AI code reviewer with senior-level judgment and strict rubric

Lazycoder, an AI code review agent with senior-level judgment, evaluates every changed code block against a fixed 17-rule rubric, runs real checks, and returns a defensible verdict of APPROVE, REQUEST_CHANGES, or BLOCK before code is merged. The tool enforces deterministic, auditable reviews with cited evidence, and integrates into CI via exit codes, aiming to remove human inconsistency and ensure all rules are checked every time.

A code review agent with senior-level judgement. It interrogates every changed block against a fixed rubric, runs the real checks, and returns a defensible verdict — APPROVE / REQUEST CHANGES / BLOCK — before code is trusted or merged. Code gets written fast. The bottleneck is trusting it. lazycoder is the reviewer that never gets tired, never skips a rule, and never self-reports green without running the checks. export ANTHROPIC API KEY=sk-ant-... uvx lazycoder my.diff zero-install run pipx install lazycoder or install the CLI permanently git diff main | uvx lazycoder - review your branch straight from a pipe Exit codes map the verdict — 0 APPROVE, 1 REQUEST CHANGES, 2 BLOCK — so it drops into CI as a gate with no glue code. --json emits the full report. | Manual review | lazycoder | | |---|---|---| Coverage | Whatever the reviewer remembers to look at | Every rule R1–R17 evaluated, every time | Consistency | Varies by reviewer, mood, time of day | Same rubric, same policy, deterministic | Verdict | "LGTM" / gut feel | APPROVE / REQUEST CHANGES / BLOCK from a severity policy | Evidence | Comments, sometimes | Every finding cites rule id + exact file:line | Green claims | "tests pass" trust me | Real linter/typecheck/test output in a sandbox | Untrusted code | Reviewer may run it locally | Reviewed code is data, never executed outside the sandbox | Speed at scale | Slows down as diffs grow | Loops the rubric per block, unattended | Auditability | Lives in someone's head | Append-only decision log; any verdict is replayable | lazycoder does not replace the human — a person still confirms consequential decisions. It removes the parts humans are bad at: remembering all 17 rules, staying consistent across 200 files, and proving the checks actually ran. Two structural facts, at a glance. These are not benchmarks — they are properties enforced by the schema, so they hold on every single review: xychart-beta title "Rubric rules guaranteed evaluated per code block" x-axis "manual review", "lazycoder" y-axis "rules of 17 " 0 -- 17 bar 0, 17 Manual review may cover all 17 — nothing guarantees it. lazycoder cannot emit a verdict until every rule has a recorded pass/fail APPROVE is refused otherwise . xychart-beta title "Findings that cite rule id + exact file:line % " x-axis "manual review", "lazycoder" y-axis "% enforced" 0 -- 100 bar 0, 100 A human reviewer can cite evidence; the lazycoder domain model makes an uncited finding unrepresentable — pydantic rejects it before it exists. The full pipeline is live end to end — deterministic core plus the real model. A unified diff flows all the way to an aggregated verdict: diff → parse diff → CodeBlock └─ review rubric block, rubric every rule, every block └─ RuleResult → from rule results → aggregate → verdict The same flow runs in two modes, sharing every line of plumbing: Fake client default, CI : deterministic, network-free. pytest -q proves the parser, aggregator, and verdict policy on every run. Real client opt-in : AnthropicClient hits the live API. The first live run of eval E3 already passed — the model caught the SQL injection, flagged R7, and the pipeline derived BLOCK with zero parse failures. Because the model was the last thing plugged in, any failure isolates to the prompt or the model — never to the plumbing, which is already proven. The response parser is hardened against real LLM output code fences, surrounding prose, severity casing , and the reviewer prompt teaches the model the exact Finding schema with a literal example, so form errors die at the source. Policy is declarative and lives in config/ , not buried in code. Each file is one part of the setup — reviewable, diffable, swappable: lazycoder/ ├── config/ │ ├── harness.json project context, stack, hard rules, definition of done │ ├── guardrails.json what the agent may / may not do; injection defense; limits │ ├── setup.json runtime, deps + rationale, env vars, bootstrap │ ├── working loop.json specify → plan → execute → verify → decide │ ├── task loop.json orchestrator + review subagents, isolation, aggregation │ ├── review rules.json R1..R17 — the interrogation rubric the core │ ├── production readiness.json the release gate │ ├── evals.json known-flawed/clean cases that test the reviewer │ └── observability.json append-only decision log, tracing, redaction ├── src/argus/ domain, config loader, reviewers, llm client └── tests/ unit + integration + eval coverage Code-level: data structure R1 , control flow R2 , inputs/outputs R3 , failure modes R4 , side effects R5 , dependencies R6 . Security: validation, secrets, injection R7 . Simplicity: simplest form R8 . System-level: state R9 , sync vs async R10 , monolith vs services R11 , invariant R12 . Plus maintainability, tests, and compatibility rules through R17. The interesting part of this project is not the review logic; it's the choices that make the review logic trustworthy. - Deterministic core, model last. Everything that can be pure logic is pure logic, and the non-deterministic LLM is bolted on at the very end. This is a deliberate failure-isolation strategy: when a review goes wrong, the bug is in the prompt or the model, because the plumbing has tests proving it isn't there. - Contracts make invalid state unrepresentable. The domain types are strict pydantic models with validators, not bags of fields. A passed rule cannot carry a finding; a failed one must. Every finding must cite its rule id and an exact file:line . The verdict is a computed field over findings, never a value someone can set by hand. You cannot construct a lying ReviewReport . - Normalize at the boundary, keep the core strict. Untrusted LLM text is cleaned up where it enters "HIGH" → "high" , but the domain enum stays the single source of truth and never loosens. Leniency lives at the edge; the core does not bend. - Debt is executable, not documented. The one known parser limitation is pinned by a strict xfail test, not a comment someone can ignore. The day the fix lands, that test flips to green and the suite tells you the debt is closed. Notes rot; tests don't. - TDD throughout. Every behavior went RED before GREEN — including the garbage-input fixtures that hardened the parser. - The eval is the product. config/evals.json is a set of known-flawed and known-clean cases whose job is to measure the reviewer itself . Wired as a CI gate, it closes the loop: a code reviewer that has its own reviewer, and knows whether it's still good every time it changes. uv sync --extra dev pre-commit install pytest -q deterministic suite — no network, no key ruff check . && black --check . mypy src To run the live-API suite opt-in, never part of pytest -q : cp .env.example .env fill in ANTHROPIC API KEY — .env is gitignored set -a; source .env; set +a pytest -m integration ~~Multi-file / diff orchestration on top of~~✓ review rubric .~~Harden the response parser against real LLM output fixtures .~~✓~~Wire~~✓ config/evals.json as a regression gate on the fake client — a missed rule fails the gate.~~Wire the real Anthropic client behind the same~~✓ LLMClient protocol, with an opt-in integration suite pytest -m integration . First live run: the model caught eval E3's SQL injection R7 → BLOCK . Run the full evals.json set against the live model and track the score over time — the eval stops measuring the plumbing and starts measuring the reviewer: does this prompt, on this model, still catch what it must?~~Distribution: published to~~✓ PyPI https://pypi.org/project/lazycoder/ with a lazycoder console entry point uvx lazycoder my.diff , rubric bundled in the wheel, releases via trusted publishing on v tags. GitHub Action wrapping the CLI, so uses: aisona-lab/lazycoder gates a PR with the same rubric and exit codes.