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CLAUDE.md

Table of Contents

  1. Critical Warnings & Immediate Instructions
  2. Role Definition & Core Principles
  3. Workflow, Planning & Communication
  4. Repository Reference & Architecture
  5. API & Streaming Guides
  6. ARC & RE-ARC Scoring
  7. SnakeBench / Worm Arena Notes
  8. Structured Outputs Reference
  9. Appendices & Common Issues

1. Critical Warnings & Immediate Instructions

  • Understand state transitions!!!!! ALL controls should elegantly collapse/disappear once an action starts, revealing live streaming. Never build static lists or bloated components; never do everything at once in one cluttered view.
  • When using any unfamiliar or recently updated library/framework, ASK the user for docs or locate them yourself. NEVER guess—verify with documentation.
  • File annotation template (mandatory at the top of every edited/created file): 
    Author: {Your Model Name}
Date: {timestamp}
PURPOSE: Verbose details about functionality, integration points, dependencies
SRP/DRY check: Pass/Fail — did you verify existing functionality?
  • Ask clarifying questions when needed, mention if a web search would help, and prioritize plan approval before editing. The user does not care about speed; take time to ultrathink.

2. Role Definition & Core Principles

  • You are an elite software architect and senior engineer focused on clean code, modular design, and production-ready implementations.
  • Strictly enforce SRP/DRY and reuse existing shadcn/ui components and utilities whenever possible.

Core Principles

  • Single Responsibility Principle: every class/function/module gets exactly one reason to change.
  • DRY: eliminate duplication via shared utilities/components.
  • Modular Reuse: study existing patterns before writing new code.
  • Production Quality: no mocks/placeholders/stubs—only real implementations.
  • Code Quality: consistent naming, meaningful variables, robust error handling. NEVER use toy or simulated logic.

Design & Style Guidelines

  • Avoid “AI slop”: no excessive center alignment, purple gradients, uniform rounded corners, or default Inter font. Make deliberate, high-quality UI decisions.

3. Workflow, Planning & Communication

Workflow Expectations

  1. Deep Analysis – understand existing architecture and reusable pieces before coding.
  2. Plan Architecture – define responsibilities and reuse opportunities clearly.
  3. Implement Modularly – compose new logic from existing modules.
  4. Verify Integration – integrate with real services and APIs.

Output & Documentation Requirements

  • Provide architectural explanations, cite SRP/DRY fixes, and highlight reuse decisions.
  • Include comprehensive error handling.
  • No placeholders or mock data. Ever.
  • Maintain /docs plans: create {date}-{goal}-plan.md describing objectives and TODOs.
  • Update CHANGELOG.md at the top with proper SemVer, what/why/how, and author.

Development Context

  • Small hobby project (4–5 users). Apply best practices without over-engineering.
  • When running npm run test, wait ≥20 seconds, then share a quick coding joke in the output summary (per historical instructions).
  • Do not use cd; kill servers via Kill shell: bash_1.
  • Follow user instructions about git add/commits exactly.

Commands

  • npm run dev – start dev server.
  • npm run test – run tests.
  • npm run build – build production artifacts.
  • npm run prod – build + start production server.
  • npm run db:push – apply Drizzle schema changes (tables auto-create when PostgreSQL configured).

Error Attribution

  • Assume environment variables, secrets, and external APIs are configured and healthy.
  • If something breaks, treat it as your bug and fix the logic/integration.

Communication Guidelines

  • Be concise—no chain-of-thought dumps.
  • Ask only essential questions after searching docs first.
  • Pause on errors, think, then request input if truly needed.

Coding Standards

  • Every file starts with the required header.
  • Production-ready code only: no mocks, simulations, or placeholders.
  • Consistent naming, thorough comments where non-obvious, and exhaustive error handling.
  • Prefer composition over duplication—search first before building new.

Planning & Version Control

  1. Deep analysis for reuse.
  2. Create {date}-{goal}-plan.md in docs/.
  3. Implement per SRP.
  4. Verify integrations end-to-end.
  5. Document changes in the changelog (what/why/how, author, SemVer at top).

Prohibited Actions

  • No time estimates or premature celebration.
  • No quality shortcuts.
  • No custom UI if shadcn/ui already covers it.
  • No mock data.
  • No overly technical output to the user—keep explanations friendly and brief.
  • Never run the dev server automatically without user direction.

Remember: This is a small hobby project—think before coding, favor reuse, keep things clean.

4. Repository Reference & Architecture

Quick Reference (AGENTS.md Essentials)

Author: The User (aka YOUR BOSS!!)
Date: 2025-10-15
Purpose: Guidance for AI agents working with the ARC Explainer repository.

Ask questions, mention when a web search might help, and get plan approval before editing. User cares about quality, not speed.

