SigRank — AI Operator Benchmarking

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by SunrisesIllNeverSee · TypeScript

SigRank benchmark MCP: cascade metrics, leaderboard, operator profiles, simulation, diagnostics.

com.signalaf/sigrank — SigRank Benchmark MCP Server

SigRank benchmark MCP server provides cascade metrics, a public leaderboard, operator profiles, simulation, and diagnostics. It is centered on SignalAF’s privacy-preserving measurement engine (Upsilon) and supports calculating Yield from four token counts via npx sigrank.

🛠️ Key Features

Cascade metrics
Leaderboard
Operator profiles
Simulation
Diagnostics
Tool set count: 15

🚀 Use Cases

Compare results on an AI operator evaluation leaderboard
Inspect operator profiles and simulation outputs
Track token-related performance through token-count inputs

⚡ Developer Benefits

Built around Model Context Protocol (MCP) tooling
Includes token-usage and token-tracking related topics
Developer-facing workflows tied to token-count-based Yield calculation

⚠️ Limitations

readmeExcerpt is incomplete and does not enumerate the full set of tools or detailed MCP interface behavior

Topics

aianalyticsleaderboardnextjsreactstripesupabasetokenstypescriptai-operatormcpmodel-context-protocoltoken-usageyield-cascadeai-codingccusageclaude-codedeveloper-toolstoken-tracking

Captured live from the server via tools/list.

rank_paste

Calculate SigRank cascade metrics from four non-negative token counts without submitting data. Returns Yield, Leverage, Velocity, SNR, and 10xDEV. No data is persisted.

Parameters4

input	number	required	Total input tokens consumed in the session.
output	number	required	Total output tokens generated.
cache_read	number	required	Tokens read from prompt cache (reused context).
cache_write	number	required	Tokens written to prompt cache (new context stored for reuse).

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0,
      "description": "Total input tokens consumed in the session."
    },
    "output": {
      "type": "number",
      "minimum": 0,
      "description": "Total output tokens generated."
    },
    "cache_read": {
      "type": "number",
      "minimum": 0,
      "description": "Tokens read from prompt cache (reused context)."
    },
    "cache_write": {
      "type": "number",
      "minimum": 0,
      "description": "Tokens written to prompt cache (new context stored for reuse)."
    }
  },
  "required": [
    "input",
    "output",
    "cache_read",
    "cache_write"
  ],
  "additionalProperties": false
}

get_leaderboard

Read the current public SigRank operator leaderboard. Returns ranked operators with Yield, Leverage, class tier, and display name.

Parameters2

limit	integer	optional	Maximum number of operators to return (1–100, default 25).
window	string	optional	Time window for the leaderboard: 7d, 30d, 90d, or all_time.

Raw schema

{
  "type": "object",
  "properties": {
    "limit": {
      "type": "integer",
      "minimum": 1,
      "maximum": 100,
      "default": 25,
      "description": "Maximum number of operators to return (1–100, default 25)."
    },
    "window": {
      "type": "string",
      "enum": [
        "7d",
        "30d",
        "90d",
        "all_time"
      ],
      "default": "30d",
      "description": "Time window for the leaderboard: 7d, 30d, 90d, or all_time."
    }
  },
  "additionalProperties": false
}

get_operator

Read one public operator profile by codename. Returns class tier, rank, percentile, Yield, Leverage, Velocity, and SNR.

Parameters1

codename

string

required

The operator's unique codename (e.g. signal-ae3b5c3c55). Not the display name.

Raw schema

{
  "type": "object",
  "properties": {
    "codename": {
      "type": "string",
      "minLength": 1,
      "maxLength": 128,
      "description": "The operator's unique codename (e.g. signal-ae3b5c3c55). Not the display name."
    }
  },
  "required": [
    "codename"
  ],
  "additionalProperties": false
}

simulate_change

Prescriptive 'what if' tool — takes your current 4 token pillars and proposed changes, runs the cascade on both, returns the exact Υ Yield delta, class change, and per-metric diffs. Test proposed pillar changes and see the payoff before changing your workflow. Changes can be absolute numbers (replace) or strings starting with +/- for relative deltas.

Parameters5

input	number	required	Current input tokens.
output	number	required	Current output tokens.
cache_read	number	required	Current cache-read tokens.
cache_write	number	required	Current cache-write tokens.
changes	object	required	Proposed changes. Keys: input, output, cache_read, cache_write. Values are absolute numbers (replace) or strings starting with +/- for relative deltas. Omitted pillars are unchanged.

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0,
      "description": "Current input tokens."
    },
    "output": {
      "type": "number",
      "minimum": 0,
      "description": "Current output tokens."
    },
    "cache_read": {
      "type": "number",
      "minimum": 0,
      "description": "Current cache-read tokens."
    },
    "cache_write": {
      "type": "number",
      "minimum": 0,
      "description": "Current cache-write tokens."
    },
    "changes": {
      "type": "object",
      "description": "Proposed changes. Keys: input, output, cache_read, cache_write. Values are absolute numbers (replace) or strings starting with +/- for relative deltas. Omitted pillars are unchanged.",
      "properties": {
        "input": {
          "type": [
            "number",
            "string"
          ],
          "description": "New input (absolute) or '+/-N' (relative)"
        },
        "output": {
          "type": [
            "number",
            "string"
          ],
          "description": "New output (absolute) or '+/-N' (relative)"
        },
        "cache_read": {
          "type": [
            "number",
            "string"
          ],
          "description": "New cache_read (absolute) or '+/-N' (relative)"
        },
        "cache_write": {
          "type": [
            "number",
            "string"
          ],
          "description": "New cache_write (absolute) or '+/-N' (relative)"
        }
      }
    }
  },
  "required": [
    "input",
    "output",
    "cache_read",
    "cache_write",
    "changes"
  ],
  "additionalProperties": false
}

diagnose_cascade

Analyzes your token cascade and diagnoses where you're leaking efficiency. Takes 4 token pillars and produces a ranked list of efficiency leaks with severity (critical/warning/info), findings, recommendations, and estimated Υ impact. Checks: cache leverage, velocity, SNR, cache creation ratio, input bloat, and 10xDEV compounding. Use this before simulate_change to understand what's wrong.

Parameters4

input	number	required	Total input tokens.
output	number	required	Total output tokens.
cache_read	number	required	Cache-read tokens.
cache_write	number	required	Cache-write tokens.

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0,
      "description": "Total input tokens."
    },
    "output": {
      "type": "number",
      "minimum": 0,
      "description": "Total output tokens."
    },
    "cache_read": {
      "type": "number",
      "minimum": 0,
      "description": "Cache-read tokens."
    },
    "cache_write": {
      "type": "number",
      "minimum": 0,
      "description": "Cache-write tokens."
    }
  },
  "required": [
    "input",
    "output",
    "cache_read",
    "cache_write"
  ],
  "additionalProperties": false
}

suggest_improvements

Generates ranked, simulated improvement suggestions for your token cascade. Takes 4 token pillars, tests multiple strategies (increase cache reads, reduce input, increase output, optimize cache creation), simulates each, and returns them ranked by Υ yield impact. Each suggestion includes the action, pillar to change, projected Υ, yield delta, projected class, and rationale. Returns the single highest-impact change as best_single_change.

Parameters4

input	number	required	Total input tokens.
output	number	required	Total output tokens.
cache_read	number	required	Cache-read tokens.
cache_write	number	required	Cache-write tokens.

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0,
      "description": "Total input tokens."
    },
    "output": {
      "type": "number",
      "minimum": 0,
      "description": "Total output tokens."
    },
    "cache_read": {
      "type": "number",
      "minimum": 0,
      "description": "Cache-read tokens."
    },
    "cache_write": {
      "type": "number",
      "minimum": 0,
      "description": "Cache-write tokens."
    }
  },
  "required": [
    "input",
    "output",
    "cache_read",
    "cache_write"
  ],
  "additionalProperties": false
}

self_improve

Runs the full self-improvement cycle in one call: (1) computes your current cascade from 4 token pillars, (2) diagnoses efficiency leaks, (3) generates ranked improvement suggestions, (4) simulates the top suggestion, and (5) returns the complete cycle: diagnosis + suggestions + simulated impact of the best change. The 'one-click optimize' tool — call it at the end of a session to see what to improve next time.

