--- name: propose-hypotheses-b364176753cb description: Execute complete FPF cycle from hypothesis generation to decision argument-hint: "[problem-statement]" allowed-tools: Task, Read, Write, Bash, AskUserQuestion --- # 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: **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: **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 File: .fpf/knowledge/L0/.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 File: .fpf/knowledge/L1/.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 File: .fpf/knowledge/L2/.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**: **Rationale**: ``` **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 --- Source attribution: NeoLabHQ Original project: https://github.com/NeoLabHQ/context-engineering-kit License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: NeoLabHQ Original project: https://github.com/NeoLabHQ/context-engineering-kit Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Data & Analytics, Product Management Roles: Data Analyst, Market Research Analyst, Product Manager Specialties: API Tools: Sonnet Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 5153a2587f25eef6f36fb972842ad0bf50edbf95c63917b06e7285c6d36b3c2a