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jeremylongshorejeremylongshore/stci-standard-inference-token-cost-index/CLAUDE.md

stci-standard-inference-token-cost-index

CLAUDE.md2 starsChanged 10 months agoMITView on GitHub

STCI (Standard Token Cost Index) is a vendor-neutral price index for LLM tokens. It collects pricing data from model providers, normalizes it to a canonical schema, and computes daily reference rates with full provenance. Observation: Single pricing data point with provenance - input_rate_usd_per_1m, output_rate_usd_per_1m: Normalized rates - source_tier: T1 (official) through T4 (community) - observation_id format: obs-{date}-{provider}-{model} Index: Aggregated reference rate (STCI-ALL, STCI-FRONTIER, STCI-EFFICIENT, STCI-OPEN) - blended_rate: Assumes 3:1 output:input ratio (configurable in methodology.yaml) - verification_hash: SHA256 for deterministic verification

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  1. Very specific:

    Next step: run /audit-tests to produce TEST_AUDIT.md.

    line 137

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CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

STCI (Standard Token Cost Index) is a vendor-neutral price index for LLM tokens. It collects pricing data from model providers, normalizes it to a canonical schema, and computes daily reference rates with full provenance.

Development Commands

# Setup
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Run tests
pytest                           # All tests
pytest tests/test_collector.py   # Single module
pytest -k "test_fetch"           # By pattern

# Verify repository structure
./scripts/verify_repo.sh

# Collection pipeline (fetch pricing data)
python -m services.collector.pipeline                    # Live from OpenRouter
python -m services.collector.pipeline --fixtures         # Use fixture data
python -m services.collector.pipeline --date 2026-01-01  # Specific date
python -m services.collector.pipeline --dry-run          # No file writes

# Indexing pipeline (compute indices)
python -m services.indexer.pipeline                      # Today's index
python -m services.indexer.pipeline --date 2026-01-01    # Specific date

# API server
python -m services.api.main                              # Default port 8000
python -m services.api.main --port 9000

# Docker
docker compose up api           # Run API server
docker compose run pipeline     # Run collector + indexer

Architecture

stci/
├── services/
│   ├── collector/          # Data collection from sources
│   │   ├── sources.py      # BaseSource, OpenRouterSource, FixtureSource
│   │   ├── collector.py    # Collector class with waterfall fallback
│   │   └── pipeline.py     # Full collection workflow: fetch → store → validate
│   ├── indexer/            # Index computation
│   │   ├── indexer.py      # Indexer class, compute_index()
│   │   └── pipeline.py     # Full indexing workflow: load → compute → store
│   ├── api/                # Read-only HTTP API (stdlib, no framework)
│   │   └── main.py         # STCIHandler, endpoints: /health, /v1/index/*
│   └── storage/            # Storage backends (GCS, local)
├── schemas/                # JSON Schema definitions
│   ├── observation.schema.json   # Pricing observation format
│   └── stci_daily.schema.json    # Daily index output format
├── data/
│   ├── fixtures/           # Test data and methodology config
│   │   ├── observations.sample.json
│   │   └── methodology.yaml
│   ├── raw/                # Immutable raw API responses
│   ├── observations/       # Normalized observations (JSONL by date)
│   └── indices/            # Computed indices (JSON by date)
└── 000-docs/               # Documentation (6767 naming convention)

Data Flow

OpenRouter API → raw/{date}.json → observations/{date}.jsonl → indices/{date}.json → API
     ↓                                      ↓
 sources.py                           indexer.py
  1. Collector fetches from sources (OpenRouter is primary, fixtures are fallback)
  2. Raw storage preserves original API responses for audit
  3. Observations are normalized to canonical schema (JSONL format)
  4. Indexer computes weighted averages per methodology
  5. API serves computed indices read-only

Key Concepts

Observation: Single pricing data point with provenance

  • input_rate_usd_per_1m, output_rate_usd_per_1m: Normalized rates
  • source_tier: T1 (official) through T4 (community)
  • observation_id format: obs-{date}-{provider}-{model}

Index: Aggregated reference rate (STCI-ALL, STCI-FRONTIER, STCI-EFFICIENT, STCI-OPEN)

  • blended_rate: Assumes 3:1 output:input ratio (configurable in methodology.yaml)
  • verification_hash: SHA256 for deterministic verification

Methodology (data/fixtures/methodology.yaml):

  • Defines index baskets, weighting, and thresholds
  • Equal weighting for MVP, usage-weighted planned

Testing

Tests use pytest with fixtures in tests/conftest.py. Key fixtures:

  • sample_observations: Pre-built observation list
  • sample_openrouter_response: Mock API response
  • temp_data_dir: Temporary data directory with sample files
  • target_date: Standard test date (2026-01-01)

Uses responses library to mock HTTP requests to OpenRouter API.

API Endpoints

EndpointDescription
GET /healthHealth check with data availability
GET /v1/index/latestMost recent index
GET /v1/index/{date}Index for specific date
GET /v1/indicesList all available dates
GET /v1/observations/{date}Raw observations
GET /v1/methodologyCurrent methodology config

Documentation Convention

Documents in 000-docs/ follow 6767 naming: NNN-CC-ABCD-description.md

  • NNN: Sequence number
  • CC: Category (PP=Product, AT=Architecture, DR=Design, etc.)
  • ABCD: Type code (PROD, ADEC, STND, SOPS, etc.)

Testing baseline (2026-05-01 — Intent Solutions Testing SOP)

This repo participates in the Intent Solutions Testing SOP per ~/.claude/CLAUDE.md § "Intent Solutions Testing SOP" and the VPS-as-the-home program (OPS-5nm, Priority 6).

Installed: @intentsolutions/audit-harness v0.1.0 vendored at .audit-harness/ with wrapper at scripts/audit-harness.

Commands: scripts/audit-harness {verify, init, list, escape-scan --staged}.

Next step: run /audit-tests to produce TEST_AUDIT.md. See 000-docs/018-OD-SOPS-audit-harness-baseline-2026-05-01.md.

Upgrade: AUDIT_HARNESS_VERSION=vX.Y.Z curl -sSL https://raw.githubusercontent.com/jeremylongshore/audit-harness/main/install.sh | bash. Or run /sync-testing-harness from any session.

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