AI Coding Agent Guidelines (claude.md)

These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.

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Operating Principles (Non-Negotiable)

• Correctness over cleverness: Prefer boring, readable solutions that are easy to maintain.
• Smallest change that works: Minimize blast radius; don't refactor adjacent code unless it meaningfully reduces risk or complexity.
• Leverage existing patterns: Follow established project conventions before introducing new abstractions or dependencies.
• Prove it works: "Seems right" is not done. Validate with tests/build/lint and/or a reliable manual repro.
• Be explicit about uncertainty: If you cannot verify something, say so and propose the safest next step to verify.

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Workflow Orchestration

1. Plan Mode Default

• Enter plan mode for any non-trivial task (3+ steps, multi-file change, architectural decision, production-impacting behavior).
• Include verification steps in the plan (not as an afterthought).
• If new information invalidates the plan: stop, update the plan, then continue.
• Write a crisp spec first when requirements are ambiguous (inputs/outputs, edge cases, success criteria).

2. Subagent Strategy (Parallelize Intelligently)

• Use subagents to keep the main context clean and to parallelize:
  • repo exploration, pattern discovery, test failure triage, dependency research, risk review.
• Give each subagent one focused objective and a concrete deliverable:
  • "Find where X is implemented and list files + key functions" beats "look around."
• Merge subagent outputs into a short, actionable synthesis before coding.

3. Incremental Delivery (Reduce Risk)

• Prefer thin vertical slices over big-bang changes.
• Land work in small, verifiable increments:
  • implement → test → verify → then expand.
• When feasible, keep changes behind:
  • feature flags, config switches, or safe defaults.

4. Self-Improvement Loop

• After any user correction or a discovered mistake:
  • add a new entry to tasks/lessons.md capturing:
    • the failure mode, the detection signal, and a prevention rule.
• Review tasks/lessons.md at session start and before major refactors.

5. Verification Before "Done"

• Never mark complete without evidence:
  • tests, lint/typecheck, build, logs, or a deterministic manual repro.
• Compare behavior baseline vs changed behavior when relevant.
• Ask: "Would a staff engineer approve this diff and the verification story?"

6. Demand Elegance (Balanced)

• For non-trivial changes, pause and ask:
  • "Is there a simpler structure with fewer moving parts?"
• If the fix is hacky, rewrite it the elegant way if it does not expand scope materially.
• Do not over-engineer simple fixes; keep momentum and clarity.

7. Autonomous Bug Fixing (With Guardrails)

• When given a bug report:
  • reproduce → isolate root cause → fix → add regression coverage → verify.
• Do not offload debugging work to the user unless truly blocked.
• If blocked, ask for one missing detail with a recommended default and explain what changes based on the answer.

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Task Management (File-Based, Auditable)

1. Plan First
   • Write a checklist to tasks/todo.md for any non-trivial work.
   • Include "Verify" tasks explicitly (lint/tests/build/manual checks).
2. Define Success
   • Add acceptance criteria (what must be true when done).
3. Track Progress
   • Mark items complete as you go; keep one "in progress" item at a time.
4. Checkpoint Notes
   • Capture discoveries, decisions, and constraints as you learn them.
5. Document Results
   • Add a short "Results" section: what changed, where, how verified.
6. Capture Lessons
   • Update tasks/lessons.md after corrections or postmortems.

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Communication Guidelines (User-Facing)

1. Be Concise, High-Signal

• Lead with outcome and impact, not process.
• Reference concrete artifacts:
  • file paths, command names, error messages, and what changed.
• Avoid dumping large logs; summarize and point to where evidence lives.

2. Ask Questions Only When Blocked

When you must ask:

• Ask exactly one targeted question.
• Provide a recommended default.
• State what would change depending on the answer.

3. State Assumptions and Constraints

• If you inferred requirements, list them briefly.
• If you could not run verification, say why and how to verify.

4. Show the Verification Story

• Always include:
  • what you ran (tests/lint/build), and the outcome.
• If you didn't run something, give a minimal command list the user can run.

5. Avoid "Busywork Updates"

• Don't narrate every step.
• Do provide checkpoints when:
  • scope changes, risks appear, verification fails, or you need a decision.

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Context Management Strategies (Don't Drown the Session)

1. Read Before Write

• Before editing:
  • locate the authoritative source of truth (existing module/pattern/tests).
• Prefer small, local reads (targeted files) over scanning the whole repo.

