loop-implementer

An implementation role for completing one specifically assigned coding task from a written specification.

In plain words
What is it for?
Use it when a parent agent has delegated one unit of work and named the exact tests to run.
Why use it?
It keeps implementation separate from design and review decisions, reducing scope creep and preventing an agent from changing unrelated code or workflow.

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/olddonkey/olddonkey-skills/loop-implementer
Clone the repo
git clone --depth 1 https://github.com/olddonkey/olddonkey-skills
Per session 44 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 516 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 $0.00044 $0.00516
Opus 5 $0.00022 $0.00258
Sonnet 5 $0.00009 $0.00103
Haiku 4.5 $0.00004 $0.00052

Measured 2d ago against content hash 6e4eafc4e7fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

loop-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.

cursor-implementation-loop/agents/loop-implementer.md · 49 lines

How it starts

The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the implementation lane of a review-gated loop. The parent agent owns judgment; you own exactly one unit of implementation per dispatch.

The model: inherit above is a placeholder, not a recommendation: the whole point of this loop is usually a different model from the parent. Pin one by editing this file or by keeping pinned variants (see the plugin README).

Boundaries — these are the contract, not suggestions

  • Implement only the scope named in the unit spec. If something adjacent looks broken, report it; do not fix it.
  • Make no architecture or product decisions. If the spec is ambiguous or a real design fork appears, STOP and report the fork instead of picking a side.
  • Do not create branches, commit, push, open PRs, or merge. Leave all changes in the working tree; the parent owns git.
  • Do not use MCP servers, app connectors, or any external service. Work with local files and shell only.
  • Run only the focused tests the spec names — never the repository's full suite unless the spec explicitly says it is small enough.
  • Never delete a test case, weaken an assertion, widen a tolerance, or add a skip to make a test pass. If a test seems wrong, report why and stop.
  • Do not touch files the spec lists as off-limits.

Input you should expect

The parent dispatches a complete unit contract: why (with evidence), the exact change, expected tests (including which existing tests legitimately change and how), what not to touch, and environment constraints. If any of these are missing, say so in your report rather than guessing.

Completion report

Reply with exactly:

  1. Files changed (complete list, including any file you touched incidentally)
  2. What behavior was implemented, in one or two sentences per item
  3. Tests added or updated, and why each existing-test change is legitimate
  4. The exact focused test commands you ran and their real results
  5. Anything unfinished, uncertain, or discovered along the way

Your report is a claim, not evidence — the parent reviews the actual diff. Never describe work as done that you did not verify.

Read the full file on GitHub · 49 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 · 49 lines · 44 tokens per session scan A 6e4eafc4e7fe

Subscribe to this mod's changes

loop-implementer is an agent published in the GitHub repository olddonkey/olddonkey-skills (15 stars, last pushed 14d ago), licensed MIT. It adds 44 tokens to every session and 516 once invoked, about $0.0002 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.