Anchorlaw Protocol

"Any claim must have a verifiable practice anchor."

— First Law, Materialist Practice Theory

Anchorlaw is a code verification protocol for vibe coding — not a test framework, not a linter.

It detects defensive code patterns that signal cognitive gaps and provides a structured feedback loop for AI-assisted development.

Honesty notice: Components marked EXPERIMENTAL, UNVERIFIED, or CONJECTURE are working hypotheses. Their value has not been demonstrated through practice. Use them to help us test the hypotheses — not because we claim they work.

v0.9 update (2026-08-08): Judge institutionalization — the review gate is a mandatory checkpoint at decision points, not only a closing gate: confirmed requires a prior judge opinion; major redirections (case reopening, root-cause determination, scope decisions) require judge review; self-review does not constitute the review gate; plans pre-place judge steps (§15.4). Verification termination gates: convergence terminates on mechanical criteria (external test set, three-tier review opinions with blocking limited to test/compile/claim contradictions, 3-round cap) — no more endless review loops (§15.4).

v0.8 update (2026-08-08): Convergence-gate model — programming is linear convergence; the main agent writes with full context; the only sanctioned subagent role is the review gate (judge). anchor.write/anchor.test are inline.

v0.7 update (2026-08-08): First-host practice feedback (CoreSwap 8576-24blocks) absorbed — source artifact requirement + probe source type (§5.5), retry-cap scope (§9.4), verification executor separation (§9.6), order-dependent semantic equivalence (§13), judge three-source baseline (§15.4).

v0.6 update (2026-08-08): Agent Execution Topology (§15) + Host Integration Contract (§16) — the four-layer interface surface is complete: claim (§13), knowledge (§14), execution isolation (§15), host integration (§16). Subprocess skills keep the main session clean.

v0.5 update (2026-08-08): Agent Skill Manifest (§14) — layered, single-responsibility skills move protocol knowledge to on-demand playbooks, fixing agent-side attention dilution. Reference implementation ships in .reasonix/skills/.

v0.3 update (2026-06-18): Source Provenance, Degraded Verification modes, and Verify retry cap. See Protocol Spec v0.3.

# Python
pip install anchorlaw-scanner
anchorlaw-scanner check src/

# TypeScript
npm install anchorlaw-scanner
npx anchorlaw-scanner check src/

The scanner finds:

• Swallowed exceptions — except: pass / catch {}
• Bare exception handlers — catching Exception / any
• Missing anchors — functions with no @pt or @idk
• Defensive null chains — 3+ chained if x is None: return None
• Trivial tests — assert f(x) == f(x)
• Vague TODOs — // TODO: fix without ticket reference

pip install anchorlaw

from anchorlaw import test as pt, i_dont_know as idk

@pt("empty list returns empty",
    lambda: process([]) == [],
    source="trace:process#000, input=[] output=[] observed 2026-06-18")  # v0.3: source field records data origin
@pt("keep positives",
    lambda: process([-1, 0, 3, -5]) == [3],
    source="trace:process#001, output=[3] observed 2026-06-18")
@idk("behavior with massive lists (>1M items) not verified",
    source="static: not covered in traces")
def process(data: list[int]) -> list[int]:
    return [x for x in data if x > 0]

Traditional development separates "writing code" from "writing tests." Vibe coding makes this separation costly — AI generates code fast, but verification happens later, manually, and feedback is lost between iterations.

Anchorlaw inverts this: tests are part of the declaration, not an add-on. A function without a test anchor or an i_dont_know declaration is flagged at scan time — not because it's buggy, but because it has no evidence of correctness.

When tests fail at runtime, failures are captured as noise cards — structured knowledge that accumulates over time and can be injected back into AI context for future code generation.

The protocol does not prohibit — it demands proof:

• Traditional: "You cannot divide by zero." (defensive)
• Anchorlaw: "Prove the divisor is non-zero, or handle the zero case." (offensive)

The single allowed defense is @idk — an honest declaration that opens the battlefield for practice feedback.

anchorlaw/
├── README.md                    <-- you are here
├── spec/
│   └── protocol-v0.9.md        # Language-agnostic protocol spec (current)
├── python/
│   ├── anchorlaw-scanner/       # Standalone scanner (Level 1, VERIFIED)
│   └── anchorlaw/               # Full protocol (Level 2-4, EXPERIMENTAL)
└── typescript/
    └── anchorlaw-scanner/       # TS/JS scanner (Level 1, IN DEVELOPMENT)

This project is actively seeking practice data, not pull request debates.

The most valuable contribution right now:

1. Run the scanner on your codebase. Report false positives.
2. Use the anchors on a real project for 2+ weeks. Tell us if they helped or hurt.
3. Accumulate noise cards. We need projects with 30+ cards to test AI context injection.

Start a discussion on GitHub Discussions or open an issue with your findings.

• Protocol Specification v0.9
• Degraded Verification: §9 of the spec — three operating modes for when code can't compile
• Materialist Practice Theory — the philosophical foundation

"This project does not promise eternal truth. It promises a set of tools forged in current historical conditions, open to being replaced by better ones. Its highest commitment is to make its users capable of questioning, improving, and ultimately transcending it."

— First Law, Applied Reflexively