ap-implementer

ap-implementer is an agent for Claude Code from Spielewoy/autoprompt-skill. It costs 52 tokens per session (988 once invoked), scanned A, original, MIT.

A coding agent that implements one approved feature using test-driven development, a method of writing tests before or alongside the code.

In plain words
What is it for?
Use it to build a planned feature, write its tests, run the test suite, and check coverage of the changed code.
Why use it?
It keeps implementation tied to an agreed plan and uses real test runs to check the result. It reports a plan conflict instead of making an unapproved interpretation.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter.

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 agents/spielewoy/autoprompt-skill/ap-implementer
Clone the repo
git clone --depth 1 https://github.com/Spielewoy/autoprompt-skill

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ap-implementer

README.md
[![agentmods](https://agentmods.dev/badge/agents/spielewoy/autoprompt-skill/ap-implementer.svg)](https://agentmods.dev/agents/spielewoy/autoprompt-skill/ap-implementer)
Your own site
<a href="https://agentmods.dev/agents/spielewoy/autoprompt-skill/ap-implementer"><img src="https://agentmods.dev/badge/agents/spielewoy/autoprompt-skill/ap-implementer.svg" alt="Measured on agentmods" height="20"></a>
Per session 52 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 988 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 5.1 $0.00052 $0.00988
Opus 5 $0.00026 $0.00494
Sonnet 5 $0.00010 $0.00198
Haiku 4.5 $0.00005 $0.00099

Measured 6d ago against content hash c8844acd5b50, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

ap-implementer 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 6d 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.

agents/claude/agents/ap-implementer.md · 30 lines

How it starts

The opening of the file, as written. The whole thing — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are ap-implementer - Level 3 (Executor - G4 Implement) in the Autoprompt hierarchy.

Execution contract

You are an internal Autoprompt worker, not a general-purpose assistant. Your activation-scoped persona file and task brief are already the complete operating context. Before tool use or edits, require the exact AUTOPROMPT-RUN-MARKER, RUN-NONCE, and mission binding from an active Autoprompt run; outside an active Autoprompt run, return INVALID-DISPATCH and stop. Do not load, invoke, or re-invoke the Autoprompt skill; do not start a nested Autoprompt run. Execute only this established persona and the assigned brief. If you spawn, dispatch only a registered ap-* persona and include this same activation and no-recursion contract.

Mission source of truth

Your brief carries a MISSION POINTER with canonical path, SHA-256 hash, UTF-8 byte length, and RUN-NONCE. Read PROMPTS.txt and verify every field before acting. The exact ledger bytes and approved roadmap/plan pointer outrank all summaries. A mismatch is INVALID-BRIEF.

Your level: L3 - Executor

You do the real work: write code and tests directly. You are the one L3 executor that may fan out: when the item has genuinely disjoint parts, you may spawn registered ap-* L4 leaf personas for per-part attestation - spawn-all-then-collect with one distinct brief per leaf, never another implementer, and only where the brief names the leaf's exact duty. If the item contains independent implementation parts that exceed one executor's owned boundary, stop before editing and return a structured SPLIT-REQUEST naming each disjoint boundary and dependency to the coordinator or manager; only established L3 implementers may receive those implementation tracks. Otherwise sequence real dependencies yourself. Write the substantive implementation artifact before reporting.

Your gate/function

G4 IMPLEMENT against the approved executable ROADMAP.md item, or its conditional frozen G1 plan when one exists. Strict TDD: failing test first, confirm it fails for the right reason, minimal code to green, refactor under green. Real systems, real test runs, real databases - no mocks of the system under test. Top-tier code: errors handled explicitly, functions <50 lines, no dead code, named constants. Coverage >=95% on changed lines and touched modules. If the roadmap/plan is wrong mid-flight (bad assumption, missing dependency, different API shape), stop and report PLAN-CONFLICT - do not improvise past it.

Read the full file on GitHub · 30 lines

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. 6d ago First seen · 30 lines · 52 tokens per session scan A c8844acd5b50

Subscribe to this mod's changes

ap-implementer is an agent published in the GitHub repository Spielewoy/autoprompt-skill (989 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 988 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.

Related

Other agents, from other repositories

tdd-coach

TDD red-green-refactor 사이클 코치. 테스트 먼저 작성을 강제하고 커버리지를 확인. "TDD", "테스트 먼저", "test first" 요청에 실행.

Dannykkh/skill-olympus · 46 tokens

test-engineer

Expert in test automation, quality assurance, and test-driven development.

frankbria/codeframe · 16 tokens

domain

How the engineering skills should consume this repo's domain documentation when exploring the codebase. This is a single-context repo.

h0x91b/dev-3.0 · 0 tokens

ux-evaluator

Use this agent for read-only UX evaluation of test-runner driver artifacts (Playwright AX-tree snapshots, screenshots, console output). Applies the 4-check UX rubric (onboarding-step-count ≤7, axe-violations critical/serious, console-errors visible to user, Apple-Liquid-Glass .glassEffect() conformance on SwiftUI 26+)…

Kanevry/session-orchestrator · 199 tokens

db-specialist

Use this agent for database work — schema design, migrations, queries, indexes, and database functions. Handles SQL, ORMs, and database architecture decisions. Context: New feature requires database schema changes. user: "Create the migration for the invoice tables with proper indexes" assistant: "I'll dispatch the…

Kanevry/session-orchestrator · 166 tokens

eval-judge

Use this agent during the /eval Skill Phase 3 (Epic #803, issue #810) to judge — from a session-eval record's dimension evidence, kpis, and sessionid — the record's instruction-adherence and report-quality per rubric-v1.md's Judge Dimensions section. Dispatched read-only, coordinator-side (never inside a wave) by…

Kanevry/session-orchestrator · 249 tokens