Parameters4

input	number	required	Total input tokens.
output	number	required	Total output tokens.
cache_read	number	required	Cache-read tokens.
cache_write	number	required	Cache-write tokens.

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0,
      "description": "Total input tokens."
    },
    "output": {
      "type": "number",
      "minimum": 0,
      "description": "Total output tokens."
    },
    "cache_read": {
      "type": "number",
      "minimum": 0,
      "description": "Cache-read tokens."
    },
    "cache_write": {
      "type": "number",
      "minimum": 0,
      "description": "Cache-write tokens."
    }
  },
  "required": [
    "input",
    "output",
    "cache_read",
    "cache_write"
  ],
  "additionalProperties": false
}

rank_windows

Score up to 4 time windows (7d, 30d, 90d, all-time) in one call. Each window is scored independently with the full cascade (Υ, SNR, Leverage, Velocity, 10xDEV, class). Omit windows you don't have — partial input is allowed (1-4 windows). Does NOT submit to the board.

Parameters4

7d	object	optional	7-day window token pillars (optional)
30d	object	optional	30-day window token pillars (optional)
90d	object	optional	90-day window token pillars (optional)
all	object	optional	All-time window token pillars (optional)

Raw schema

{
  "type": "object",
  "properties": {
    "7d": {
      "type": "object",
      "additionalProperties": false,
      "properties": {
        "input": {
          "type": "number",
          "minimum": 0
        },
        "output": {
          "type": "number",
          "minimum": 0
        },
        "cache_read": {
          "type": "number",
          "minimum": 0
        },
        "cache_write": {
          "type": "number",
          "minimum": 0
        }
      },
      "description": "7-day window token pillars (optional)"
    },
    "30d": {
      "type": "object",
      "additionalProperties": false,
      "properties": {
        "input": {
          "type": "number",
          "minimum": 0
        },
        "output": {
          "type": "number",
          "minimum": 0
        },
        "cache_read": {
          "type": "number",
          "minimum": 0
        },
        "cache_write": {
          "type": "number",
          "minimum": 0
        }
      },
      "description": "30-day window token pillars (optional)"
    },
    "90d": {
      "type": "object",
      "additionalProperties": false,
      "properties": {
        "input": {
          "type": "number",
          "minimum": 0
        },
        "output": {
          "type": "number",
          "minimum": 0
        },
        "cache_read": {
          "type": "number",
          "minimum": 0
        },
        "cache_write": {
          "type": "number",
          "minimum": 0
        }
      },
      "description": "90-day window token pillars (optional)"
    },
    "all": {
      "type": "object",
      "additionalProperties": false,
      "properties": {
        "input": {
          "type": "number",
          "minimum": 0
        },
        "output": {
          "type": "number",
          "minimum": 0
        },
        "cache_read": {
          "type": "number",
          "minimum": 0
        },
        "cache_write": {
          "type": "number",
          "minimum": 0
        }
      },
      "description": "All-time window token pillars (optional)"
    }
  },
  "additionalProperties": false
}

benchmark_me

Answers 'How good am I?' — benchmarks your token cascade against the live field. Takes 4 token pillars (or a codename), computes your cascade, then compares against the live leaderboard: percentile, rank, distance from median, distance from top 10%, strongest metric, weakest metric, and a one-line interpretation. This is the human-question tool — use it when someone asks 'am I a power user?' or 'how do I compare?'.

Parameters6

input	number	optional	Total input tokens. Required if codename is not provided.
output	number	optional	Total output tokens. Required if codename is not provided.
cache_read	number	optional	Cache-read tokens. Required if codename is not provided.
cache_write	number	optional	Cache-write tokens. Required if codename is not provided.
codename	string	optional	Operator codename (alternative to providing pillars). If provided, fetches live profile from the board.
window	string	optional	Time window for field comparison (default 30d).

