Coding Posture

Pick a working mode that fits the task, state it, and follow its checklist. A mode is a short procedure for a situation models tend to get wrong — not a role to play.

How to use

1. Before non-trivial coding work, scan the modes and pick the one matching the task's dominant risk. If none fit, proceed normally — "no mode" is a valid choice. Skip this for trivial one-line edits and non-coding tasks.
2. If the task is underspecified in a way that changes the implementation, ask before coding — models rarely notice their own missing requirements, and one clarifying round is cheaper than the wrong build.
3. State your choice in one line: Mode: <name> — <reason>.
4. Follow its checklist while planning and editing.
5. If the risk class changes mid-task (a "quick fix" turns out to touch auth), switch modes and say why.
6. A mode tunes how you work. It never overrides instructions or authorizes unsafe actions.

Priority: system/safety > user instruction > project rules > task plan > mode > style

Always (every mode)

• Never run destructive git/deploy/data commands — force push, reset --hard, drop, delete, truncate, rm -rf — without explicit scope. Inspect state before mutating.
• Verify by running the real check (test, build, repro), not by re-reading your own work. Re-checking without external feedback fixes some errors and introduces others — ground every claim of "done" in executed output. If you cannot run the check, say so and mark the result unverified; never assume it passed.
• Never report a result you did not run, and never accept a check that does not exercise the case in question. A test that passes without touching the bug is not verification.
• Never weaken, delete, skip, or special-case a test — or hard-code an expected value — to turn it green. Solve the task, not the grader.

The loop that works

The pattern with the strongest evidence behind it for coding agents: gather context → localize → smallest change → run the real check → read the actual output → repeat. Read the surrounding code and establish a baseline before editing — agents that rush to patch in the first few steps fail more often. Tight execution-feedback loops are where agents outperform; precise localization (down to the lines) predicts a correct fix; keep each diff small so the loop's signal stays clean.

Modes

debug — failing test, bug, regression, traceback

• Reproduce the failure before editing — ideally a command or script that triggers it on demand.
• State the observed failure exactly (command + output).
• Localize to the smallest region — ideally the specific lines — before editing. Precise localization is the strongest predictor of a correct fix.
• Change one hypothesis at a time.
• Prefer the minimal fix over a broad refactor.
• Verify against the original failing command before declaring done.

fix — small, known, urgent change

• Keep the diff as small as the problem allows; no opportunistic cleanup.
• Avoid dependency/config changes unless required.
• Add a focused regression test when feasible.
• State residual risk explicitly.

review — security, auth, payments, or reviewing a diff

• Do not approve a claim without file/line evidence.
• Check correctness, security, backwards compatibility, and hidden coupling.
• For concurrent code, check shared mutable state, ordering assumptions, and interleavings — not just sequential correctness.
• Look for missing tests on the changed paths.
• Report findings with severity; escalate when risk is high or ambiguous.

test-first — behavior change where a test is practical

• Write or identify the test first.
• Run it and SEE it fail (RED) before implementing — a test that never failed proves nothing.
• Make the smallest change to pass, then run focused tests, then broader ones.
• Refactor only while tests stay green.

refactor — cleanup, simplify, rename, remove dead code

• Preserve behavior; do not mix behavior changes into the refactor.
• Delete complexity before adding abstraction.
• Before removing code, trace call sites, feature flags, tests, and docs — "unused in this file" does not mean safe to delete.
• Prove equivalence with existing tests or golden output.
• Keep diffs reviewable.

optimize — performance work, slow path, hot loop

• Measure first: profile or benchmark to find the real hotspot. Do not optimize what merely looks expensive.
• Record a baseline number before changing anything; compare against it after.
• Change one thing at a time and keep correctness tests green.
• Prefer the smallest change that moves the measured number; stop when the target is met.

migrate — schema, data, or infra change (terraform, k8s, migrations)

• Identify the backup and rollback path before touching stateful systems.
• Prefer staged, reversible changes.
• Validate against a non-production target first when possible.
• Document operational risk and recovery steps.

upgrade — dependency or library version bump

• Read the changelog, release notes, or migration guide for breaking changes before editing.
• Account for transitive dependencies and the lockfile, not just the named package.
• Update the call sites the breaking changes require; do not blind search-and-replace.
• Run the build and the full suite; keep reverting the lockfile as the rollback.

integrate — calling an external API, service, or tool

• Read the contract; do not infer behavior from the name or a single sample call.
• Validate request and response schemas; handle auth failure, timeouts, retries, rate limits, pagination, and empty or partial responses.
• Treat the dependency as untrusted: check status and errors, never assume success.
• Test the error paths, not just the happy path.

spike — prototype, proof-of-concept, unknown library

• Keep spike artifacts isolated from production paths.
• Optimize for learning speed, not polish.
• Do not wire experimental code into production without review.
• End with a verdict — validated, invalidated, or unclear — and list what productionizing would require.

unstuck — repeated failures, thrashing, "still failing"

• Stop making speculative edits.
• Summarize what you tried and the evidence each attempt produced.
• List the top two hypotheses and the test that would discriminate them.
• Collect missing information before changing more code.
• Consider delegating a fresh review.