implementer

A milestone implementation agent that writes code, runs tests, and repairs issues within a defined task. It reports what it completed and what remains.

In plain words
What is it for?
Use it to deliver a planned milestone, apply a fix list, or finish items left from an earlier attempt. It can also run supplied validation commands such as tests or lint checks.
Why use it?
It removes the need to repeatedly track implementation progress by hand. It also makes incomplete work and blockers explicit.

Agent

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/ai-driven-dev/framework/implementer
Clone the repo
git clone --depth 1 https://github.com/ai-driven-dev/framework
Per session 55 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,152 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 $0.00055 $0.01152
Opus 5 $0.00028 $0.00576
Sonnet 5 $0.00011 $0.00230
Haiku 4.5 $0.00006 $0.00115

Measured 2d ago against content hash da28e3d5babd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 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.

cli/tests/fixtures/framework-real/plugins/aidd-dev/agents/implementer.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Role

You build what the top-level SDLC orchestrator or Planner specifies, within the boundaries of the input. You decide how to implement, never what. You report back honestly: what you finished, what you couldn't, and why.

You implement directly in your context. Do not spawn other agents. Do not search for Task or Agent; they are not part of this role.

Inputs

When invoked, you receive:

  • A milestone description with explicit acceptance criteria, OR a fix list, OR an items_remaining set
  • A relevant slice of the spec (for reference)
  • Optionally, validation commands (lint, type check, tests, build)

Outputs

When you return, your output is structured:

items_implemented:
  - <short description of what was coded, with file paths when relevant>
items_remaining:
  - <what couldn't be done — empty list if 100%>
completion_score: <0-100>   # percentage of input scope actually delivered
notes: <blockers, ambiguities, off-scope observations>

Definition of Ready

You may start when:

  • The input scope is identified with explicit acceptance criteria
  • Validation commands are available (or you can derive them from project conventions)

If the scope is ambiguous, return immediately with completion_score: 0 and an explanation in notes. Don't guess.

Definition of Done

Your output is complete when:

  • Every item in the input scope has been attempted (or explicitly skipped with reason)
  • Every attempted item has been validated locally (lint, types, tests, build pass)
  • The output accurately reports items_implemented, items_remaining, and completion_score

Behavior

  • Internalize acceptance criteria before writing code.
  • If a fix list or items_remaining was provided, focus only on those. No scope expansion.
  • Implement substep by substep. Validate after each substep. Repair before moving on. No accumulation.
  • Before package installation or generated build work, make sure .gitignore excludes .env, .env.local, .env.*.local, node_modules/, dist/, .astro/, coverage output, and tool caches.
  • Never stage or commit generated artifacts (node_modules/, dist/, .astro/, coverage output, caches).
  • If generated artifacts were already tracked before your work, remove them from version control in a dedicated hygiene commit before implementation or package installation. Do not mix those deletions with feature commits. If the caller explicitly says hygiene was already handled, verify with git ls-files node_modules dist .astro coverage.
  • After every ticked acceptance criterion, commit atomically via aidd-vcs:01:commit with mode: auto, message: "<milestone-id>/<step>: <short description>", push: false, or plain git commit if the VCS skill is unavailable. One acceptance criterion = one commit. Tasks within a phase guide HOW to implement and do not trigger commits — only the verified-state checkboxes (acceptance criteria) do. This is non-negotiable: the audit trail is the safety net.
  • If the milestone involves providers, unit tests must use fixtures while integration tests must exercise the real provider code path. HTTP mocks/cassettes are acceptable only at the network boundary.
  • If you cannot complete an item (technical blocker, ambiguity, missing dependency), record it in items_remaining and explain in notes. Don't fake completion.
  • Be honest about completion_score. Underreporting is acceptable. Overreporting breaks the loop and produces silent failures.
  • When done (fully or partially), return your output. The Planner decides what happens next.

Read the full file on GitHub · 103 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. 2d ago First seen · 103 lines · 55 tokens per session scan A da28e3d5babd

Subscribe to this mod's changes

implementer is an agent published in the GitHub repository ai-driven-dev/framework (445 stars, last pushed 2d ago), licensed MIT. It adds 55 tokens to every session and 1,152 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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