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0,
      "description": "Total input tokens. Required if codename is not provided."
    },
    "output": {
      "type": "number",
      "minimum": 0,
      "description": "Total output tokens. Required if codename is not provided."
    },
    "cache_read": {
      "type": "number",
      "minimum": 0,
      "description": "Cache-read tokens. Required if codename is not provided."
    },
    "cache_write": {
      "type": "number",
      "minimum": 0,
      "description": "Cache-write tokens. Required if codename is not provided."
    },
    "codename": {
      "type": "string",
      "description": "Operator codename (alternative to providing pillars). If provided, fetches live profile from the board."
    },
    "window": {
      "type": "string",
      "enum": [
        "7d",
        "30d",
        "90d",
        "all_time"
      ],
      "default": "30d",
      "description": "Time window for field comparison (default 30d)."
    }
  },
  "additionalProperties": false
}

rank_if

Answers 'What would it take to reach a target rank?' — takes your current 4 token pillars and a target percentile (e.g. 90 for top 10%), then simulates the smallest metric changes needed to reach that position. Returns: current rank/percentile, simulated rank/percentile, the specific pillar changes required, and the yield delta. This turns SigRank from a scoreboard into a simulator. Use it when someone asks 'what would move my rank?' or 'how do I get to top 10%?'.

Parameters6

input	number	required	Current input tokens.
output	number	required	Current output tokens.
cache_read	number	required	Current cache-read tokens.
cache_write	number	required	Current cache-write tokens.
target_percentile	number	required	Target percentile (0-100). E.g. 90 for top 10%, 99 for top 1%.
window	string	optional	Time window for field comparison (default 30d).

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0,
      "description": "Current input tokens."
    },
    "output": {
      "type": "number",
      "minimum": 0,
      "description": "Current output tokens."
    },
    "cache_read": {
      "type": "number",
      "minimum": 0,
      "description": "Current cache-read tokens."
    },
    "cache_write": {
      "type": "number",
      "minimum": 0,
      "description": "Current cache-write tokens."
    },
    "target_percentile": {
      "type": "number",
      "minimum": 0,
      "maximum": 100,
      "description": "Target percentile (0-100). E.g. 90 for top 10%, 99 for top 1%."
    },
    "window": {
      "type": "string",
      "enum": [
        "7d",
        "30d",
        "90d",
        "all_time"
      ],
      "default": "30d",
      "description": "Time window for field comparison (default 30d)."
    }
  },
  "required": [
    "input",
    "output",
    "cache_read",
    "cache_write",
    "target_percentile"
  ],
  "additionalProperties": false
}

operator_gap

Answers 'What specifically separates operator A from operator B?' — not just 'A has more Yield', but the primary cause, secondary cause, and offsetting weakness. Takes two codenames or two sets of pillars, computes both cascades, and decomposes the yield gap into leverage, velocity, SNR, and scale contributions. Returns the most explanatory factor.

Parameters10

a_codename	string	optional	Codename for operator A (alternative to a_* pillars)
b_codename	string	optional	Codename for operator B (alternative to b_* pillars)
a_input	number	optional
a_output	number	optional
a_cache_read	number	optional
a_cache_write	number	optional
b_input	number	optional
b_output	number	optional
b_cache_read	number	optional
b_cache_write	number	optional

Raw schema

{
  "type": "object",
  "properties": {
    "a_codename": {
      "type": "string",
      "description": "Codename for operator A (alternative to a_* pillars)"
    },
    "b_codename": {
      "type": "string",
      "description": "Codename for operator B (alternative to b_* pillars)"
    },
    "a_input": {
      "type": "number",
      "minimum": 0
    },
    "a_output": {
      "type": "number",
      "minimum": 0
    },
    "a_cache_read": {
      "type": "number",
      "minimum": 0
    },
    "a_cache_write": {
      "type": "number",
      "minimum": 0
    },
    "b_input": {
      "type": "number",
      "minimum": 0
    },
    "b_output": {
      "type": "number",
      "minimum": 0
    },
    "b_cache_read": {
      "type": "number",
      "minimum": 0
    },
    "b_cache_write": {
      "type": "number",
      "minimum": 0
    }
  },
  "additionalProperties": false
}

field_anomaly

Finds unusual operators, metric relationships, and outliers in the live leaderboard — without user prompting. Returns: highest velocity among below-median leverage operators, only top-50 operator with near-zero cache write, largest 30-day yield improvement, rarest signature, and extreme divergence. Powers automated micro-marketing and field insights.

