Practify Protocol

"Any claim must have a verifiable practice anchor."

— First Law, Materialist Practice Theory

Practify 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.

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Maturity (Per Component, Per First Law)

Component	Python	TypeScript	Maturity
Scanner	✅ practify-scanner	✅ practify-scanner	VERIFIED — tested on real projects
Anchors	✅ practify	—	EXPERIMENTAL — API stable, no efficacy data
Source Provenance (v0.3)	—	—	CONJECTURE — field defined, 0 RE projects have produced sourced anchors
Noise Cards	✅ practify	—	UNVERIFIED — schema defined, no accumulated data
AI Context	✅ practify	—	CONJECTURE — format defined, no A/B test
Degraded Verification (v0.3)	—	—	CONJECTURE — modes defined, no RE project has exercised Partial/Degraded paths

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.

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

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Quick Start

Scanner (Immediate Value)

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

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

The scanner finds:

• Swallowed exceptions — except: pass / catch {}
• Bare exception handlers — catching Exception / any
• Missing anchors — functions with no @pract.test or @pract.i_dont_know
• 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

Full Protocol (Experimental)

pip install practify

import practify as pract

@pract.test("empty list returns empty",
    lambda: process([]) == [],
    source="manual: expected behavior")  # v0.3: source field records data origin
@pract.test("keep positives",
    lambda: process([-1, 0, 3, -5]) == [3],
    source="trace:process#001, output=[3] observed 2026-06-18")
@pract.i_dont_know("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]

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The Principle

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.

Practify 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.

"Only Offense, No Defense"

The protocol does not prohibit — it demands proof:

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

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

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Project Structure

practify/
├── README.md                    <-- you are here
├── spec/
│   └── protocol-v0.3.md        # Language-agnostic protocol spec (current)
├── python/
│   ├── practify-scanner/       # Standalone scanner (Level 1, VERIFIED)
│   └── practify/               # Full protocol (Level 2-4, EXPERIMENTAL)
└── typescript/
    └── practify-scanner/       # TS/JS scanner (Level 1, IN DEVELOPMENT)

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Contributing

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.

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References

• Protocol Specification v0.3
• Degraded Verification: §9 of the spec — three operating modes for when code can't compile
• Materialist Practice Theory — the philosophical foundation

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"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