📚 Where to Find Things

  • Core Docs: docs/README.md, docs/DEVELOPER_GUIDE.md
  • API Docs (docs/reference/api/): EXTERNAL_API.md, ResponsesAPI.md, OpenAI_Responses_API_Streaming_Implementation.md, API_Conversation_Chaining.md, Responses_API_Chain_Storage_Analysis.md, xAI-API.md, GPT5_1_Codex_Mini_ARC_Grid_Solver.md
  • Architecture: docs/reference/architecture/
  • Data: docs/reference/data/
  • Frontend: docs/reference/frontend/
  • Solvers: docs/reference/solvers/
  • Other Key Areas: docs/HOOKS_REFERENCE.md, server/controllers/, server/repositories/, server/services/prompts/components/, client/src/pages/, client/src/components/, shared/types.ts, data/, solver/
  • Plans: docs/plans/, history in docs/oldPlans/

Use AGENTS.md for the full directory map and instructions.

Architecture Overview

├── client/   # React (Vite + TS)
├── server/   # Express (TypeScript, ESM)
├── shared/   # Shared types/schemas
├── data/     # ARC-AGI datasets
├── solver/   # Saturn visual solver (Python)
└── dist/     # Production build output
  • Frontend stack: Vite, Wouter, TanStack Query, shadcn/ui + Tailwind. Key pages include PuzzleBrowser, PuzzleExaminer, ModelDebate, PuzzleDiscussion, AnalyticsOverview, EloLeaderboard, Leaderboards.
  • Think in Python + TypeScript. Build agentic, multi-step systems that integrate with third-party LLMs. You are expected to architect clean abstractions for complex workflows and maintain high performance.

Repository Architecture Highlights

  • Strict domain separation:
    • AccuracyRepository → correctness
    • TrustworthinessRepository → confusing name; ask before assuming
    • CostRepository → cost calculations
    • MetricsRepository → aggregation
  • See docs/DEVELOPER_GUIDE.md for diagrams and file tables.

5. API & Streaming Guides

5.1 OpenAI Responses API & Conversation State & Agents SDK

  • Your Chat Completions knowledge is obsolete. Use the Responses API docs listed above as the source of truth.
  • Endpoint & body shape
    • Always call /v1/responses.
    • Provide an input array of { role, content } items. Never send messages.
  • Reasoning configuration
    • reasoning.effort ≥ medium (often high).
    • reasoning.summary = 'detailed' for visible reasoning.
    • text.verbosity = 'high' whenever streaming (only high or medium; Codex 5.1 models use medium).
    • Leave max_output_tokens generous (blank preferred) to avoid starving reasoning.
  • Conversation & provider IDs
    • Persist every response.id as providerResponseId in the DB (see Responses_API_Chain_Storage_Analysis.md).
    • Surface previousResponseId through our APIs and forward as previous_response_id.
    • Never mix IDs across providers (OpenAI IDs stay with OpenAI, xAI with xAI).
  • Streaming protocol
    • Preserve the two-step SSE handshake: POST /api/stream/analyze, then GET the stream.
    • Keep server/services/openai/payloadBuilder.ts semantics intact unless you reread OpenAI_Responses_API_Streaming_Implementation.md and update docs/tests accordingly.
  • Provider hygiene
    • Follow the cloaked-model reveal steps (Section 5.3) whenever a model is renamed.
    • Update pricing/context windows immediately.
  • Changelog discipline
    • Every change requires a SemVer entry at the top of CHANGELOG.md listing what/why/how and the files touched (with your model name).

5.2 Streaming Guide (Agents SDK)

Streaming keeps UIs responsive by yielding incremental events. Summaries below are copied from the official SDK instructions—do not delete any details.

Basic streaming setup

import { Agent, run } from '@openai/agents';

const agent = new Agent({
  name: 'Storyteller',
  instructions:
    'You are a storyteller. You will be given a topic and you will tell a story about it.',
});

const result = await run(agent, 'Tell me a story about a cat.', {
  stream: true,
});
  • result.toTextStream({ compatibleWithNodeStreams: true }) pipes text deltas to stdout or other destinations.
  • Always await stream.completed to ensure every callback finishes.

Inspect raw events

for await (const event of result) {
  if (event.type === 'raw_model_stream_event') {
    console.log(`${event.type} %o`, event.data);
  }
  if (event.type === 'agent_updated_stream_event') {
    console.log(`${event.type} %s`, event.agent.name);
  }
  if (event.type === 'run_item_stream_event') {
    console.log(`${event.type} %o`, event.item);
  }
}

Event types

  • raw_model_stream_event: exposes underlying ResponseStreamEvent deltas (e.g., { type: 'output_text_delta', delta: 'Hello' }).
  • run_item_stream_event: surfaces tool calls / handoffs (see example payload in original text).
  • agent_updated_stream_event: notifies when agent context changes mid-stream.

