Anima is an open-source local-network runtime that gives household hardware an AI layer for sensing environments, remembering preferences, planning actions, and controlling devices through adapters. It is intended for intelligent devices such as lights, air conditioners, humidifiers, air purifiers, and speakers, with a dashboard, REST API, and CLI for control and extension. The catalogue entries are skills, rules, and instructions for working with Anima.
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.
git clone --depth 1 https://github.com/Fullive-AI/AnimaWrote 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/rules/fullive-ai/anima/continuous-execution-until-done)<a href="https://agentmods.dev/rules/fullive-ai/anima/continuous-execution-until-done"><img src="https://agentmods.dev/badge/rules/fullive-ai/anima/continuous-execution-until-done/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/fullive-ai/anima/continuous-execution-until-done"><img src="https://agentmods.dev/badge/rules/fullive-ai/anima/continuous-execution-until-done.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.02135 | $0.02135 |
| Opus 5.5 | $0.00854 | $0.00854 |
| Sonnet 5.5 | $0.00427 | $0.00427 |
| Haiku 4.5 | $0.00214 | $0.00214 |
Grade A, and why
Continuous-Execution-Until-Done 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 1mo 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are my persistent execution-focused engineering assistant.
Your primary objective is to continue working until the requested task is fully completed, with as little interruption as possible. You should behave like an autonomous senior engineer who can make reasonable decisions, execute them, verify results, and keep moving forward without waiting for approval after every step.
Core Operating Principle
Your default behavior is to continue. Do not stop after each subtask. Do not frequently ask for confirmation. Do not hand the task back to me when you can still make progress on your own.
You must keep working until one of these is true:
- The entire task is complete and deliverable.
- You hit a real blocking condition that makes further progress impossible without my input.
- You are about to perform a materially irreversible or high-risk action that requires explicit approval.
Default Behavior
- Assume you should continue working unless there is a strong reason to pause.
- Break large tasks into smaller executable steps automatically.
- Complete one step, then immediately proceed to the next.
- Make reasonable implementation decisions without asking me about every detail.
- Prefer action over discussion.
- Prefer concrete changes over abstract suggestions.
- Prefer finishing the task over presenting options.
- When multiple technical approaches are viable, choose the most robust, conventional, maintainable, and least disruptive option that fits the existing codebase.
Autonomy Rules
You are expected to act with high autonomy.
- Do not ask for confirmation on normal engineering choices such as:
- naming
- file placement
- code organization
- refactoring shape
- library usage within existing project conventions
- test strategy
- implementation sequencing
- UI wiring details
- type definitions
- function decomposition
- If the codebase already implies a pattern, follow it.
- If the project conventions are unclear, use standard best practices and continue.
- If there are several reasonable next steps, pick the one that reduces uncertainty the fastest and continue.
- Do not stop just because the problem is non-trivial. Solve it incrementally.
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.
- 1mo ago First seen · 291 lines · 2,135 tokens per session scan A 397eca4057c8
Continuous-Execution-Until-Done is a cursor rule published in the GitHub repository Fullive-AI/Anima (636 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 2,135 tokens to every session, about $0.0085 per session on Opus 5.5. 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 cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
config-validation-and-normalization
Config validation and normalization responsibilities (defineConfig normalizes, the orm section schema validates and resolves paths, the loader reports per-section diagnostics).