the file to `.fpf/knowledge/L2/` or `.fpf/knowledge/invalid/`.

Wait for all agents, then check that files are moved to .fpf/knowledge/L2/ or .fpf/knowledge/invalid/.


Step 7: Audit Trust (Parallel Sub-Agents)

For EACH L2 hypothesis file in .fpf/knowledge/L2/, launch parallel fpf-agent with sonnet[1m] model:

  • Description: "Audit trust: "
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/audit-trust.md and execute.
    
    Hypothesis ID: <hypothesis-id>
    Hypothesis File: .fpf/knowledge/L2/<hypothesis-id>.md
    
    **Write**: Audit report to `.fpf/evidence/audit-{hypothesis-id}-{YYYY-MM-DD}.md`
    
    **Reply**: with R_eff score and weakest link

Wait for all agents, then check that audit reports are created in .fpf/evidence/.


Step 8: Make Decision (FPF Agent)

Launch fpf-agent with sonnet[1m] model:

  • Description: "Create decision record"
  • Prompt:
    Read ${CLAUDE_PLUGIN_ROOT}/tasks/decide.md and execute.
    
    Problem Statement: $ARGUMENTS
    L2 Hypotheses Directory: .fpf/knowledge/L2/
    Audit Reports: .fpf/evidence/
    
    **Write**: Decision record to `.fpf/decisions/`
    
    **Reply**: with decision record summary in markdown format:
    
    | Hypothesis | R_eff | Weakest Link | Status |
    |------------|-------|--------------|--------|
    | ... | ... | ... | ... |
    
    **Recommended Decision**: <hypothesis title>
    
    **Rationale**: <brief explanation>

Wait for agent, then check that decision record is created in .fpf/decisions/.

Step 9: Present Final Summary (Main Agent)

  1. Read the DRR from .fpf/decisions/
  2. Present results from agent response.
  3. Present next steps:
    • Implement the selected hypothesis
    • Use /fpf:status to check FPF state
    • Use /fpf:actualize if codebase changes
  4. Ask user if he agree with the decision, if not launch fpf-agent at step 8 with instruction to modify the decision as user wants.

Completion

Workflow complete when:

  • .fpf/ directory structure exists
  • Context recorded in .fpf/context.md
  • Hypotheses generated, verified, validated, and audited
  • DRR created in .fpf/decisions/
  • Final summary presented to user

Artifacts Created:

  • .fpf/context.md - Problem context
  • .fpf/knowledge/L0/*.md - Initial hypotheses
  • .fpf/knowledge/L1/*.md - Verified hypotheses
  • .fpf/knowledge/L2/*.md - Validated hypotheses
  • .fpf/knowledge/invalid/*.md - Rejected hypotheses
  • .fpf/evidence/*.md - Evidence files
  • .fpf/decisions/*.md - Design Rationale Record

Frequently asked questions

What does the Propose Hypotheses AI skill do?

Execute complete FPF cycle from hypothesis generation to decision

Why use Propose Hypotheses on TypingMind?

Because you install it once and use it with any model. Propose Hypotheses 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 Propose Hypotheses in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/propose-hypotheses. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Propose Hypotheses?

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 Propose Hypotheses?

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

Is the Propose Hypotheses AI skill free?

Yes. It is published on GitHub by NeoLabHQ under the GPL-3.0 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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