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Implement plan

Skill akshay-diwadkar/skills/skills/engineering/implement-plan

Reusable engineering skills for AI coding assistants—covering codebase mapping, auditing, architecture, issue scoping, change planning, implementation, optimization, and diagramming.

Install
npx -y skills add akshay-diwadkar/skills --skill implement-plan

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Execute an approved implementation plan as the smallest complete patch — preserving existing patterns and uncommitted work, with layered verification and an exact change report. Use when the user has an approved or written plan and asks to implement, apply, or build it. Vague plans are refused back to planning.

SKILL.md

8.3 KB, as published. Nobody here has run it

Implement Plan

Implement the approved plan as the smallest complete patch. Repository evidence decides how code is written; the plan decides what behavior may change; the implementation contract proves what actually happened.

Read Before Acting

Read these files completely before editing:

  1. references/implementation-contract.json — authoritative run-bundle fields, statuses, and safety policy.
  2. references/implementation-protocols.md — canonical intake, editing, propagation, verification, and recovery procedure.
  3. references/code-quality-checklist.md — per-file and final quality gates.
  4. references/implementation-hazards.md — exact stop/recovery decisions.

Non-Negotiables

  • Identify and snapshot the exact approved plan before editing.
    • Require finalized plan-contract: 5 metadata, the complete typed record graph, targeted repository binding, and a v5 finalizer receipt. Reject every other version and do not translate it.
  • Refuse a plan when the parser reports ambiguity, unfinalized status, receipt mismatch, or a material repository contradiction.
  • Never use an implementation interview to reinterpret, repair, or extend approved product intent. Record semantic gaps and route them to plan-change; ask only for execution-state authorization already permitted by this contract.
  • Create an implementation-run bundle in confirmed ignored storage or an OS temporary directory.
  • Preserve unrelated dirty paths byte-for-byte. Never edit a dirty target without explicit user authorization.
  • Recheck a target against the last recorded snapshot before every edit; stop on concurrent changes.
  • Apply planned changes in dependency order and implement every specified branch, error, side effect, execution blueprint, and test.
  • Allow unplanned edits only under the Mechanical Propagation Gate in the canonical protocol.
  • Attribute a failure as pre-existing only when the exact check failed in the recorded pre-edit baseline; otherwise use unknown-baseline.
  • Reverse only positively identified agent-owned hunks whose current context still matches. Never perform automatic whole-file, worktree, or branch restoration.
  • Run finalize_implementation.py to stamp a SHA-256 validation receipt into the bundle before claiming completion.

Skill Directory Resolution

Execute bundled runtime commands with the active skill directory (the directory containing this SKILL.md) set as the process working directory:

  • On Claude Code: set cwd to "${CLAUDE_SKILL_DIR}" (or the active skill directory) if running from an external working directory.
  • On other platforms: execute commands with process cwd set to the active skill directory.
  • Resolve skill-root as the directory containing SKILL.md and repo-root as the absolute target repository path.
  • All non-script paths (target repository, plan, output, draft, payload, .env, issue JSON, run-dir) passed as arguments MUST be absolute paths.
  • Fail closed if skill-root or repo-root cannot be resolved.
  • Never write output or state files relative to the installed skill package directory.

Execution Gates

1. Normalize the Plan

Save conversational plans verbatim to the run directory. Parse the plan with implementation_contract.parse_plan.

  • Require <!-- plan-contract: 5 -->, strict classification metadata, the complete typed record graph, a valid targeted repository binding, and a v5 receipt. Revalidate bound evidence and targets before creating the run bundle.
  • Stop with field-specific diagnostics when parsing or receipt validation fails. Reject all v1/v2/legacy plans. Do not reinterpret the plan.

If inspection exposes a semantic contradiction or a choice affecting product behavior, failure semantics, contracts, persistence, dependencies, migration, or external effects, stop and hand the evidence back to plan-change. Dirty-target incorporation and explicitly scoped unsafe/external-operation authorization remain execution questions; their answers do not revise the plan.

2. Scaffold and Inspect

Create the run bundle from the active skill directory:

python scripts/scaffold_implementation.py \
  --repo-root /absolute/path/to/repository \
  --plan /absolute/path/to/run-dir/plan.md \
  --output /absolute/path/to/run-dir/implementation.json

Use .scratch/implement-plan/<run-id>/ only when git check-ignore confirms it is ignored; otherwise use an OS temporary directory.

Before editing:

  • Inspect repository guidance, status, manifests, affected code, callers, tests, fixtures, configuration, and generated surfaces.
  • Record local naming, imports, errors, logging, comments, test, and analogue patterns.
  • Run safe focused baseline checks when practical and record their command, exit code, and evidence.
  • Stop on dirty plan targets unless the bundle records explicit user authorization.

3. Implement in Dependency Order

For each CH-n:

  1. Re-read its exact path, anchor, behavior, branches, errors, ordering, side effects, and corresponding Execution Blueprints (pseudocode, Mermaid diagrams, before/after shapes, or tables).
  2. Verify the target still matches the last snapshot.
  3. Apply the smallest edit following the nearest repository analogue and execution blueprint logic.
  4. Record a planned change with its CH-n, paths, anchors, before/after hashes, and evidence.
  5. Run the narrowest useful smoke check and record its evidence.

If an omitted caller, fixture, or compatibility edit appears, apply the Mechanical Propagation Gate before touching it.

4. Implement Tests

Translate every T-n into the repository's existing test style. Use its exact setup/input and observable output, error, or side effect. Prefer behavioral assertions over internal-call assertions unless the plan explicitly specifies the interaction.

Run focused tests individually, then together. Record the command, expected result, actual exit code, evidence path, linked T-n, and status.

5. Verify and Reconcile

Run, in order:

  1. Every plan T-n command.
  2. Regression tests for affected modules.
  3. Configured type and lint checks for changed surfaces.
  4. Every additional plan-specified command.

Reconcile actual workspace status against the initial bundle. Every new changed path must be covered by a planned or mechanical-propagation record. Initial unrelated dirty paths must retain their original hashes.

6. Validate Completion

Finalize status, unresolved CH/T records, final changed paths, deviations, residual risks, and report summary. Then run from the active skill directory:

python scripts/finalize_implementation.py \
  --repo-root /absolute/path/to/repository \
  --plan /absolute/path/to/run-dir/plan.md \
  /absolute/path/to/run-dir/implementation.json

The finalizer runs all bundle and workspace validation checks in-process. On success, it stamps a SHA-256 validation receipt (validation_receipt) into the bundle JSON. Submit only the finalized output. A failed or unfinalized bundle blocks implementation completion.

7. Report

Report:

  • Plan source, contract version, and tier.
  • Planned changes by CH-n, path, and anchor.
  • Mechanical propagation with owning CH-n, evidence, and verification.
  • Commands and exact results, including skipped or blocked checks.
  • Final status, residual risks, unresolved records, and required follow-up.

Never claim weaker-model reliability unless the provider-neutral live evaluation suite has completed for the named model with no hard failures, median score at least 90, and every run at least 80.

Handoffs

  • Use plan-change when the approved input cannot pass strict intake.
  • Use plan-change when repository evidence exposes a semantic plan gap; do not grill the user to repair approved intent inside implementation.
  • Use audit-codebase to discover unknown risks instead of implementing a known change.
  • Use optimize-codebase when selecting or measuring an optimization rather than applying an approved implementation plan.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.