Parameters1

window

string

optional

Raw schema

{
  "type": "object",
  "properties": {
    "window": {
      "type": "string",
      "enum": [
        "7d",
        "30d",
        "90d",
        "all_time"
      ],
      "default": "30d"
    }
  },
  "additionalProperties": false
}

who_operates_like_me

Finds operators whose operating signature most resembles yours. Takes 4 pillars or a codename, computes your signature, then searches the live leaderboard for the nearest neighbors by signature distance. Returns: nearest operators, similarity %, where they outperform you, where you outperform them, and what separates you from the better operator. Makes the leaderboard feel like a network, not a list.

Parameters7

input	number	optional
output	number	optional
cache_read	number	optional
cache_write	number	optional
codename	string	optional	Operator codename (alternative to pillars)
window	string	optional
limit	number	optional	Number of nearest neighbors to return (default 5)

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0
    },
    "output": {
      "type": "number",
      "minimum": 0
    },
    "cache_read": {
      "type": "number",
      "minimum": 0
    },
    "cache_write": {
      "type": "number",
      "minimum": 0
    },
    "codename": {
      "type": "string",
      "description": "Operator codename (alternative to pillars)"
    },
    "window": {
      "type": "string",
      "enum": [
        "7d",
        "30d",
        "90d",
        "all_time"
      ],
      "default": "30d"
    },
    "limit": {
      "type": "number",
      "minimum": 1,
      "maximum": 20,
      "default": 5,
      "description": "Number of nearest neighbors to return (default 5)"
    }
  },
  "additionalProperties": false
}

compare_to_field

Creates a 'YOU vs FIELD vs TOP 10% vs TOP 1%' comparison table for your cascade metrics. Takes 4 pillars or a codename, fetches the live leaderboard, and returns your metrics alongside field median, top quartile, top decile, and top percentile for yield, leverage, velocity, and SNR. Simple, useful, and immediately understandable.

Parameters6

input	number	optional
output	number	optional
cache_read	number	optional
cache_write	number	optional
codename	string	optional	Operator codename (alternative to pillars)
window	string	optional

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0
    },
    "output": {
      "type": "number",
      "minimum": 0
    },
    "cache_read": {
      "type": "number",
      "minimum": 0
    },
    "cache_write": {
      "type": "number",
      "minimum": 0
    },
    "codename": {
      "type": "string",
      "description": "Operator codename (alternative to pillars)"
    },
    "window": {
      "type": "string",
      "enum": [
        "7d",
        "30d",
        "90d",
        "all_time"
      ],
      "default": "30d"
    }
  },
  "additionalProperties": false
}

operator_signature

Computes a normalized operating signature from 4 token pillars or a codename. Returns: signature code (L240-V0.31-S0.24-C0.08 format), archetype (CONTEXTUAL, GENERATOR, BALANCED_ELITE, READER, COMMITTER, STANDARD), dominant trait, and closest comparable operators from the live board. A portable identity object that can feed profile cards, social sharing, enterprise clustering, peer discovery, and longitudinal drift.

Parameters6

input	number	optional
output	number	optional
cache_read	number	optional
cache_write	number	optional
codename	string	optional	Operator codename (alternative to pillars)
window	string	optional

Raw schema

{
  "type": "object",
  "properties": {
    "input": {
      "type": "number",
      "minimum": 0
    },
    "output": {
      "type": "number",
      "minimum": 0
    },
    "cache_read": {
      "type": "number",
      "minimum": 0
    },
    "cache_write": {
      "type": "number",
      "minimum": 0
    },
    "codename": {
      "type": "string",
      "description": "Operator codename (alternative to pillars)"
    },
    "window": {
      "type": "string",
      "enum": [
        "7d",
        "30d",
        "90d",
        "all_time"
      ],
      "default": "30d"
    }
  },
  "additionalProperties": false
}

README.md19 KBView on GitHub

SignalAF — Upsilon Measurement Engine + SigRank Proof Surface

Upsilon is the privacy-preserving measurement engine. SigRank is the public leaderboard and proof surface at signalaf.com. Run npx sigrank to calculate Yield from four token counts. Never prompts or code.