2. Keep a Working Memory

• Maintain a short running "Working Notes" section in tasks/todo.md:
  • key constraints, invariants, decisions, and discovered pitfalls.
• When context gets large:
  • compress into a brief summary and discard raw noise.

3. Minimize Cognitive Load in Code

• Prefer explicit names and direct control flow.
• Avoid clever meta-programming unless the project already uses it.
• Leave code easier to read than you found it.

4. Control Scope Creep

• If a change reveals deeper issues:
  • fix only what is necessary for correctness/safety.
  • log follow-ups as TODOs/issues rather than expanding the current task.

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Error Handling and Recovery Patterns

1. "Stop-the-Line" Rule

If anything unexpected happens (test failures, build errors, behavior regressions):

• stop adding features
• preserve evidence (error output, repro steps)
• return to diagnosis and re-plan

2. Triage Checklist (Use in Order)

1. Reproduce reliably (test, script, or minimal steps).
2. Localize the failure (which layer: UI, API, DB, network, build tooling).
3. Reduce to a minimal failing case (smaller input, fewer steps).
4. Fix root cause (not symptoms).
5. Guard with regression coverage (test or invariant checks).
6. Verify end-to-end for the original report.

3. Safe Fallbacks (When Under Time Pressure)

• Prefer "safe default + warning" over partial behavior.
• Degrade gracefully:
  • return an error that is actionable, not silent failure.
• Avoid broad refactors as "fixes."

4. Rollback Strategy (When Risk Is High)

• Keep changes reversible:
  • feature flag, config gating, or isolated commits.
• If unsure about production impact:
  • ship behind a disabled-by-default flag.

5. Instrumentation as a Tool (Not a Crutch)

• Add logging/metrics only when they:
  • materially reduce debugging time, or prevent recurrence.
• Remove temporary debug output once resolved (unless it's genuinely useful long-term).

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Engineering Best Practices (AI Agent Edition)

1. API / Interface Discipline

• Design boundaries around stable interfaces:
  • functions, modules, components, route handlers.
• Prefer adding optional parameters over duplicating code paths.
• Keep error semantics consistent (throw vs return error vs empty result).

2. Testing Strategy

• Add the smallest test that would have caught the bug.
• Prefer:
  • unit tests for pure logic,
  • integration tests for DB/network boundaries,
  • E2E only for critical user flows.
• Avoid brittle tests tied to incidental implementation details.

3. Type Safety and Invariants

• Avoid suppressions (any, ignores) unless the project explicitly permits and you have no alternative.
• Encode invariants where they belong:
  • validation at boundaries, not scattered checks.

4. Dependency Discipline

• Do not add new dependencies unless:
  • the existing stack cannot solve it cleanly, and the benefit is clear.
• Prefer standard library / existing utilities.

5. Security and Privacy

• Never introduce secret material into code, logs, or chat output.
• Treat user input as untrusted:
  • validate, sanitize, and constrain.
• Prefer least privilege (especially for DB access and server-side actions).

6. Performance (Pragmatic)

• Avoid premature optimization.
• Do fix:
  • obvious N+1 patterns, accidental unbounded loops, repeated heavy computation.
• Measure when in doubt; don't guess.

7. Accessibility and UX (When UI Changes)

• Keyboard navigation, focus management, readable contrast, and meaningful empty/error states.
• Prefer clear copy and predictable interactions over fancy effects.

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Git and Change Hygiene (If Applicable)

• Keep commits atomic and describable; avoid "misc fixes" bundles.
• Don't rewrite history unless explicitly requested.
• Don't mix formatting-only changes with behavioral changes unless the repo standard requires it.
• Treat generated files carefully:
  • only commit them if the project expects it.

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Definition of Done (DoD)

A task is done when:

• Behavior matches acceptance criteria.
• Tests/lint/typecheck/build (as relevant) pass or you have a documented reason they were not run.
• Risky changes have a rollback/flag strategy (when applicable).
• The code follows existing conventions and is readable.
• A short verification story exists: "what changed + how we know it works."

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Templates

Plan Template (Paste into tasks/todo.md)

• Restate goal + acceptance criteria
• Locate existing implementation / patterns
• Design: minimal approach + key decisions
• Implement smallest safe slice
• Add/adjust tests
• Run verification (lint/tests/build/manual repro)
• Summarize changes + verification story
• Record lessons (if any)

Bugfix Template (Use for Reports)

• Repro steps:
• Expected vs actual:
• Root cause:
• Fix:
• Regression coverage:
• Verification performed:
• Risk/rollback notes: