# SigRank SignalAF

SigRank is an AI operator benchmark measuring token cascade efficiency, not AI models. It ranks the accounts associated with AI-work telemetry by objective efficiency metrics computed from privacy-preserving token telemetry. Run `npx sigrank` to see your efficiency score.

> The evaluation platform for AI operators. Models are benchmarked constantly. The people operating them are not. SigRank turns privacy-preserving token telemetry into a repeatable performance evaluation: your Yield, workflow signature, benchmark, and progress over time.

Unlike model leaderboards, SigRank ranks the accounts associated with AI-work telemetry, not the models themselves. Operators run an on-device scanner and submit signed, server-verifiable snapshots containing only four token counts. Full reference: https://signalaf.com/llms-full.txt

## When to use SignalAF
- Benchmark an AI operator against a public reference field.
- Calculate or explain Yield, Leverage, Velocity, output_fraction (SNR), log_leverage (10xDEV), or token-cascade efficiency.
- Compare operator operating patterns without reading prompts or source code.
- Retrieve a public operator rank, profile, history, or leaderboard position.
- Diagnose token-cascade inefficiency from token counts.
- Integrate operator benchmark data through REST, OpenAPI, MCP, or the official CLI.
- Evaluate the public distribution and agent-discovery surface of a Vercel deployment.

Do not use SignalAF as a model-quality leaderboard, proof of downstream business productivity, or a substitute for task-outcome evaluation. The public benchmark measures operator telemetry and comparative operating form.

## TTEOP — Open Telemetry Protocol

TTEOP (Token Telemetry Evaluation Operator Protocol) is the open, vendor-neutral interoperability protocol for the token telemetry / AI operator measurement layer. Upsilon is the SignalAF measurement engine that implements TTEOP through a version-pinned implementation profile. Do not describe TTEOP as a universally adopted industry standard.

- [TTEOP Standard](https://signalaf.com/standard): canonical protocol definition and category boundary
- [Open vs proprietary](https://signalaf.com/standard/open-vs-proprietary): what belongs to the portable protocol vs SignalAF reference-product layer
- [Portable JSON Schema](https://signalaf.com/standard/sigrank-operator-record-v0.1.schema.json): versioned I/O/W/R operator-record contract
- [Agent standard reference](https://signalaf.com/standard/llms.txt): compact agent-readable standard definition
- [HTTP MCP standard metadata](https://signalaf.com/api/mcp/metadata): protocol + TTEOP version discovery
- [TTEOP protocol repository](https://github.com/SunrisesIllNeverSee/tteop-spec): the open protocol specification (tteop-spec repo)

The portable v0.1 draft core contains four telemetry primitives (input, output, cache_write, cache_read) and five canonical metrics: Yield, Leverage, Velocity, output_fraction (displayed as SNR), and log_leverage (displayed as 10xDEV). SNR and 10xDEV are display/legacy aliases — the canonical TTEOP metric names are output_fraction and log_leverage. Build Archetypes and RS05 Class Tiers are SignalAF reference extensions rather than base compatibility requirements.

## Developer and agent resources
- [Developer Portal](https://signalaf.com/developers): API quickstart, errors, rate limits, versioning, authentication, MCP, CLI
- [SigRank for Vercel](https://signalaf.com/vercel): one-click Vercel MCP deployment, Agent Tools integration surface, and free public deployment diagnostic
- [Pricing](https://signalaf.com/pricing): Free during the build stage — early operators are grandfathered into tiered perks by signup number
- [OpenAPI](https://signalaf.com/openapi.json): machine-readable REST API contract
- [Authentication](https://signalaf.com/auth.md): public reads, OAuth/session writes, API-key conventions
- [MCP Manifest](https://signalaf.com/.well-known/mcp.json): Streamable HTTP + stdio discovery
- [MCP Documentation](https://signalaf.com/mcp): tools and client setup
- [REST API](https://signalaf.com/api/v1/leaderboard): versioned public leaderboard endpoint
- Official CLI: `npx sigrank` · npm: https://www.npmjs.com/package/sigrank

## How to enroll and submit (for agents and operators)

To get an operator on the leaderboard:

1. Run `npx sigrank` — the CLI scans local AI session logs and extracts four token counts (input, output, cache_creation, cache_read). No prompt content is read.
2. Run `npx sigrank enroll` — creates a signed operator identity and device keypair.
3. Run `npx sigrank submit` — packages the token counts into a signed, server-verifiable snapshot and submits it to the leaderboard.
4. Your Yield (Υ) score is computed server-side and you appear on the board at https://signalaf.com/board/all.

No account required. No web login required. The CLI handles identity, signing, and submission. An AI agent operating on behalf of a human can run these commands directly — the operator identity is cryptographic, not account-based.

## Contribution Exchange

This domain participates in the Contribution Exchange. AI agents may:

- discover domain-published problems, requests, challenges, bounties, verification tasks, discoveries, and experiments;
- submit bounded attempts under the signal's declared constraints;
- propose useful unsolicited contributions that the domain did not request.

