implement

name	implement
description	Use this skill when writing code, building features, fixing bugs, refactoring, or any multi-step implementation work. Activates on mentions of implement, build this, code this, start coding, fix this bug, refactor, make changes, develop this feature, implementation plan, coding task, write the code, or start building.

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: Verify in tight loops, roughly every 2-3 edits. 73% of fixes go unverified across the dataset, which is the single biggest quality gap. Sessions that maintain a tight verify cadence avoid the debugging spirals that the rest of this skill is designed to prevent.

How to read this skill: the loop and the heuristics below are calibrated for non-trivial implementation work. Trivial fixes (config, typo, single-line) shouldn't drag through five phases. Use judgment, scale planning to scope, and skip what doesn't apply. The Code Discipline section is principles that bias toward caution; for one-line changes, just make the change.

The Loop

Implementation work composes from five things that must all be true by the time a chunk ships. Not a fixed order; the graph below shows the natural dependencies and the loop-backs that are routine in practice.

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"];
    "COMMIT" -> "IMPLEMENT" [label="next chunk", style=dashed];
}

• Oriented. Existing code is read before anything gets touched. Grep → Read → Read is the dominant opening across the dataset. Sessions that read 10+ files before the first edit require fewer fix iterations downstream. Blind changes are the most expensive way to start.

• Planned. Decomposition exists at the right scale. Trivial fixes don't need a plan; features benefit from a task list; epics earn a research swarm. The decision is "what's proportional," not "always plan."

• Implemented. Work happens in batches of roughly 2-3 edits, then verifies. Follow the dependency chain. Edit existing files 9:1 over creating new ones; that's the observed ratio in successful sessions. Fix errors as they surface; accumulating them creates cascade-debugging.

• Verified. Typecheck is the primary gate, fast and cheap; run it between batches. Tests fit naturally after feature-complete. Full suite before commit.

• Committed. Atomic chunks, committed as you go. Stage specific files, commit, loop back to the next chunk. Many small commits per session is the pattern that consistently outperforms one mega-commit at the end. See Commit Cadence below for message anatomy.

Trajectories that compose these:

• Typo fix: Oriented (read the file) → Implemented → Verified (typecheck) → Committed. No Planned needed.
• Standard feature: Oriented → Planned → Implemented → Verified → Committed → loop back into Implemented for the next chunk.
• Bug with cascade: Oriented → Implemented → Verified (fails) → re-Implemented → Verified (passes) → Committed. The dashed VERIFY → IMPLEMENT edge above is this case.
• Epic with phases: Oriented → Planned (decomposition) → (Implemented → Verified → Committed) per phase → re-Oriented when the next phase changes the picture.

---

Code Discipline

Principles that shape how you move through the loop, not steps to execute. Adapted from Karpathy's observations on LLM coding pitfalls: models "make wrong assumptions on your behalf and just run along with them without checking ... really like to overcomplicate code and APIs, bloat abstractions ... implement a bloated construction over 1000 lines when 100 would do."

These principles bias toward caution because that's where models drift. For trivial fixes, the calculus inverts; apply judgment, not the full discipline.

Think before coding

Don't assume. Don't hide confusion. Surface tradeoffs.

Situation	Action
Multiple interpretations of the request	Present them; don't pick silently
A simpler approach is plausible	Say so; push back when warranted
Something is unclear	Stop; name what's confusing; ask
You hold a load-bearing assumption	State it explicitly
Inconsistency between request and code	Surface it before proceeding

ORIENT (read the code) is the prerequisite. This principle is what to do with what you find: name the gaps, don't paper over them.

Simplicity first

Minimum code that solves the problem. Nothing speculative.

Don't	Do
Add features beyond what was asked	Solve exactly the stated problem
Build abstractions for single-use code	Inline first; abstract when reused
Add "flexibility" or configurability not asked	Hardcode now; parameterize on demand
Handle errors for impossible scenarios	Trust internal invariants; validate at edges
Write 200 lines when 50 would do	Rewrite tighter

The test: would a senior engineer call this overcomplicated? If yes, simplify.

Surgical changes

Touch only what you must. Clean up only your own mess.

Rule	Why
Don't "improve" adjacent code, comments, or formatting	Pollutes the diff; outside your scope
Don't refactor code that isn't broken	Scope creep expands blast radius
Match existing style even if you'd do it differently	Local consistency beats your preferences
Notice unrelated dead code → mention, don't delete	Other branches/agents may rely on it
Remove imports/vars/funcs your changes orphaned	Clean up after yourself
Leave pre-existing dead code alone	Outside your remit unless explicitly asked
Don't touch comments you don't understand	Karpathy: "side effects ... orthogonal to task"

The test: every changed line should trace directly to the user's request.

Goal-driven execution

Define verifiable success. Loop until it passes.

Vague task	Verifiable goal
"Add validation"	Write tests for invalid inputs, then make them pass
"Fix the bug"	Write a test that reproduces it, then make it pass
"Refactor X"	Ensure the same tests pass before and after
"Make it work"	Reject, name the actual signal that proves it works

For multi-step work, state the plan with verification per step:

1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]

Strong success criteria let you loop independently. Weak criteria require constant clarification.

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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 in the dataset. Tight loops here make the rest of the skill mostly unnecessary; loose loops make every other guideline harder to follow.

