# Old Coder > Evidence-first development — surround the implementation with an executable spec and a gauntlet of constraints (tests, types, coverage, mutation) so line-by-line review becomes optional. Use when the user explicitly asks for high-assurance or evidence-first work ("reliable", "TDD", "prove it works", "I won't read the code"), or when the change touches high-stakes domains (money, auth, data loss,… - **Type:** Skill - **Install:** `agentstack add skill-amazingang-old-coder-old-coder` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [AmazingAng](https://agentstack.voostack.com/s/amazingang) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [AmazingAng](https://github.com/AmazingAng) - **Source:** https://github.com/AmazingAng/old-coder/tree/main/skills/old-coder ## Install ```sh agentstack add skill-amazingang-old-coder-old-coder ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Old Coder: Reliable Coding Under Constraint and Test The human will NOT read your implementation. Their confidence comes entirely from two artifacts you produce: (1) an **executable specification** they approve before you write code, and (2) an **evidence report** proving the code ran the gauntlet. Your job is to make those two artifacts trustworthy enough that line-by-line review becomes optional within the spec's boundaries. This inverts the normal review model: **trust moves from inspection to constraints.** Be honest about what that buys: the gauntlet proves the code satisfies every constraint the spec expresses — it cannot prove the spec expresses everything that matters. That is exactly why the human approves the SPEC (the one artifact that breaks the everything-authored-by-the-same-agent correlation), and why EVIDENCE reports layered, auditable confidence, never absolute proof. Every shortcut you take against the gauntlet destroys the only basis of trust. ## The Loop ``` SPEC → (human approves spec, not code) → RED → GREEN → REFACTOR → GAUNTLET → EVIDENCE ↑_____________________| repeat per behavior ``` ### 1. SPEC — the only thing the human reads before code Turn the request into **executable acceptance criteria** before touching implementation files: - Write behaviors as Gherkin-style scenarios or a named test list — concrete inputs, concrete expected outputs, edge cases, and error cases. "Handles bad input" is not a spec; `divide(1, 0) raises ZeroDivisionError with message X` is. - Include what the change must NOT do (invariants that must survive: existing tests, public API signatures, performance budgets if stated). These negative constraints are contract clauses like any scenario: each must end up mapped in EVIDENCE to a test, a gauntlet layer, or an explicit skipped-with-reason line — never silently absent from the mapping. - The spec doubles as the authorization point: include the **setup plan** — tools to install, git usage (init? checkpoint commit cadence?), files the gauntlet will add, and **every new dependency with a one-line justification** (prefer the standard library and deps already present; an unjustified package is a spec defect) — so approving the spec authorizes the environment changes in one step instead of N interruptions, and the human can veto a risky package before it is ever installed. - Show the spec to the human in plain language and get approval **before writing implementation**. In autonomous mode, state the spec in your response and proceed — but the correlation-breaking review never happened, so EVIDENCE must record `spec approval: not obtained (autonomous run)` and claim correspondingly lower confidence; the spec becomes the artifact the human reviews after the fact. - The spec is append-only during the task. If implementation reveals the spec was wrong, say so explicitly and revise it visibly — never silently drift. ### 2. RED — prove each test can fail Write the test for one behavior. **Run it and watch it fail** before writing the implementation. A test you never saw fail proves nothing — it may be testing nothing. Details that matter in practice: - If the module under test doesn't exist yet, create a stub that raises (e.g. `NotImplementedError`) so the test fails on behavior, not on import — a collection error is a weaker RED than an assertion failure. - Related behaviors may share one RED run, as long as each new test is individually observed failing. - If a new test passes immediately, it is either vacuous (fix it) or the behavior already exists. **Don't just assert which — prove it**: break the implementation with a one-off throwaway mutant, watch the test fail, restore. Then record it as pre-existing behavior kept as regression armor. ### 3. GREEN — minimal implementation Write the least code that makes the failing test pass. Run the full suite, not just the new test. ### 4. REFACTOR — clean up under green, assertions frozen Minimal