
Propose Hypotheses
- 880 installs
- 1.3k repo stars
- Updated August 3, 2026
- neolabhq/context-engineering-kit
propose-hypotheses is a Claude Code workflow skill that executes a full First Principles Framework cycle to turn vague problem statements into competing hypotheses, evidence folders, and traceable decisions for developer
About
propose-hypotheses is a context-engineering-kit skill that runs the complete First Principles Framework (FPF) cycle from a single problem-statement argument. The workflow creates a `.fpf/` directory tree with `evidence`, `decisions`, `sessions`, and `knowledge` layers including L0, L1, L2, and invalid buckets, then generates competing hypotheses, verifies logic, validates evidence, audits trust, and records a decision. Developers reach for propose-hypotheses when architectural or product choices are underspecified and need documented reasoning rather than gut calls. The skill uses Task, Read, Write, Bash, and AskUserQuestion tools, making it suited to agent-led discovery sessions that must leave auditable artifacts behind.
- Executes complete FPF cycle: generate competing hypotheses, verify logic, validate evidence, audit trust, produce decisi
- Creates persistent .fpf/ directory scaffold with evidence, decisions, sessions, and knowledge layers L0-L2
- Launches specialized fpf-agent sessions using Claude 3.5 Sonnet for context initialization and hypothesis generation
- Writes structured outputs to .fpf/context.md and .fpf/knowledge/L0/ hypothesis files
- Supports full workflow from problem statement to decision with built-in trust auditing
Propose Hypotheses by the numbers
- 880 all-time installs (skills.sh)
- +27 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #548 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 880 |
|---|---|
| repo stars | ★ 1.3k |
| Security audit | 3 / 3 scanners passed |
| Last updated | August 3, 2026 |
| Repository | neolabhq/context-engineering-kit ↗ |
How do you structure hypotheses before technical decisions?
Run a structured First Principles Framework cycle that turns any vague problem statement into competing hypotheses, evidence folders, and a traceable decision.
Who is it for?
Developers and tech leads scoping ambiguous architecture, product, or engineering problems who need evidence-backed decision trails.
Skip if: Straightforward bug fixes or tasks with a single obvious implementation path that do not benefit from formal hypothesis competition.
When should I use this skill?
A developer provides a vague problem statement and needs structured hypothesis generation, evidence validation, and a recorded decision.
What you get
`.fpf/` directories with evidence, sessions, knowledge tiers, and a documented decision.
- .fpf/evidence files
- .fpf/decisions decision record
By the numbers
- Creates four top-level `.fpf/` areas: evidence, decisions, sessions, knowledge
- Knowledge layer includes three validity tiers: L0, L1, L2, plus invalid
Files
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
Problem Statement: $ARGUMENTSWorkflow Execution
Step 1a: Create Directory Structure (Main Agent)
Create .fpf/ directory structure if it does not exist:
mkdir -p .fpf/{evidence,decisions,sessions,knowledge/{L0,L1,L2,invalid}}
touch .fpf/{evidence,decisions,sessions,knowledge/{L0,L1,L2,invalid}}/.gitkeepPostcondition: .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: <hypothesis-id>"
- 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: <hypothesis-id>"
- 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: <hypothesis-id>"
- 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 linkWait 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: $ARGUMENTS
L2 Hypotheses Directory: .fpf/knowledge/L2/
Audit Reports: .fpf/evidence/
**Write**: Decision record to `.fpf/decisions/`
**Reply**: with decision record summary in markdown format:
| Hypothesis | R_eff | Weakest Link | Status |
|------------|-------|--------------|--------|
| ... | ... | ... | ... |
**Recommended Decision**: <hypothesis title>
**Rationale**: <brief explanation>Wait for agent, then check that decision record is created in .fpf/decisions/. ---
Step 9: Present Final Summary (Main Agent)
1. Read the DRR from .fpf/decisions/ 2. Present results from agent response. 3. Present next steps:
- Implement the selected hypothesis
- Use
/fpf:statusto check FPF state - Use
/fpf:actualizeif codebase changes
4. Ask user if he agree with the decision, if not launch fpf-agent at step 8 with instruction to modify the decision as user wants.
---
Completion
Workflow complete when:
- [ ]
.fpf/directory structure exists - [ ] Context recorded in
.fpf/context.md - [ ] Hypotheses generated, verified, validated, and audited
- [ ] DRR created in
.fpf/decisions/ - [ ] Final summary presented to user
Artifacts Created:
.fpf/context.md- Problem context.fpf/knowledge/L0/*.md- Initial hypotheses.fpf/knowledge/L1/*.md- Verified hypotheses.fpf/knowledge/L2/*.md- Validated hypotheses.fpf/knowledge/invalid/*.md- Rejected hypotheses.fpf/evidence/*.md- Evidence files.fpf/decisions/*.md- Design Rationale Record
Related skills
How it compares
Pick propose-hypotheses over informal brainstorming when you need foldered evidence and an auditable decision trail, not just a list of ideas.
FAQ
What directory does propose-hypotheses create?
propose-hypotheses creates a `.fpf/` tree with `evidence`, `decisions`, `sessions`, and `knowledge` subfolders including L0, L1, L2, and invalid tiers for classified findings.
What input does propose-hypotheses require?
propose-hypotheses accepts a problem statement via `$ARGUMENTS`, then executes the full FPF cycle from hypothesis generation through evidence validation to a final decision.
Is Propose Hypotheses safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.