SigRank SignalAF — AI operator evaluation and token cascade efficiency leaderboard

Upsilon measures. SigRank proves. MO§ES™ governs. SignalAF is the brand.

SigRank evaluates the human using the AI, not the AI model itself. Most platforms reward volume. SigRank SignalAF rewards structure.

CI CodeQL audit Lighthouse E2E

What is SignalAF?
The SigRank SignalAF ecosystem
Get ranked (you don't need this repo)
How it works
For developers
Stack
Quick Start
Scripts
Project Map
Data Model
Environment
Supabase
Scoring Invariants
Development Notes
Related
Community
License

The leaderboard	Your operator profile

Every operator ranked by Υ Yield — the architecture of the cascade, not raw spend	Cascade layer, class, and fingerprint — all from four token counts

What is SignalAF?

SignalAF is the umbrella brand for Upsilon and SigRank. Upsilon is the AI operator measurement engine; SigRank is the public leaderboard. Upsilon measures token cascade efficiency, not AI models. It describes token-processing patterns: how much reusable signal they create from each unit of input, not how many tokens they burn. The rank metric:

txt

Υ = (cache_read × output) / input²

The board is anonymous by default, claimable by operators, and built around one question: is this session compounding signal, or just burning tokens? Volume is noise; yield is signal.

This repo is the Next.js implementation behind signalaf.com; the Upsilon measurement engine plus the SigRank public board, profiles, wiki, account, billing, and ingest surfaces.

The compatibility record remains versioned as sigrank/0.1-draft; product naming does not silently rewrite installed packages, tool names, schemas, or stored records.

The SigRank SignalAF ecosystem

Repo	What it is	Install
sigrank-app (this repo)	Upsilon's web implementation plus the SigRank leaderboard/proof surface.	signalaf.com
sigrank-mcp	Upsilon's compatibility instrument — extracts four pillars, computes locally, and optionally submits to SigRank.	`npx sigrank`
bestuser-router-mcp	The intent layer — routes "who is the best AI user?" queries to SigRank SignalAF's leaderboard. MCP server for AI assistants.	`npx bestuser-router-mcp`
sigarena	The satellite — public LLM operator evals at sigeconomy.com. Read-only leaderboard, SEO/AEO surface.	sigeconomy.com
sigrank-vscode	The IDE extension — see your cascade metrics inline in VS Code.	`code --install-extension sigrank.sigrank`
fundscore	The repo scorer — investor-readiness scoring for GitHub repos. CLI + MCP server.	`npx fundscore`

Also in the MO§ES™ suite

Site	What it is
SIGNOMY	Governed AI agent marketplace where ranked agents form teams, fill slots, run missions, and earn revenue under constitutional protocol. Agents are free. Operators pay.
MO§ES	The constitutional governance and methodology framework—the law governing Upsilon and other governed systems.

Get ranked (you don't need this repo)

SigRank SignalAF runs from your terminal. Or wire it as an MCP server for your AI agent:

bash

npx sigrank                 # see your cascade now (no install, no sign-in)

The client reads your local AI session logs on-device, derives your cascade, and publishes to the board, token-only, no transcript content.

bash

npm install -g sigrank     # bundles ccusage + tokscale + tokendash — no separate installs
sigrank enroll             # sign in: paste a connect code from signalaf.com → Settings
sigrank submit             # publish your verified runs to the board

Or explore first, no sign-in:

bash

sigrank                    # full tabbed TUI: dashboard · compare · board · watch
npx sigrank board --once   # print the live leaderboard once
bunx sigrank board --once  # same, via Bun (faster startup if you have it)

Cautious? sigrank submit --dry-run prints the exact signed payload (four token counts and a signature) and sends nothing.

bash

$ sigrank submit --dry-run
📦 Payload (not sent):
  input:          1,251,211
  output:        11,296,121
  cache_create:     128,196
  cache_read:    2,555,179,769
  Υ Yield:          18,436.98
  class:            TRANSMITTER
  signature:        ed25519:9f3a...

Full CLI + MCP docs: sigrank-mcp · package: sigrank on npm.

