# Legacy Code Review > Use when reviewing, testing or taking ownership of code nobody has looked at in a long time, especially untested code and code an AI agent generated and then left. Covers scoping a cohort, building the caller map, adding first coverage to code that has never been executed without freezing its defects into assertions, inventorying findings with evidence, and adversarially verifying that the new tests actually hold. Use when asked to "review this legacy code", "add tests to this old module", "audit the code we inherited", "clean up AI-generated code", "we have no tests on this", "write characterization tests", or when a codebase has grown faster than anyone has read it. - Skill: `looties-io/legacy-code-review` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add looties-io/legacy-code-review` - Raw SKILL.md: https://api.skillmd.com/api/skills/looties-io/legacy-code-review/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: looties-io (https://skillmd.com/u/looties-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/looties-io/legacy-code-review --- # Legacy Code Review Legacy no longer means old. It means **code that is in production and that nobody has read**, and generated code reaches that state in weeks rather than years. It arrives with plausible structure, consistent naming, and no history of anyone verifying that it does what it appears to do. The trap is specific and it is the reason this skill exists. ## The one rule: never derive an assertion from the code under test Writing a characterization test for untested code is how a defect gets **blessed**. You read the handler, you assert what it currently returns, the test goes green, and the bug is now protected by your test suite. Nobody can fix it later without arguing with CI, so nobody does. The canonical scar: a refund handler dropped a currency unit conversion and refunded 39 cents where 3900 was owed. Its own test asserted `amount === "39"`. Green in CI for months. The test was not missing. The test was the reason the bug survived. So the assertions come from **outside** the code under test: - the schema and the migrations (what the data actually permits and means) - the callers (what they pass and what they do with the answer) - the provider's API contract (units, required fields, idempotency semantics) - the project's own standards and specifications - the domain rule as a human states it, written down before you open the file And where the code disagrees with what you derived, **report the disagreement instead of asserting it**. That inventory is the deliverable of the pass. Not the fix. ## Step 1: scope a cohort, not a file Pick a set with a shared reason to be suspect, so the findings compound instead of scattering: - everything written in a given period, or by a given generator - everything with zero test coverage, ranked by what it touches - everything on one blast radius: money, authorization, deletion, outbound messages Rank by consequence, not by size. The riskiest untested handler is worth more than ten small ones. Say the cohort's size out loud and hold to it; scope creep here is what turns a review into an abandoned branch. ## Step 2: build the caller map before reading any handler For each item, answer three questions from outside it: 1. **Who calls this?** The frontend, a scheduler, another service, a provider webhook, or nothing. 2. **With what credential?** And does the handler require the one that is actually sent. 3. **Does anything call it at all?** This is cheap and it front-loads the largest findings. Items with **no caller** are dead weight carrying live authority: archive them before deleting so a rollback is possible. Items whose caller sends a different credential than the handler checks have been failing on every invocation, possibly for months, which `ground-truth` will date for you. The caller map also gives you the intent source for Step 3. What a caller does with a response is often the only surviving statement of what the response was supposed to be. ## Step 3: triage the cohort through fixed lenses Read each item once per lens rather than once in total. Mixed-lens reading finds the first thing and stops. The five that earn their keep: - **Duplication of an owned rule.** Is this a hand copy of a rule that already has an owner elsewhere? Copies drift, and the copy is where the defect lives, because the owner is the one that gets fixed. Every money and authorization formula should have exactly one implementation. - **Boilerplate.** Repeated envelopes, repeated client construction, repeated secret reads. Each copy is a place the next change will be forgotten. - **Security.** Who can call it, what does it act on, does it act on something the caller named. - **Silent failure.** Errors caught per item, run reports success. See `ground-truth`. - **Orphans and drift.** Deployed but uncalled, or pointing at a host, table, or field that no longer exists. ## Step 4: write tests that pin the intended behavior Now write coverage, with the assertions derived in Step 3 and never from the handler body. For each test, be explicit about which of two things you are pinning: - **Intended:** derived from an external source. Assert it. If the code disagrees, the test fails, and that failure is a finding, not a broken test. - **Merely current:** you could not establish the intent. Do not assert it as correct. Either leave it uncovered and inventory the ambiguity, or assert it with a comment naming it as unverified so the next person knows the assertion carries no authority. The second category is where blessing happens. Making it visible is most of the cure. Do not fix as you go. Mixing an inventory pass with a remediation pass makes both unreviewable, and you will lose the ability to prove any given fix worked. Fix in a separate pass, test-first, with each fix **proven red against the pre-fix code**. ## Step 5: adversarially verify the coverage New tests on old code feel like progress. Verify they are, with two independent checks that do not trust the person who wrote them: - **Mutation testing, weighted.** Deliberately break the production code in the places that matter most (a comparison in an authorization check, a unit conversion, a signature verification, an amount) and confirm each mutation turns a test red, and that it is the test which claims to guard it. Anything that survives is a coverage gap wearing a passing test. Expect the survivors to be few and genuinely instructive. - **Independent re-derivation.** A second reviewer re-derives the findings from the schema, the migrations and the callers, without reading the first reviewer's tests. Agreements are confirmed, disagreements are the interesting part. In practice this both downgrades some findings and adds ones the first pass missed, which is exactly why it is worth the cost. If you are orchestrating agents, these two are the roles to keep separate: the one who wrote the tests must not be the one who validates them. ## Step 6: hand over an inventory, not a narrative Each finding needs four fields, and nothing else: | Field | Why | |---|---| | Location | `file:line`, so it can be checked in ten seconds | | Current behavior | What it does, stated as fact | | Intended behavior | What it should do, **and the source that says so** | | Evidence | Live confirmation, or explicitly "not confirmed in production" | Severity comes after the evidence, never before. A finding with no named intent source is not a finding, it is a preference. ## The law you will keep rediscovering Across this kind of review, one observation repeats almost without exception: > **The correct mechanism already existed in the codebase, and using it was optional.** The shared helper was there and the handler contained a copy. The guard existed and the endpoint did not call it. The dedupe registry existed and the send path skipped it. Very little of what you find is missing knowledge. Almost all of it is a convention that nothing enforced. That means the durable output of a legacy review is not the fixed files. It is the set of conventions you convert into constraints so the next generated handler cannot repeat them. That conversion is `impossible-by-design`, and it is where the value of this pass actually lands. ## Verification You are done when: - Every item in the cohort has a caller answer, including "nothing calls it". - Every assertion you added names an external intent source, or is explicitly marked unverified. - No fix shipped without first being red against the pre-fix code. - A weighted mutation run turned the money and authorization tests red, and the survivors are closed. - A second reviewer re-derived the findings independently and their disagreements were resolved. - At least one finding was converted into a constraint rather than a fix. ## Related - `ground-truth`: date each finding and confirm it is really happening before assigning severity. - `endpoint-surface-map`: the systematic version of Step 2 for services with many endpoints. - `impossible-by-design`: turn the recurring findings into constraints so the cohort cannot regrow.