Implementation

Verification-driven coding with tight feedback loops. Distilled from 21,321 tracked operations across 64+ projects, 612 debugging sessions, and 2,476 conversation histories. These are the patterns that consistently ship working code.

Core insight: 2-3 edits then verify. 73% of fixes go unverified — that's the #1 quality gap. The difference between a clean session and a debugging spiral is verification cadence.

The Sequence

Every implementation follows the same macro-sequence, regardless of scale:

digraph implement {
    rankdir=LR;
    node [shape=box];

    "ORIENT" [style=filled, fillcolor="#e8e8ff"];
    "PLAN" [style=filled, fillcolor="#fff8e0"];
    "IMPLEMENT" [style=filled, fillcolor="#ffe8e8"];
    "VERIFY" [style=filled, fillcolor="#e8ffe8"];
    "COMMIT" [style=filled, fillcolor="#e8e8ff"];

    "ORIENT" -> "PLAN";
    "PLAN" -> "IMPLEMENT";
    "IMPLEMENT" -> "VERIFY";
    "VERIFY" -> "IMPLEMENT" [label="fix", style=dashed];
    "VERIFY" -> "COMMIT" [label="pass"];
}

ORIENT — Read existing code before touching anything. Grep -> Read -> Read is the dominant opening. Sessions that read 10+ files before the first edit require fewer fix iterations. Never start with blind changes.

PLAN — Scale-dependent (see below). Skip for trivial fixes, write a task list for features, run a research swarm for epics.

IMPLEMENT — Work in batches of 2-3 edits, then verify. Follow the dependency chain. Edit existing files 9:1 over creating new ones. Fix errors immediately — don't accumulate them.

VERIFY — Typecheck is the primary gate. Run it after every 2-3 edits. Run tests after feature-complete. Run the full suite before commit.

COMMIT — Tests are the final gate. Stage specific files only, never git add -A. HEREDOC commit messages with conventional commit format.

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Scale Selection

Strategy changes dramatically based on scope. Pick the right weight class:

Scale	Edits	Strategy
Trivial (config, typo)	1-5	Read -> Edit -> Verify -> Commit
Small fix	5-20	Grep error -> Read -> Fix -> Test -> Commit
Feature	50-200	Plan -> Layer-by-layer impl -> Verify per layer
Subsystem	300-500	Task planning -> Wave dispatch -> Layer-by-layer
Epic	1000+	Research swarm -> Spec -> Parallel agents -> Integration

Skip planning when: Scope is clear, single-file change, fix describable in one sentence.

Plan when: Multiple files, unfamiliar code, uncertain approach.

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Dependency Chain

Build things in this order. Validated across fullstack, Rust, and monorepo projects:

Types/Models -> Backend Logic -> API Routes -> Frontend Types -> Hooks/Client -> UI Components -> Tests

Fullstack (Python + TypeScript):

1. Database model + migration
2. Service/business logic layer
3. API routes (FastAPI or tRPC)
4. Frontend API client
5. React hooks wrapping API calls
6. UI components consuming hooks
7. Lint -> typecheck -> test -> commit

Rust:

1. Error types (thiserror enum with #[from])
2. Type definitions (structs, enums)
3. Core logic (impl blocks)
4. Module wiring (mod.rs re-exports)
5. cargo check -> cargo clippy -> cargo test

Key finding: Database migrations are written AFTER the code that needs them. Frontend drives backend changes as often as the reverse.

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Verification Cadence

The single most impactful practice. Get this right and everything else follows.

Gate	When	Speed
Typecheck	After every 2-3 edits	Fast (primary gate)
Lint (autofix)	After implementation batch	Fast
Tests (specific)	After feature complete	Medium
Tests (full suite)	Before commit	Slow
Build	Before PR/deploy only	Slowest

The Edit-Verify-Fix Cycle

The sweet spot: 3 changes -> verify -> 1 fix. This is the most common successful pattern.