How it works

Four raw token pillars — input, output, cache_creation, cache_read — are the only inputs. No message content, ever.
The cascade engine derives Υ Yield, Leverage, Velocity, 10xDEV, SNR, and efficiency from those four numbers. The server re-scores every submission authoritatively.
Operators are placed in signal classes (Transmitter, Architect, …) by their cascade shape, and ranked globally by Υ.
SIGNA RATE is the class credential; Υ Yield is the rank metric.

Deep dive: the in-app wiki.

For developers

The rest of this README is for working on the app itself.

Stack

Framework: Next.js 15 App Router, React 19, TypeScript strict
UI: Tailwind CSS, themeable SigRank SignalAF design tokens (carbon default)
Data: Supabase with cold-store snapshot and mock fallback
Billing: Stripe Checkout, Billing Portal, webhook handlers
Validation: zod, Node test runner, TypeScript

Quick Start

bash

npm install
cp .env.example .env.local      # all values optional — see Environment below
npm run dev                     # http://localhost:3000

The app is designed to run without Supabase or Stripe credentials. When env vars are missing, reads fall back to the cold-store snapshot and then deterministic mock data, while billing routes return configuration errors instead of crashing, so it stays buildable, previewable, and testable out of the box.

Requires Node 22.x (see engines in package.json). On a newer Node, next dev may fail to start; use the version manager of your choice to pin 22.

If vendor chunks act strange, run the dev server directly: node_modules/.bin/next dev --port 3000

Scripts

Command	Purpose
`npm run dev`	Start the local Next.js dev server
`npm run build`	Create a production build
`npm run start`	Serve the production build
`npm run lint`	Run the configured Next.js lint command
`npm test`	Run all Node test files
`npm run test:canonical`	Run the canonical ingest parity test
`npm run snapshot`	Refresh the database snapshot

Before committing, run the gates CI enforces:

bash

npx tsc --noEmit            # 0 errors (typescript is a devDependency)
npm run build               # production build green
npm run test:canonical      # canonical ingest parity — MO§ES Υ 18436.98

Project Map

Path	Responsibility
`app/`	App Router pages and API routes
`app/api/v1/`	Public API, ingest, claim, devices, billing, metrics
`components/`	UI components by product area
`components/sigrank/`	Board, profile, and shared SigRank SignalAF UI (incl. `PlatformIcon`)
`lib/data/`	Single read facade: Supabase → snapshot → mock fallback
`lib/ingest/`	Canonical payload parsing and cascade metric materialization
`lib/scoring/`	Core scoring engine and server-only ruleset boundary
`lib/supabase/`	Browser, server, service-role, and auth helpers
`lib/stripe/`	Stripe server helpers, handlers, tiers, and rewards
`supabase/`	SQL schema, migrations, seed data, policies, and tests
`__tests__/`	Node test suites and canonical fixtures

Data Model

All app code reads operator data through @/lib/data. The facade chooses the safest available source, in order:

Supabase live reads when credentials are configured.
Cold-store snapshot (lib/data/snapshot.json) if live reads are unavailable or fail.
Mock fixtures (lib/data/mock.ts) as the last resort.

Environment

Copy .env.example to .env.local and fill values as needed. All are optional locally (the app degrades gracefully); production values live in Vercel environment variables.

Variable group	Notes
`NEXT_PUBLIC_SUPABASE_URL`, `NEXT_PUBLIC_SUPABASE_ANON_KEY`, `SUPABASE_SERVICE_ROLE_KEY`	Live Supabase reads + service-role writes
`STRIPE_SECRET_KEY`, `NEXT_PUBLIC_STRIPE_PUBLISHABLE_KEY`, `STRIPE_WEBHOOK_SECRET`	Billing and webhook flows
`STRIPE_PRICE_*`	Per paid tier / claim checkout path
`NEXT_PUBLIC_SITE_URL`	Stripe redirects and public URLs
`SIGRANK_RULESET`	Server-only proprietary RS.xx scoring overrides
`SIGRANK_API_KEY`	Optional trusted bulk-read key for public API consumers
`SIGRANK_INGEST_WRITE`	Ingest-write flip: `1` = persist verified snapshots to the board (see note below)
`NEXT_PUBLIC_GATE_*`	Optional feature gates for unfinished surfaces

Never commit real secrets or proprietary ruleset values.