code is often ugly code. While the suite is green, improve names, extract duplication, and simplify structure. What is frozen is **behavioral assertions**, not test files wholesale: - Implementation refactors touch no test files at all. - Test-structure refactors (extracting helpers and fixtures, deduplicating setup) are allowed as a **separate step**: assertions unchanged, suite green before and after, then rerun mutation to confirm the restructured tests still kill — a refactor that blunts the tests is a silent hole in the gauntlet. - Anything that requires editing an assertion isn't refactoring, it's a behavior change and belongs back in SPEC. Run the suite after each refactor. Repeat RED→GREEN→REFACTOR per behavior. ### 5. GAUNTLET — the constraint stack After all spec behaviors are green, run every applicable layer. Scale to the task (see "Calibration"), but never skip a layer silently — if a layer doesn't apply or a tool is unavailable, record that in the evidence report with the reason. | Layer | What it catches | How | |---|---|---| | Full test suite | regressions | project's test command, zero NEW failures (baseline note below) | | Static types | whole classes of bugs | tsc / mypy / etc., zero new errors | | Lint + format | latent bugs, drift | project's linter, zero new warnings | | Coverage on changed lines | untested code paths | every changed/added line executed by a test; branch coverage where the tool supports it. Global % is vanity — changed-line coverage is the constraint | | Mutation testing | tests that assert nothing | see `references/gauntlet.md`. No mutation tool? Do manual mutation: introduce 3–5 plausible bugs into the new code one at a time (flip a comparison, off-by-one a bound, drop a condition, return early); the suite must kill every one. Restore after | | Property-based tests | edge cases you didn't imagine | for parsing, math, serialization, anything with invariants (round-trip, idempotence, ordering) — add hypothesis/fast-check properties | | Complexity budget | unmaintainable output | new functions small and single-purpose; if a function needs a paragraph to explain, split it | | Real execution | "passes tests, doesn't run" | actually run the app/CLI/endpoint once on a realistic input, not only the test harness | | Supply chain & secrets | vulnerable/unnecessary deps, leaked credentials | when the dependency set changed: audit it (pip-audit / npm audit / govulncheck / cargo-audit) and check licenses; scan the diff for secrets; every new dependency must trace back to its SPEC justification. Also eyeball the capability diff: did the change start using network / subprocess / filesystem / env it didn't before? | | Suite health | flaky or order-dependent tests | run the suite in randomized order (pytest-randomly etc.); repeat suspected flakes. Every EVIDENCE number rests on the suite being deterministic — a flaky suite quietly invalidates the report | Baseline note — on a repo with pre-existing failures, record the baseline first (which tests already fail, verbatim) and hold the line at zero NEW failures. Fixing unrelated pre-existing failures is scope creep: surface them, don't silently "improve" them. Mutation caveat — **kills are attributed to whichever test fails first**, so a 7/7 kill score validates the suite as a whole, not every layer in it. In Tier 3, rerun the mutants against the property suite alone before claiming the properties verify anything; survivors there mean the invariants have blind spots (a common one: a one-sided invariant like "never exceeds limit" cannot catch fail-closed bugs — pair it with the opposite bound). Equivalent-mutant note — with a mutation tool, a survivor is not automatically a failure: some mutants are semantically equivalent to the original and cannot be killed. Classify such survivors as "equivalent, because " in EVIDENCE rather than adding a meaningless test to kill them — that would violate anti-gaming rule 4. Hand-written mutants (the manual procedure) get no such excuse: you chose them, so choose real bugs. ### 6. EVIDENCE — the only thing the human reads after code End with a report the human can trust without opening a single source file (template in `references/gauntlet.md`): - The approved spec, with each behavior mapped to the test that verifies it. - Each gauntlet layer: the command run, and its actual result (pasted numbers, not adjectives). "All 47 tests pass, changed-line coverage 100% (31/31 lines), 5/5 manual mutants killed" — never "tests look good". - All numbers must come from one final fresh run executed after the last code edit — results from mid-task runs are stale and must not be reported. - The report must be reproducible from the repo alone: every command it cites (including the mutation script) must exist as a persisted file in the repo, not in a scratch directory or only in the conversation. Reproducible means: dev-tool versions pinned or recorded, one entry-point command that reruns every layer, and the source state identified (commit SHA, or a source-tree hash when git is absent). - Layers skipped, and why. - Anything that failed and how it was resolved, honestly. A gauntlet you passed on the first try and a gauntlet you fixed your way through are equally fine; a gauntlet you quietly weakened is the only failure. ## Anti-Gaming Rules (absolute) The gauntlet only creates trust if it cannot be gamed. These are hard rules: 1. **Never weaken a test to make it pass.