The expensive pattern: 2 changes -> typecheck -> 15 fixes (type cascade). Prevent by grepping all consumers before modifying shared types.

Combined gates save time: turbo lint:fix typecheck --filter=pkg runs both in one shot. Scope verification to affected packages, never the full monorepo.

Practical tips:

• Run lint:fix BEFORE lint check to reduce iterations
• cargo check over cargo build (2-3x faster, same error detection)
• Truncate verbose output: 2>&1 | tail -20
• Wrap tests with timeout: timeout 120 uv run pytest

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Decision Trees

Read vs Edit

Familiar file you edited this session?
  Yes -> Edit directly (verify after)
  No  -> Read it this session?
    Yes -> Edit
    No  -> Read first (79% of quick fixes start with reading)

Subagents vs Direct Work

Self-contained with a clear deliverable?
  Yes -> Produces verbose output (tests, logs, research)?
    Yes -> Subagent (keeps context clean)
    No  -> Need frequent back-and-forth?
      Yes -> Direct
      No  -> Subagent
  No -> Direct (iterative refinement needs shared context)

Refactoring Approach

Can changes be made incrementally?
  Yes -> Move first, THEN consolidate (separate commits)
        New code alongside old, remove old only after tests pass
  No  -> Analysis phase first (parallel review agents)
        Gap analysis: old vs new function-by-function
        Implement gaps as focused tasks

Bug Fix vs Feature vs Refactor

Type	Cadence	Typical Cycles
Bug fix	Grep error -> Read 2-5 files -> Edit 1-3 files -> Test -> Commit	1-2
Feature	Plan -> Models -> API -> Frontend -> Test -> Commit	5-15
Refactor	Audit -> Gap analysis -> Incremental migration -> Verify parity	10-30+
Upgrade	Research changelog -> Identify breaking changes -> Bump -> Fix consumers	Variable

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Error Recovery

65% of debugging sessions resolve in 1-2 iterations. The remaining 35% risk spiraling into 6+ iterations.

Quick Resolution (Do This)

1. Read relevant code first (79% success correlation)
2. Form explicit hypothesis: "The issue is X because Y"
3. Make ONE targeted fix
4. Verify the fix worked

Spiral Prevention (Avoid This)

1. Separate error domains — fix ALL type errors first, THEN test failures. Never interleave.
2. 3-strike rule — after 3 failed attempts on same error: change approach entirely, or escalate.
3. Cascade depth > 3 — pause, enumerate ALL remaining issues, fix in dependency order.
4. Context rot — after ~15-20 iterations, /clear and start fresh. A clean session with a better prompt beats accumulated corrections every time.

The Two-Correction Rule

If you've corrected the same issue twice, /clear and restart. Accumulated context noise defeats accuracy.

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Anti-Patterns

Anti-Pattern	Fix
20+ edits without verification	Verify every 2-3 edits
Fix without verifying the fix (73% of fixes!)	One fix, one verify, repeat
fix -> fix -> fix chains without checking	Always verify between fixes
Editing without reading first	Read the file immediately before editing
Writing tests from memory	Read actual function signatures first
Changing shared types without grepping consumers	Grep all usages before modifying shared types
Mixing move and change in one commit	Move first commit, change second commit
Debugging spiral past 3 attempts	Change approach or escalate
Premature optimization	Correctness first, optimize after tests pass

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Cross-Model Review

For high-stakes changes, use /hyperskills:codex-review after implementation. A fresh model context eliminates implementation bias and catches real bugs: migration idempotency, PII in debug logging, empty array edge cases, missing batch limits.

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References

For quantitative benchmarks and implementation archetype templates, consult references/benchmarks.md.

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What This Skill is NOT

• Not a gate. Don't follow all five phases for a typo fix. Scale selection exists for a reason.
• Not a replacement for reading code. This skill tells you HOW to implement, not WHAT to implement.
• Not a planning tool. Use /hyperskills:plan for task decomposition.
• Not an excuse to skip tests. "Verify" means running actual checks, not eyeballing the diff.