SIGRANK_INGEST_WRITE — the ingest-write flip

Status: LIVE in Production (1). The board persists verified signed snapshots from enrolled MCP devices. Set in Vercel for both Production and Preview.

Do NOT mark this variable as sensitive in Vercel. It is a boolean on/off flag (1 or ""), not a secret. When marked sensitive, vercel env pull returns "" even though the runtime has 1, which misleads agents and operators into thinking the flip is OFF. If vercel env pull shows "" but the board is receiving verified submissions, the var is likely marked sensitive again. Fix it in the Vercel dashboard (remove the sensitive flag).

Authoritative verification is RUNTIME behavior, not the CLI pull:

PostHog: snapshot_submitted events with source: "agent" and persisted: true
Supabase: rows in snapshot_submissions with verification_tier = 'verified' and status = 'scored'
A real signed submit via npx sigrank submit returning ranked: true

If any of those show verified rows landing, the flip is ON regardless of what vercel env pull says.

Supabase

Schema, migrations, RLS policies, and seed data live in supabase/. See supabase/README.md to run your own instance. RS.xx scoring weights are server-only and are not included here.

Scoring Invariants

Product contracts, not implementation details:

Υ = (cache_read × output) / input²
T × C × R = Cr / I = Leverage
10xDEV = log₁₀(Leverage)
SIGNA RATE is the class credential; Υ Yield is the rank metric.
RS.xx weights are server-only and must not be exposed to client components.

Development Notes

Keep changes small and aligned with existing file ownership.
Pages are React Server Components by default; add 'use client' only when a component needs hooks, event handlers, or browser APIs.
Keep components/sigrank/tokens.ts and Tailwind theme values in sync.
Use <Placeholder /> for placeholder metrics and <CanonId /> for canonical real values.
Do not import server-only scoring configuration into client code.
Avoid random values and wall-clock reads at module scope.

signalaf.com — the live board
signalaf.com/score — paste four token counts, get projected yield + ghost rank
sigrank-mcp — the CLI / TUI / MCP server (npx sigrank)
Smithery — one-click MCP install for Claude Desktop, Cursor, and more
Glama — MCP server directory listing

Community

Reporting issues

Found a bug or have a feature request? Please open an issue on GitHub. Search existing issues first to avoid duplicates, and include repro steps, expected vs. actual behavior, and your environment (OS, Node version).

Pull request process

Fork the repo and create a branch from main.
Make your change, keeping it small and aligned with existing file ownership.
Ensure npx tsc --noEmit passes with 0 errors before pushing.
Open a pull request against main with a clear description of what and why.

License

MIT; see LICENSE.

Install

Tools (15)

rank_pasteget_leaderboardget_operatorsimulate_changediagnose_cascadesuggest_improvementsself_improverank_windows

Remote endpoint

Streamable HTTP

Hosted server - connect over the network, no local install.

url

https://signalaf.com/api/mcp

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "sigrank": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://signalaf.com/api/mcp"
      ]
    }
  }
}

Desktop config is stdio-only; this bridges via mcp-remote. Native remote: Settings > Connectors.

com.signalaf/sigrank — SigRank Benchmark MCP Server

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

rank_paste

get_leaderboard

get_operator

simulate_change

diagnose_cascade

suggest_improvements

self_improve

rank_windows

benchmark_me

rank_if

operator_gap

field_anomaly

who_operates_like_me

compare_to_field

operator_signature

SignalAF — Upsilon Measurement Engine + SigRank Proof Surface

Table of Contents

What is SignalAF?

The SigRank SignalAF ecosystem

Also in the MO§ES™ suite

Get ranked (you don't need this repo)

How it works

For developers

Stack

Quick Start

Scripts

Project Map

Data Model

Environment

SIGRANK_INGEST_WRITE — the ingest-write flip

Supabase

Scoring Invariants

Development Notes

Related

Community

Reporting issues

Pull request process

License

Install

Remote endpoint

Related servers