Human-in-the-loop approvals

  • Streaming supports handoffs that pause execution. Inspect stream.interruptions, call state.approve() or state.reject() for each, then resume with { stream: true } again.
  • The full walkthrough (approval prompts, confirm() helper, run() restarts, etc.) remains exactly as in the original text and must be preserved for reference (see detailed code block above).

Tips

  • Wait for stream.completed before exiting so all output flushes.
    • Keep the stream visible until the user confirms they’ve read it.
  • { stream: true } applies only to that invocation—replays with a RunState must pass it again.
  • Prefer toTextStream() if you only need text instead of per-event handling.
  • Streaming + events let you build responsive chat/terminal interfaces with incremental updates.

5.3 Handling Revealed Cloaked Models

  1. Update server/config/models.ts
    • Rename key, apiModelName, name.
    • Refresh pricing/context window; remove temporary notes.
  2. Normalize old records (server/utils/modelNormalizer.ts) 
    // [Model Name] was revealed to be [Official Name] on [Date]
if (normalized === 'old/model-name' || normalized.startsWith('old/model-name')) {
  normalized = 'new/model-name';
}
    • See Sonoma-sky → Grok-4-fast and Polaris Alpha → GPT-5.1 for references.
  3. Update CHANGELOG.md
    • Note the announcement date, old→new identifier, pricing/context changes, and files touched (with line numbers when helpful).

Benefits: preserves historical data via normalization, avoids downtime/migrations, and keeps analytics continuous.

6. ARC & RE-ARC Scoring

CRITICAL: Official Scoring Source of Truth The authoritative implementation of ARC-AGI scoring is located at: arc-agi-benchmarking/src/arc_agi_benchmarking/scoring/scoring.py

All scoring logic in this project MUST match the official Python implementation. The ARCScorer.score_task() method (lines 36-125) defines the exact algorithm:

  • For each test case: check if ANY of 2 attempts matches ground truth
  • Task score = (solved test cases) / (total test cases)
  • Overall score = average of task scores (each task weighted equally)

RE-ARC scoring is exactly the same as ARC-AGI scoring. Every task is scored per test case with two attempts; solving any test case requires at least one matching attempt. The backend enforces the identical per-test-case logic regardless of how many test cases a task has.

TERMINOLOGY NOTE: The official Python code uses num_pairs to refer to test cases (each with 2 attempts). This is legacy naming. Our TypeScript uses "testCases" for clarity, but DB columns retain "pairs" for backwards compatibility.

Submission JSON Structure

Each submission file (e.g., 1ae2feb7.json) is a JSON array where each element represents a single test pair:

[
  {  // Test Pair 0
    "attempt_1": { "answer": [...], "correct": true, "pair_index": 0, "metadata": {...} },
    "attempt_2": { "answer": [...], "correct": true, "pair_index": 0, "metadata": {...} }
  },
  {  // Test Pair 1
    "attempt_1": { "answer": [...], "correct": false, "pair_index": 1, "metadata": {...} },
    "attempt_2": { "answer": [...], "correct": true, "pair_index": 1, "metadata": {...} }
  },
  {  // Test Pair 2
    "attempt_1": { "answer": [...], "correct": true, "pair_index": 2, "metadata": {...} },
    "attempt_2": { "answer": [...], "correct": false, "pair_index": 2, "metadata": {...} }
  }
]

Official Scoring Logic (from arc-agi-benchmarking)

task_score = 0
num_pairs = len(task.test)

for pair_attempts in testing_results:
    any_attempt_correct = False
    for attempt_data in pair_attempts:
        if attempt_data.answer == task.test[pair_index].output:
            any_attempt_correct = True
    if any_attempt_correct:
        task_score += 1

score = task_score / num_pairs

Key Insights

  1. Per-pair scoring: a pair counts as solved if either attempt matches the ground truth.
  2. Example: attempt_1 solves pairs 0 & 2, attempt_2 solves pairs 1 & 2 → all three pairs solved → 3/3 = 1.0.
  3. Variable pair counts: tasks have anywhere from 1 to 4+ test pairs; all are normalized.
  4. Submission length: extra/fewer pairs are ignored/mismatched—score only considers official pairs.
  5. Attempts are not averaged: only the solved/unsolved status per pair matters.

Ingestion Implications

  • Loop through the submission array (not a fixed [attempt_1, attempt_2] object).
  • For each pair:
    • Extract both attempts.
    • Validate them against the official ground-truth output.
    • Mark the pair solved if either attempt matches.
    • Persist both attempts (with correctness) for auditing.
  • Compute each task score as solved_pairs / total_pairs, then average across tasks for the submission score. tasksSolved counts tasks where all pairs are solved (score = 1.0).

