dev-implementation

Instructions for carrying out an approved software feature plan and checking that the code matches its design.

In plain words
What is it for?
Use it to implement planned features, update their requirements and design documents, write tests before production code, and verify the finished work.
Why use it?
It provides a defined implementation process, including tests and documentation, so planned work is less likely to drift from the intended result.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/codeaholicguy/ai-devkit/dev-implementation
Any agent
npx skills add codeaholicguy/ai-devkit --skill dev-implementation
Clone the repo
git clone --depth 1 https://github.com/codeaholicguy/ai-devkit

Made for: Claude Code, Codex.

Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 897 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00052 $0.00897
Opus 5 $0.00026 $0.00449
Sonnet 5 $0.00010 $0.00179
Haiku 4.5 $0.00005 $0.00090

Measured 2d ago against content hash c6bbc2c322d6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dev-implementation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/dev-implementation/SKILL.md · 54 lines

How it starts

The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dev Implementation

Run implementation work for configured AI docs features. Before changing docs or code, propose the concrete plan for this phase and wait for user approval unless the user already approved the exact phase plan.

Phase Contract

  1. Run npx ai-devkit@latest lint before phase work.
  2. If working on a named feature, run npx ai-devkit@latest lint --feature <name>.
  3. Read requirements, design, planning, implementation, and testing docs before changes.
  4. Use the tdd skill while executing implementation tasks: write a failing test before production code, then make it pass.
  5. Apply the verify skill before completing tasks or making implementation alignment claims.
  6. Keep testing and implementation docs in lockstep with code. Do not defer all doc updates to final verification.
  7. If parent dev-lifecycle established usable task tracing, emit phase, progress, next-step, blocker, and evidence events per task.

Execute Plan

Use for Phase 5.

  1. Run npx ai-devkit@latest lint --feature <name> and work through the planning doc path it validates. If manual path resolution is unavoidable, first resolve .ai-devkit.json paths.docs, falling back to docs/ai.
  2. Gather context: feature name, planning doc path, supporting docs, current branch, and current diff.
  3. Parse task lists and build an ordered queue by section.
  4. Present the task queue with status: todo, in-progress, done, blocked.
  5. For each task, show context, suggest relevant docs, and outline sub-steps from the design doc when useful.
  6. If task tracing is available, record current task progress and immediate next action per task.
  7. Reuse before writing: grep for existing utilities/functions before adding new ones. Reuse only if it fits cleanly.
  8. Subtract before adding: delete dead paths, redundant guards, stale tests, and one-caller wrappers when they are in task scope.
  9. Handle breaking changes carefully: update all in-repo callers atomically and delete the old internal API; for external/public/cross-service callers, add a new function and deprecate the old one.
  10. Model the domain when implementation friction repeats: replace synchronized flags, branch growth, or duplicated shape assumptions with the smallest domain structure that removes them.
  11. Generate a markdown tracking snippet after each status change.
  12. After each task, update the testing doc with completed scenarios, newly discovered scenarios, and invalidated scenarios. Update the implementation doc with changed files, decisions, design deviations, and edge cases handled.
  13. After each section, ask if new tasks were discovered.
  14. Summarize completed, in-progress, blocked, skipped, new tasks, task-tracing events emitted or why tracing was unavailable, and doc deltas.

Read the full file on GitHub · 54 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 54 lines · 52 tokens per session scan A c6bbc2c322d6

Subscribe to this mod's changes

dev-implementation is a skill published in the GitHub repository codeaholicguy/ai-devkit (1,601 stars, last pushed 2d ago), licensed MIT. It adds 52 tokens to every session and 897 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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