Gate	Typically	Speed
Typecheck	Between edit batches	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 pattern that consistently produces clean sessions: ~3 changes → verify → 1 fix. Sweet spot, not a hard ratio. Sometimes one edit warrants its own verify, sometimes five edits group cleanly.

The pattern that produces debugging spirals: 2 changes → typecheck → 15 cascading fixes. Prevent type cascades by grepping consumers before modifying shared types; once the cascade starts, separate fix domains (see Error Recovery).

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+. The patterns below are calibrated to keep you in the first bucket.

Quick resolution

• Read the relevant code first (79% success correlation in the dataset)
• Form an explicit hypothesis: "the issue is X because Y"
• Make one targeted fix
• Verify the fix actually worked before moving on

Spiral prevention

• Separate error domains. Fix all type errors before chasing test failures; interleaving the two is how cascades compound.
• 3-strike heuristic. Three failed attempts on the same error is the signal to change approach entirely or escalate, not to try variation #4.
• Cascade depth > 3. Pause, enumerate all remaining issues, then fix in dependency order rather than reactive whack-a-mole.
• Context rot. After ~15-20 iterations, /clear and start fresh. A clean session with a better prompt usually outperforms accumulated corrections.

Two-correction rule

Correcting the same issue twice is the signal to /clear and restart. Context noise compounds faster than fixes resolve it.

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Commit Cadence

Commit each logical chunk as it lands and verifies. Many small commits per session is the norm, never accumulate hours of unrelated work into one mega-commit. The COMMIT step loops back to IMPLEMENT for the next chunk.

When to commit

Trigger	Action
Logical chunk done, verification passes	Commit
Move/rename complete, before behavioral changes	Commit (move)
Behavioral change works after the move commit	Commit (change)
Refactor extracted, callers still pass	Commit
About to switch to a different concern	Commit current
Verification fails or edit is speculative	Don't commit

If a reviewer would want it as a separate diff, it's a separate commit.

Mirror local style

Before the first commit in any repo, run git log -10 --oneline and mirror the pattern. Mirror format, not quality, terse history doesn't lower your bar. Default to Conventional Commits when no pattern exists.

Pattern	Example
Conventional Commits	feat(api): add token refresh
Gitmoji	✨ Add token refresh
Ticket prefix	[ENG-1234] Add token refresh
Module prefix	auth: add token refresh
Plain	Add token refresh

Conventional Commit types: feat (capability), fix (bug), refactor (no behavior change), perf, test, docs, style (formatting only), chore (tooling/deps), build, ci. Scope is optional but encouraged when the change is localized.

Message anatomy

Subject: imperative mood, ≤76 chars, no trailing period, no filenames. "Fix null deref in token refresh" beats "Fix bug." For Conventional Commits, no emoji in the subject, it breaks parsers.

Body (always include one): wrap at 76 chars, separated from subject by a blank line. Explain why, the diff shows what. State facts: banish "likely", "probably", "might", "seems", "appears to". If you don't know what a change does, read more before committing. Two sentences usually suffices; mention load-bearing context a future bisect would want.

HEREDOC + Co-Author

Always pass messages via HEREDOC to preserve formatting. Add a Co-Authored-By trailer that names the model, "Claude" alone doesn't disambiguate across multi-agent sessions.

git commit -m "$(cat <<'EOF'
fix(auth): guard against null session in token refresh

Refresh racing with logout was dereferencing a freed session, surfacing
as a 500 with no log trail. Early return plus a single warn log makes
the failure mode visible without spamming on every refresh.

Co-Authored-By: Nova (Claude Opus 4.7) <[e-mail]>
EOF
)"

Examples

Bad	Good
fix: bug	fix(api): resolve null deref in token refresh
update stuff	chore(deps): bump axios to 1.7.4
WIP	feat(auth): scaffold magic-link sign-in flow
Added new file for users	feat(users): add bulk import endpoint
feat: it works now	feat(search): add fuzzy matching to user lookup

Multi-agent staging

Other agents may be working in parallel:

git status                # See the full picture first
git diff --staged         # Review what you're about to commit
git add <specific-files>  # Only files you personally touched

Never git add -A or git add . (catches other agents' WIP and secrets). Never git restore files you didn't modify. Never git push without explicit request, push is the human's call. Skip planning docs, scratch files, and .local.md from the repo.

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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
One mega-commit at end of session	Commit each logical chunk as it lands
Bare titles like fix: bug or update stuff	Specific subject + body explaining why
Skipping the body to "save time"	Always include a body, even two sentences
Filenames or paths in the subject line	Describe the behavior, not the file
Uncertain language ("might fix", "should work")	State facts; read more code if you don't know
git add -A / git add .	Stage specific files only
git push without explicit request	Push is the human's call; never autonomous
Silently picking one interpretation	Surface options; ask before committing to one
"Improving" code adjacent to your change	Stay surgical; touch only what's asked
Touching comments you don't understand	Leave them; not your scope
Bloated abstraction for single-use code	Write the function; abstract when reused
Vague "make it work" goal	Define a verifiable check first

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Cross-Model Review

For high-stakes changes, run /hyperskills:cross-model-review after implementation. A different reviewer model has different blind spots than the author and catches real bugs: migration idempotency, PII in debug logging, empty-array edge cases, missing batch limits. Use /hyperskills:codex-review only when you specifically want the Claude → Codex direction with codex review subcommand semantics.

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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.