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.
npx agentmods add skills/olddonkey/olddonkey-skills/cursor-implementation-loopnpx skills add olddonkey/olddonkey-skills --skill cursor-implementation-loopgit clone --depth 1 https://github.com/olddonkey/olddonkey-skillsWrote 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.
[](https://agentmods.dev/skills/olddonkey/olddonkey-skills/cursor-implementation-loop)<a href="https://agentmods.dev/skills/olddonkey/olddonkey-skills/cursor-implementation-loop"><img src="https://agentmods.dev/badge/skills/olddonkey/olddonkey-skills/cursor-implementation-loop.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00205 | $0.04905 |
| Opus 5 | $0.00102 | $0.02452 |
| Sonnet 5 | $0.00041 | $0.00981 |
| Haiku 4.5 | $0.00020 | $0.00490 |
Grade A, and why
cursor-implementation-loop 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 385 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cursor implementation loop
The implementer subagent writes the code; you own the judgment. A delegated implementer is fast but it self-reports success, and pointing it at a long test suite wastes the machine you share with it. You give it a precise spec, then be the thing that actually verifies and ships. This includes bug fixes: a bug found at review, at the gate, or later is a unit like any other — you diagnose and spec, the implementer implements. Editing code directly "because it's faster" silently inverts the division of labor and costs review its independence.
The loop: decompose → dispatch → review → iterate → gate → publish → next.
Cursor-specific mechanics (subagent dispatch, resume, model pinning and its limits, enforcement gaps vs. hard sandboxes) live in references/cursor-runtime.md; read it before the first dispatch of a session.
Non-negotiables
- Review is mandatory and independent. The implementer's summary is a claim; the diff is the evidence. Never skip review because it says it's done.
- An assumed default never leaves the machine. With no explicit user choice, stop at the working tree — even a local commit can fire hooks/signing. Commit, push, PR, and merge each need the user to have said yes once for this repo, asked at kickoff, not discovered at publish time. Once is once: that authorization is per-repo, persists across sessions until revoked or the work changes character — never re-ask per unit.
- Never push straight to the default branch.
- The full-suite gate is yours, run by you, with the real exit code. A fix means the code satisfies the test — a weakened assertion, deleted case, or widened tolerance to turn red green is a stop, not a pass.
- Don't let the implementer run the full test suite by default — focused subsets only, unless calibration showed the suite is small and fast.
- Only one writable subagent operates in a worktree at a time. Parallel implementers in one checkout make diffs unattributable.
What ships with it
7 files 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.
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.
- 3d ago First seen · 385 lines · 205 tokens per session scan A c872dd5fe94c
cursor-implementation-loop is a skill published in the GitHub repository olddonkey/olddonkey-skills (15 stars, last pushed 15d ago), licensed MIT. It adds 205 tokens to every session and 4,905 once invoked, about $0.0010 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.
Other skills, from other repositories
iflytek-image-understanding
Use when user asks to analyze an image, describe image contents, or answer questions about a picture. iFlytek Image Understanding (图片理解) — analyze and answer questions about images using Spark Vision model. WebSocket API, pure Python stdlib, no pip dependencies.
genai-tk
Build GenAI and agentic applications with the genai-tk toolkit (https://github.com/tclatos/genai-tk) — a YAML-driven wrapper over LangChain, LangGraph, and 100+ LLM providers. Use this skill whenever the user mentions genai-tk, genaitk, the GenAI Toolkit, cli init, LangchainAgent, getllm/getembeddings…
adaption-ai
Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe…
langchain
Build AI agents with LangChain framework. Use when building agents, tools, memory, MCP integrations, RAG pipelines, multi-agent systems, or any LLM-powered applications using LangChain or LangGraph in Python or TypeScript.
okhp3-thread-extract-chatgpt
Extract manually supplied ChatGPT conversations into standalone, actionable Markdown. Use when the user pastes a ChatGPT chat, Project thread, Canvas, Deep Research report, search answer, prompt-response sequence, generated file or image reference, or export excerpt and wants its goals, reasoning, decisions, reusable…
okhp3-decision-model-authoring
Author and validate decision models from PNS decision points using DMN-aligned rule tables. Use this skill when the PNS contains three or more gateway decision points that warrant a structured decision table; when the user asks to document business rules as decision logic, build a DMN table, or make decision criteria…