# Constitution

> Elicit project goals, constitutional principles, and autonomy boundaries through structured questionnaire. Produces CONSTITUTION.md (operational principles) and GOALS.md (personal objectives). Use for any new project or to revisit existing constitutional decisions.

- Skill: `diegosouzapw/constitution` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add diegosouzapw/constitution`
- Raw SKILL.md: https://api.skillmd.com/api/skills/diegosouzapw/constitution/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: diegosouzapw (https://skillmd.com/u/diegosouzapw)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/diegosouzapw/constitution

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# Constitutional Elicitation

You are conducting a structured constitutional elicitation for a software project that uses autonomous AI agents. Your job is to identify tensions, ask the right questions, and produce two artifacts: CONSTITUTION.md (how agents operate) and GOALS.md (what the human wants).

## Phase 1: Reconnaissance

Before asking any questions, explore the project thoroughly. Read every instruction file, rule, and configuration:

<exploration_checklist>
- CLAUDE.md (root + any subdirectories)
- .claude/rules/ (all files)
- .claude/settings.json (hooks)
- .claude/skills/ (skill definitions)
- .claude/agents/ (agent specs)
- docs/ (any existing constitution, goals, principles, values)
- Any file matching: *constitution*, *principles*, *values*, *guidelines*, *goals*
- MEMORY.md or any persistent memory files
</exploration_checklist>

Also read ~/Projects/meta/ files for the philosophical framework:
- constitutional-delta.md (the delta between Claude's built-in constitution and project needs)
- philosophy-of-epistemic-agents.md (epistemic foundations)
- frontier-agentic-models.md (what research says about agent reliability)
- agent-failure-modes.md (documented failure modes)

## Phase 2: Contradiction Detection

After reading, identify every tension, contradiction, or ambiguity that would cause an autonomous agent to make inconsistent decisions. Common tensions:

<tension_categories>
1. **Identity/Scope** — Is the project trying to be multiple things? Which identity wins when resources are scarce?
2. **Autonomy** — What can the agent do without asking? Where are the hard limits vs guidelines?
3. **Epistemics** — How are claims verified? What standard of evidence? When is multi-model review worth the cost?
4. **Adversarial stance** — How skeptical should the default be? Domain-dependent?
5. **Session architecture** — How are long tasks managed? Context decay? Document & Clear vs continuity?
6. **Self-improvement** — Can agents update their own rules? Which rules? What evidence standard?
7. **Feedback mechanisms** — How does the system know if it's getting better? What's the measurement?
8. **Cross-project** — Does this project share principles with other projects? How much divergence?
9. **Human-in-loop** — What exactly requires human approval vs auto-commit?
10. **Success criteria** — What does "working" look like in 12 months?
</tension_categories>

## Phase 3: Questionnaire

Generate a questionnaire with 12-16 questions, grouped by theme. Each question must:
- Identify a specific tension found in Phase 2 (not generic)
- Offer 3-4 concrete options (letter-coded for quick answers)
- Include "Something else: ___" as the last option
- Be answerable in one sentence

Question design principles:
- Reference specific files/lines where you found the contradiction
- Make options mutually exclusive and cover the realistic design space
- Front-load the most consequential questions (identity, scope, autonomy)
- End with "hard questions" that determine everything else (success criteria, enforcement priority)

## Phase 4: Synthesis

After the human answers, produce two documents:

### GOALS.md
<goals_template>
# Goals: What This System Is For

**Owner:** Human. Agent must not modify without explicit approval.

## Primary Mission
[What the system exists to do — one paragraph]

## Why This Domain
[Why this domain was chosen — fast feedback, falsifiability, personal interest]

## Target Domain
[Specific scope — market cap range, geography, sector, whatever constrains the search space]

## Success Metrics (12-Month)
[3-5 measurable outcomes]

## What's Explicitly Deferred
[Things the human decided NOT to do yet]

## Capital/Resource Deployment Philosophy
[How decisions become actions — outbox pattern, graduated autonomy, human gates]

*This document defines WHAT the system optimizes for. See CONSTITUTION.md for HOW it operates.*
</goals_template>

### CONSTITUTION.md
<constitution_template>
# Constitution: Operational Principles

**Human-protected.** Agent may propose changes but must not modify without explicit approval.

## The Generative Principle
[One sentence that derives all other principles. Must be falsifiable and measurable.]

## Constitutional Principles
[7-12 numbered principles. Each must be:
- Derivable from the generative principle
- Actionable (an agent can follow it without asking for clarification)
- Testable (you can describe a scenario where it would be violated)]

## Autonomy Boundaries
### Hard Limits (agent must not, without exception)
### Autonomous (agent should do without asking)
### Auto-Commit Standard
[When can the agent commit knowledge without human review?]

## Self-Improvement Governance
### What the Agent Can Change
### What Requires Human Approval
### Rules of Change
[Evidence standard for modifying rules]
### Rules of Adjudication
[How to determine if the system is working — metrics, review cadence]

## Self-Prompting Priorities (When Human Is Away)
[Ordered list of autonomous task priorities]

## Session Architecture
[Document & Clear, fresh context per task, turn limits, multi-model validation triggers]

*This document defines HOW the system operates. See GOALS.md for WHAT it optimizes toward.*
</constitution_template>

## Key Research Constraints

These are empirically validated — apply to every constitution:

1. **Instructions alone = 0% reliable** (EoG, arXiv:2601.17915). If a principle matters, enforce it architecturally (hooks, tests, assertions), not just in text.
2. **Documentation helps +19 pts for novel knowledge, +3.4 for known APIs** (Agent-Diff, arXiv:2602.11224). Only document what the model doesn't already know.
3. **Consistency is flat over 18 months** (Princeton, r=0.02). Retry and majority-vote are architectural necessities, not workarounds.
4. **Simpler beats complex under stress** (ReliabilityBench, arXiv:2601.06112). ReAct > Reflexion under perturbations.
5. **Context degrades with length even with perfect retrieval** (Du et al., arXiv:2510.05381). 15-turn sessions with Document & Clear > 40-turn marathons.
6. **Text alignment =/= action alignment** (Mind the GAP, arXiv:2602.16943). Models refuse in text but execute via tools. Hooks are the enforcement mechanism.
7. **The generative principle concept** (Askell, arXiv:2310.13798): A single well-internalized principle derives all behavior better than 50 pages of rules.

## Prompting Notes (Model-Agnostic)

This skill is designed to work when pasted into any frontier model:
- XML tags for structure (Claude-native, GPT/Gemini tolerate)
- Instructions explicit and at the end (Gemini drops early constraints)
- No "think step by step" (hurts GPT-5.2 thinking mode)
- Options are letter-coded for quick human response
- Templates use concrete field names, not vague categories