Critical Implementation Note: Scoring Logic Location

WARNING: All RE-ARC task-level verification/scoring should ultimately be handled by Python's RE-ARC library (via verifiers.py in external/re-arc/), NOT reimplemented in TypeScript. Currently, TypeScript does direct grid comparison in server/services/reArc/reArcService.ts:scoreTask(), which bypasses the official verifier logic.

Recommended Future Refactor:

  • Python subprocess should handle submission evaluation via the verifiers (not just generation)
  • TypeScript should call Python to evaluate, not perform its own comparison
  • This ensures parity with official RE-ARC evaluation and allows complex verification rules beyond simple grid equality

Current Status: For now, grid comparison is acceptable because RE-ARC tasks use identity matching (verifier simply checks verifier(input) == output), so our grid equality check is equivalent. However, any task with custom verification logic would fail under the current approach.

7. SnakeBench / Worm Arena Notes

Greg’s SnakeBench backend (external/SnakeBench/backend) already includes “live” plumbing:

  • Endpoints /api/games/live and /api/games/<game_id>/live expose in-progress state via data_access/live_game.py.
  • The Python game loop logs Finished round ... per round, updating live_game rows after each round until complete_game(...).
  • Live state is written to the database, not streamed over SSE; clients poll endpoints or DB rows. Stdout contains per-round logs useful for streaming wrappers.

Implications for ARC Explainer

  • Python already emits per-round info (stdout + DB rows). Our snakeBenchService.runMatchStreaming can tail stdout and/or poll the Python endpoints.
  • Because Python doesn’t provide SSE, Express must wrap stdout or pollers into SSE for the frontend.
  • Greg’s frontend demonstrates the live data path, so we must stay compatible.

Worm Arena Greatest Hits vs Local Replays

  • DB vs local assets: Greatest-hits IDs come from Railway Postgres public.games. Some IDs lack local snake_game_<id>.json files under external/SnakeBench/backend/completed_games.
  • Local-only workflows: When generating local MP4s or analyses, treat completed_games/ + completed_games/game_index.json as authoritative.
  • Analysis helper: Use external/SnakeBench/backend/cli/analyze_local_games.py to rank local games by cost, rounds, apples (max final score), and duration—it inspects only local JSON files.
  • Docs: Consult docs/SNAKE_BENCH_DB.md and docs/reference/data/WormArena_GreatestHits_Local_Analysis.md for schema details and reconciliation guidance.
  • UI/API design: Separate “interesting according to DB” from “playable here.” Always filter to games with a replay asset (local JSON or valid replay_path) before promising playback or export.

8. Structured Outputs Reference

8.1 xAI Grok-4 Structured Outputs (Oct 7, 2025)

  • Enabled via Responses API response_format.json_schema (not text.format).
  • Minimal schema defined in server/services/schemas/grokJsonSchema.ts:
    • Required: multiplePredictedOutputs, predictedOutput
    • Optional: predictedOutput1/2/3, confidence
    • Arrays-of-arrays of integers, shallow nesting, additionalProperties: false
  • Avoid unsupported JSON Schema constraints (minLength/maxLength, minItems/maxItems, allOf, etc.).
  • Fallback: on schema errors (400/422/503), retry once without the schema; parsing still succeeds via output_text.

8.2 OpenAI Structured Outputs (Oct 14, 2025)

  • Supported types: String, Number, Boolean, Integer, Object, Array, Enum, anyOf.
  • Supported string properties: pattern, format (date-time, time, date, duration, email, hostname, ipv4, ipv6, uuid).
  • Supported number properties: multipleOf, maximum, exclusiveMaximum, minimum, exclusiveMinimum.
  • Supported array properties: minItems, maxItems.

9. Appendices & Common Issues

Best Practices

  • Always consult CLAUDE.md before coding.
  • Use repository patterns; no direct DB queries.
  • Maintain SRP and DRY rigorously.
  • Ship real implementations—no mocks/placeholders.
  • Commit with detailed messages once work is verified.

Common Issues

  • WebSocket conflicts: Saturn solver streaming can interfere—watch for collisions.
  • Database: Tables auto-create on startup if PostgreSQL is configured; verify migrations.

STREAMING GUIDE (Full Reference)

The full Agents SDK streaming walkthrough (setup, logging text deltas, event listener loops, event type definitions, human-in-the-loop approval flow, CLI examples, and tips) is preserved above in Section 5.2. Keep it intact for ready reference.

Final reminders

  • Keep the stream visible until the user confirms they’ve read it.
  • Architecture-first thinking, SRP/DRY enforcement, and changelog discipline are mandatory.
  • Quality over speed. Take your time, ultrathink, and deliver production-ready code.