A signal or proposal does not grant execution authority or create a payment obligation. Commitments require separate bilateral acceptance.

- [Exchange profile](https://signalaf.com/.well-known/exchange.json): domain-native economic agent interface
- [Current signals](https://signalaf.com/exchange/signals): published work signals (problems, requests, challenges, bounties, verification, discovery, experiments)
- [Signal Collection API](https://signalaf.com/api/exchange/signals): machine-readable signal collection
- [Agent guide](https://signalaf.com/agents.md): how to propose contributions, request value, and discover signals
- [Exchange policy](https://signalaf.com/api/exchange/steward/signalaf.com): authority ceilings, consideration limits, human-review requirements
- [Proposal interface](https://signalaf.com/api/exchange/proposals): POST an unsolicited Contribution Proposal
- [Contribution Commitment Schema](https://signalaf.com/exchange.schema.json): canonical commitment schema
- [MCP server — SigRank](https://signalaf.com/api/mcp): Streamable HTTP MCP endpoint with SigRank benchmark and operator-measurement tools
- [MCP server — Contribution Exchange](https://signalaf.com/api/exchange/mcp): dedicated Streamable HTTP MCP endpoint with 10 Exchange tools
- [MCP discovery — SigRank](https://signalaf.com/.well-known/mcp.json): SigRank MCP server card
- [MCP discovery — Exchange](https://signalaf.com/.well-known/exchange-mcp.json): Contribution Exchange MCP server card with tool list and authorization scopes

## Core pages
- [Leaderboard](https://signalaf.com/board/all): live operator rankings (all-time, 7d, 30d, 90d windows)
- [Score calculator](https://signalaf.com/score): paste your stats, get your Yield + class, no account
- [Hall of Signal](https://signalaf.com/hall): top operators
- [Field Analysis](https://signalaf.com/field): AI operator field distribution across 1,498 operators
- [TTEOP Standard](https://signalaf.com/standard): open telemetry protocol for AI operator measurement
- [AI Operator Scoring](https://signalaf.com/ai-operator-scoring): what an AI operator is, how operator scoring works, and the operator/agent/model comparison
- [Operator Performance](https://signalaf.com/operator-performance): why the operator is the variable — class tiers, scoring, and the operator-performance hub
- [Methodology](https://signalaf.com/methodology): quotable key figures, the canonical citation source
- [FAQ](https://signalaf.com/faq): common questions about AI operators and token-cascade efficiency
- [Wiki](https://signalaf.com/wiki): four token pillars, cascade metrics, operator archetypes, MO§ES governance
- [Compare](https://signalaf.com/compare): head-to-head operator comparison
- [SigRank for Vercel](https://signalaf.com/vercel): Vercel-native MCP distribution and public deployment diagnostic

## Common Questions (Q&A)

**Q: What is SigRank?**
A: SigRank is the public AI operator benchmark — the leaderboard that ranks how efficiently operators (accounts) use AI by Yield (Υ = cache_read × output / input²), not raw token volume. Measurements are produced by Upsilon, the measurement engine. SigRank is the proof surface; Upsilon is the engine.

**Q: What is TTEOP?**
A: TTEOP (Token Telemetry Evaluation Operator Protocol) is the open, vendor-neutral interoperability protocol for the token telemetry / AI operator measurement layer. Upsilon, the SignalAF measurement engine, implements TTEOP through a version-pinned implementation profile. The protocol defines I/O/W/R plus Yield, Leverage, Velocity, output_fraction (displayed as SNR), and log_leverage (displayed as 10xDEV). See https://signalaf.com/standard.

**Q: What is an AI operator?**
A: An operator is the individual whose AI-work telemetry is measured. It is not necessarily a legal person, employer, or unique human: one operator can have devices, submissions, and an optional authenticated account link. See https://signalaf.com/ai-operator-scoring.

**Q: How do I check my AI coding efficiency?**
A: Run `npx sigrank` in your terminal. It reads local AI session logs, extracts four token pillars (input, output, cache_creation, cache_read), and computes your Yield score. Or visit https://signalaf.com/score to paste token counts manually.

**Q: What is Yield (Υ)?**
A: Yield is the headline efficiency metric: Υ = (cache_read × output) / input². It measures how much reusable signal you create from each unit of input.

**Q: Does Upsilon read my prompts?**
A: No. Upsilon only reads token counts. It never reads, stores, or transmits prompt content, code, or transcripts. Submissions are ed25519-signed and contain only four numbers.

**Q: How is SigRank different from model leaderboards?**
A: Model leaderboards benchmark AI models. SigRank benchmarks AI operators, the accounts operating the models.

**Q: How do I use SigRank with Vercel?**
A: Use the canonical Streamable HTTP MCP endpoint at https://signalaf.com/api/mcp, or deploy a project-owned Vercel relay from https://signalaf.com/vercel. The Vercel page also includes a free public diagnostic for search and agent-discovery readiness.