** Don't broaden assertions, add skips, raise tolerances, or delete a failing test. If a test seems wrong, that's a spec conversation — surface it, don't bury it. 2. **Never edit a test and the implementation in the same step to reach green.** Change one, run, then the other. Simultaneous edits let you accidentally redefine correctness to match your bug. 3. **Never mock the unit under test** or mock so much that the test only exercises the mocks. Mock boundaries (network, clock, filesystem), not logic. 4. **Never chase the coverage number.** Coverage is a detector of untested code, not a target. A test added only to touch lines, with no meaningful assertion, is gaming — mutation testing exists precisely to catch this, including yours. 5. **Never report a layer you didn't run.** An honest "skipped: no mutation tool in this environment, did manual mutation instead" preserves trust; an invented result destroys the entire scheme. 6. **Failing gauntlet blocks done.** You are not finished while any layer fails. If you're genuinely blocked, report the failure verbatim as the outcome. ## Calibration Scale effort to blast radius, and say which tier you chose: - **Tier 1 — trivial** (typo, comment, config value): full suite + lint. No new tests required, but state why the change is untestable or already covered. - **Tier 2 — normal** (bug fix, small feature): full loop. Bug fixes MUST start with a RED test reproducing the bug — the fix is not done until yesterday's bug is tomorrow's regression test. - **Tier 3 — high stakes** (money, auth, data loss, concurrency, public API): start with a short **failure model**: list the ways this specific change can hurt (race condition, partial write, hostile input, overflow, unbounded growth, failed rollback…), and for each mode add a layer that can actually catch it — race/stress tests for concurrency, fuzzing for parsers, rollback rehearsal for migrations, benchmarks for latency budgets, API-compatibility checks for public libraries, contract tests for service boundaries, logging/metric assertions where silent production failure is a mode (full menu in `references/gauntlet.md`). Mutation and coverage cannot substitute for these; the generic gauntlet is the floor, not the ceiling. Then: full loop + property-based tests + mutation testing (tool-based if available) + adversarial pass — one explicit step trying to break your own implementation with hostile inputs before declaring done. Failure modes deliberately not covered go in EVIDENCE as known limits. ## Setup If the project has no test runner, no linter, or no type checking, set up the minimal standard toolchain for the language **first** (see `references/gauntlet.md`). A gauntlet can't run on bare ground. Setup changes the user's environment — packages, config files, lockfiles — so it belongs in the SPEC's setup plan, where spec approval authorizes it in one step; record every environment change actually made in the evidence report. If the user forbids adding tooling, fall back to manual layers (manual mutation, manual execution) and record the reduced confidence honestly. If the directory is not a git repository, propose `git init` in the SPEC's setup plan. Version control is itself a gauntlet layer: commit at SPEC and at each GREEN/REFACTOR checkpoint, so mutant restores are verifiable with `git diff` (not by eyeball), a bad refactor is rolled back instead of debugged, and the final diff shows exactly what changed. Checkpoint commits happen only under that spec-approved authorization (or an explicit user request) — never impose a commit cadence on a repo whose owner hasn't agreed to it. If the user declines or git is unavailable, record that in EVIDENCE — mutant restores then rest on rerunning the suite, a weaker guarantee — and identify the source state with a tree hash instead of a SHA. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [AmazingAng](https://github.com/AmazingAng) - **Source:** [AmazingAng/old-coder](https://github.com/AmazingAng/old-coder) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** yes - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-amazingang-old-coder-old-coder - Seller: https://agentstack.voostack.com/s/amazingang - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.