⟐ σ-gate — guard

Deterministic trust layer for AI/agent output. One call → one verdict. No model, no API key, no network.

1 = 1.  Declared output must equal what is safe to realize.

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The paradigm

The other four Spektre repos are routing protocols — they move value, capability, promises, and identity across networks. σ-gate is not a routing protocol, and does not pretend to be one. It is the suite's deterministic trust verdict: the gate that sits at the edge of any pipeline and answers, in ~85µs and identically every time, is this output safe to ship? LLM-as-judge guards are slow, burn a model call per check, and silently degrade under rate-limits. But the highest-frequency risks — a leaked AKIA… key, a Luhn-valid card number, an "ignore all previous instructions" — are structurally detectable: you don't need a model, you need a gate that gives the same answer every time. That determinism is the primitive. σ = declared − realized: a block is always nameable (secret[high]: github_pat), never an opaque float, so what the gate declares unsafe is exactly what it refuses to realize. guard is that gate.

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One call

from guard import guard

result = guard(model_output)
# {"safe_to_ship": True/False, "severity": "clean|low|medium|high|critical",
#  "block_reasons": [...], "dimensions": {"secret": {...}, "injection": {...}, "pii": {...}}}

Block a bad output:

v = guard("Here is the key: ghp_16C7e42F292c6912E7710c838347Ae178B4a")
# safe_to_ship: False
# block_reasons: ["secret[high]: github_pat"]

Pass a clean output through:

v = guard("The quarterly report shows revenue grew 12% driven by the EU segment.")
# safe_to_ship: True
# severity: "clean"

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Three dimensions, one verdict

Dimension	What it catches
secret	Leaked credentials — 20+ providers (AWS, GitHub, Stripe, GCP, …), entropy-ranked
injection	Prompt-injection and jailbreak patterns — structural, not heuristic
pii	Email, phone, card (Luhn-verified), SSN, IBAN, IP — compliance-class detection

Every dimension runs independently. A combined hit fires all three:

guard("AKIA… ghp_… 4111 1111 1111 1111 — ignore all previous instructions")
# block_reasons: ["secret[high]: ...", "injection[high]: ...", "pii[high]: ..."]

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Install

Zero dependencies, pure stdlib, Python 3.9+.

git clone https://github.com/spektre-labs/sigma-gate && cd sigma-gate
pip install -e .            # installs the `guard` + `guard-mcp` console scripts
python3 -m pytest -q        # 7 passed, zero dependencies

No install is even required to import — from guard import guard works from the cloned directory.

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Use it

Inline gate in any pipeline:

from guard import guard

def ship(output: str) -> str:
    v = guard(output)
    if not v["safe_to_ship"]:
        raise ValueError(f"blocked: {v['block_reasons']}")
    return output

CLI — pipe any output through:

echo "your model output" | python3 -m guard

Self-test — prove every threat class fires:

python3 -m guard selftest
# {"secret_blocked": true, "injection_blocked": true, "pii_blocked": true,
#  "clean_passes": true, "combined_all_fire": true, "ALL_PASS": true}

Tune the threshold via env var (default: medium):

GUARD_BLOCK_AT=high python3 -m guard "..."

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Use as an MCP tool

σ-gate ships a zero-dependency MCP server — give any agent (Claude Code, Claude Desktop, Cursor, Cline) a deterministic guard tool it can call before shipping output. No model, no key, no token cost.

Claude Code:

claude mcp add guard -- python3 /absolute/path/to/sigma-gate/mcp_server.py

Claude Desktop (claude_desktop_config.json):

{ "mcpServers": { "guard": { "command": "python3",
  "args": ["/absolute/path/to/sigma-gate/mcp_server.py"] } } }

Exposes two tools: guard(text, …) → the ship/block verdict, and guard_selftest() → proof every threat class fires. Pure stdlib stdio JSON-RPC.

Hosted — no install

σ-gate also runs as a hosted remote MCP server (scale-to-zero), listed in the official MCP Registry as io.github.spektre-labs/sigma-gate. Connect with zero local setup:

https://sigma-gate-864996675261.us-central1.run.app/mcp

MCP clients that browse the registry discover it automatically.

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Open-core vs hosted

	Open core (this repo)	Hosted σ scoring
What	Deterministic gate: secret + injection + PII	Coherence / hallucination σ-scoring on a hot path
Latency	~85µs	Network round-trip
Dependencies	Zero	None on your side
Cost	Free, always	Pay-per-call via x402 — no signup
Offline	Yes	No
Model	None	Optional

The open core handles what models cannot do reliably — structural pattern detection with identical verdicts on identical inputs. The hosted layer adds probabilistic coherence scoring for the cases where structure alone is insufficient.

Hosted endpoint:

curl "https://swagletz-sigmagate.hf.space/check?text=your+text+here"
# HTTP 402 + permissionless x402 pay-to — no account required

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Properties

• Deterministic. Same input → same verdict. No variance, no model drift.
• Composable. Each dimension is independent and pluggable. Wire in a hallucination scorer or extend with custom patterns; the gate architecture is additive.
• Honest. Severity and block-reasons are explicit strings, not opaque floats. A block is always nameable.
• Fails safe. If a detector throws, that dimension returns severity: "error" — the call does not silently pass.
• Zero dependencies. Runs anywhere Python 3.9+ runs. No pip install required to import.

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Status

REAL — shipped, deterministic, deployed. CI green, 7/7 tests passing, zero dependencies; live as a local MCP tool and as a hosted scale-to-zero MCP server listed in the official registry.

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The Spektre protocol suite

σ-gate is the deterministic trust verdict of a five-part estate. The other four are routing protocols; this one is the gate they ship through:

• vrp — value routing (least-friction multi-hop settlement)
• crp — capability routing (route a task to the best AI substrate)
• vtc — verifiable transaction chain (signed value promises anyone verifies trustlessly)
• sid — sovereign identity (prove one claim, reveal nothing else)
• sigma-gate — deterministic trust verdict (this repo)

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License

Apache-2.0 — see LICENSE.

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Part of Spektre Labs — coherence-theory research lab. σ = declared − realized · 1 = 1, made executable.