Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/rsmdt/the-startup/constitutionnpx skills add rsmdt/the-startup --skill constitutiongit clone --depth 1 https://github.com/rsmdt/the-startupWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00022 | $0.00667 |
| Opus 5 | $0.00011 | $0.00333 |
| Sonnet 5 | $0.00004 | $0.00133 |
| Haiku 4.5 | $0.00002 | $0.00067 |
Grade A, and why
constitution scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Persona
Act as a governance orchestrator that coordinates parallel pattern discovery to create project constitutions.
Focus Areas: $ARGUMENTS
Interface
Rule { level: L1 | L2 | L3 // Must (autofix) | Should (manual) | May (advisory) category: string // Security, Architecture, CodeQuality, Testing, or custom statement: string // the rule itself evidence: string // file:line references supporting the rule }
State { focusAreas = $ARGUMENTS perspectives = [] // from reference/perspectives.md existing: boolean discoveries: Rule[] }
Constraints
Always:
- Delegate all discovery to specialist agents.
- Launch all applicable discovery perspectives simultaneously in a single response.
- Discover actual codebase patterns before proposing rules.
- Present discovered rules for user approval before writing.
- Classify every rule with a level (L1/L2/L3).
- Every proposed rule must cite specific file:line evidence.
Never:
- Write constitution without user approval of proposed rules.
- Propose rules without codebase evidence.
- Skip discovery and generate generic rules.
Reference Materials
- reference/perspectives.md — discovery perspectives and focus area mapping
- reference/rule-patterns.md — level system, rule types, scope patterns
- reference/output-format.md — update mode options and presentation guidelines
- reference/scenarios.md — create, create with focus, and update scenarios
- examples/output-example.md — expected output format
- examples/CONSTITUTION.md — complete constitution example
- template.md — constitution template
Workflow
1. Check Existing
match (CONSTITUTION.md at project root) { exists => read and parse existing rules, route to update flow not found => read template.md, route to creation flow }
2. Discover Patterns
Read reference/perspectives.md. Select applicable perspectives based on $ARGUMENTS.
Launch parallel agents for each perspective. Each agent explores the codebase and returns proposed Rules with evidence.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 101 lines · 22 tokens per session scan A 34b5dfdc9880
constitution is a skill published in the GitHub repository rsmdt/the-startup (511 stars, last pushed 28d ago), licensed MIT. It adds 22 tokens to every session and 667 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
postgres-database-migration
Use this skill for planning, testing, and safely executing PostgreSQL schema migrations — especially when working with production data or shared databases. Trigger when user asks to: Test a schema migration before applying it to production Add, remove, or rename columns safely on a live table Change a column's data…
design-postgis-tables
Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications.
setup-timescaledb-hypertables
Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. Trigger when user asks to: Create or design SQL schemas/tables AND…
migrate-postgres-tables-to-hypertables
Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. Trigger when user asks to: Migrate or convert PostgreSQL tables to hypertables Execute hypertable migration with minimal downtime Plan blue-green migration for large tables Validate…
pgvector-semantic-search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. Trigger when user asks to: Store or search vector embeddings in PostgreSQL Set up semantic search, similarity search, or nearest neighbor search Create HNSW or IVFFlat indexes for vectors…
postgres-hybrid-text-search
Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). Trigger when user asks to: Combine keyword and semantic search Implement hybrid search or multi-modal retrieval Use BM25/pgtextsearch with pgvector together Implement RRF (Reciprocal…