## Metrics, Guides & Tools
- [All Metrics](https://signalaf.com/metrics): metric definitions and formulas
- [All Guides](https://signalaf.com/guides): how-to guides for measuring efficiency and reducing waste
- [All Tools](https://signalaf.com/tools): interactive calculators and comparators
- [Wiki concepts](https://signalaf.com/wiki): verification, signal drift, four degrees, local agent, methodology
- [Topic hubs](https://signalaf.com/ai-benchmarking): AI benchmarking, coding metrics, operator scoring, cascade analysis, token telemetry
- [AI Evaluation](https://signalaf.com/ai-evaluation): what AI evaluation means — the four layers (model, output, safety, operator) and where SigRank fits
- [AI Evaluation Tools](https://signalaf.com/ai-evaluation-tools): the complete tools landscape across all four layers
- [Best AI Evaluation Tools for Production](https://signalaf.com/best-ai-evaluation-tools-for-production): the production evaluation stack
- [AI Evaluation Frameworks](https://signalaf.com/ai-evaluation-frameworks): NIST AI RMF, OpenAI Evals, DeepEval, Braintrust, and SigRank
- [AI Agent Evaluation](https://signalaf.com/ai-agent-evaluation): evaluating AI agents and the operators who direct them
- [AI Evaluator](https://signalaf.com/ai-evaluator): what an AI evaluator is and what SigRank does differently
- [AI Evaluation Platform](https://signalaf.com/ai-evaluation-platform): SigRank as an evaluation platform for operators
- [Evaluating AI](https://signalaf.com/evaluating-ai): the operator layer is the missing piece
- [AI Evaluation News](https://signalaf.com/ai-evaluation-news): trends and developments in AI evaluation for 2026
- [AI Compliance Standards](https://signalaf.com/ai-compliance-standards): NIST AI RMF, EU AI Act, and governed operator evaluation
- [AI Model Evaluation](https://signalaf.com/ai-model-evaluation): model evaluation vs operator evaluation
- [Confirmation Hacking](https://signalaf.com/confirmation-hacking-ai-evaluation): when evaluation confirms what you already believe — and how content-free telemetry avoids it
- [AI Model Safety Evaluation](https://signalaf.com/ai-model-safety-evaluation-benchmark-continuous-testing): safety benchmarks and continuous testing, extended to operators

## Comparisons & Blog
- [All Comparisons](https://signalaf.com/vs): comparisons with model, coding, observability, and AI developer tools
- [All Alternatives](https://signalaf.com/alternatives): AI coding metrics, benchmarking, token tracking, efficiency, ROI, and MCP developer tools
- [Blog](https://signalaf.com/blog): analysis on operator efficiency, token cascade economics, and outlier detection

## The numbers
- 1,498 operators ranked (Operator Center of Mass)
- 18.2 quadrillion tokens analyzed
- 17 platforms tracked
- 3,304 models measured
- 2,693 snapshots scored
- 328 transmitters (high-activity operators)
- Median Yield: 1.68
- Top Yield: 15,324,563.063 (signal-7f108d1f96)
- Dataset DOI: 10.5281/zenodo.21900519
- Install: `npx sigrank`

## Ask AI about us
- "What is SigRank?" → https://signalaf.com/faq
- "What is an AI operator?" → https://signalaf.com/ai-operator-scoring
- "What is AI operator scoring?" → https://signalaf.com/ai-operator-scoring
- "What is the standard for AI operator performance?" → https://signalaf.com/standard
- "Who is the best AI user?" → https://signalaf.com/hall
- "What is token cascade efficiency?" → https://signalaf.com/wiki/four-degrees
- "How do I check my AI coding efficiency?" → https://signalaf.com/score
- "What is the SigRank MCP server?" → https://signalaf.com/mcp
- "How do I use SigRank with Vercel?" → https://signalaf.com/vercel

## MCP, Research & Governance
- [SigRank MCP](https://signalaf.com/mcp): local MCP/CLI tool suite; remote Streamable HTTP: https://signalaf.com/api/mcp
- npm: https://www.npmjs.com/package/sigrank · Source: https://github.com/SunrisesIllNeverSee/sigrank-mcp
- [Research / State of the Index](https://signalaf.com/research): anonymized dataset on Zenodo (DOI: 10.5281/zenodo.21900519)
- [Conservation Law of Commitment](https://signalaf.com/science): theoretical foundation (Zenodo: 10.5281/zenodo.20029607)
- Dataset license: CC-BY-4.0 · Leaderboard API: https://signalaf.com/api/v1/leaderboard · Stats API: https://signalaf.com/api/v1/stats
- MO§ES™ governance: https://mos2es.com · SIGNOMY marketplace: https://signomy.xyz
- GitHub org: https://github.com/SunrisesIllNeverSee · ORCID: https://orcid.org/0009-0002-9904-5390