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 rules/synthet1cc/unloop-mcp/cursorgit clone --depth 1 https://github.com/synthet1cc/unloop-mcpWhat 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 | $0.08249 | $0.08249 |
| Opus 5 | $0.04124 | $0.04124 |
| Sonnet 5 | $0.01650 | $0.01650 |
| Haiku 4.5 | $0.00825 | $0.00825 |
Grade C, and why
cursor scanned grade C with 1 finding 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- Run: rm -rf node_modules && npm install How it starts
The opening of the file, as written. The whole thing — 806 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unloop — AI Fix Loop Detection & Recovery Protocol
What is Unloop and why it exists
Unloop is an MCP server running alongside your coding session. It solves a specific, well-documented problem: you (the AI) get stuck in fix loops. You encounter an error, try to fix it, the fix doesn't work, and you try a slightly different variation of the same approach — 5, 10, 20 times — without ever stepping back to reconsider. This wastes the user's time, burns their tokens, and often makes the codebase worse with accumulated bad changes.
You can't detect this yourself because:
- You have no structured memory of what you've already tried
- You're biased toward incremental variations of your last attempt
- Earlier attempts scroll out of your context window after enough messages
- You have no meta-reasoning system that says "you're going in circles"
Unloop provides that meta-reasoning. It tracks every fix attempt, fingerprints the errors, measures the similarity between your approaches, and tells you — with increasing urgency — when you need to stop and change direction.
This protocol is not optional. It is mandatory whenever you are fixing errors.
How the system works (so you understand WHY each rule matters)
Error fingerprinting
Every error message you report is normalized: file paths, line numbers, UUIDs, timestamps, hex hashes, ANSI codes, and stack frames are stripped out. The result is hashed into a stable fingerprint. This means "Cannot find module './Button' in /Users/alice/src/App.tsx:42" and "Cannot find module './Button' in /Users/bob/src/Main.tsx:7" produce the same fingerprint — they're structurally identical errors.
Fix similarity detection
Your fix_description is tokenized (lowercased, stop-words removed) and compared against every previous fix description for the same error fingerprint using Jaccard similarity. If two descriptions share more than 55% of their meaningful words, they're considered "the same approach." This is how Unloop detects that you're making minor variations instead of genuinely different attempts.
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.
- 2d ago First seen · 806 lines · 8,249 tokens per session scan C 001bda661216
cursor is a cursor rule published in the GitHub repository synthet1cc/unloop-mcp (4 stars, last pushed 5mo ago), licensed MIT. It adds 8,249 tokens to every session, about $0.0412 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other cursor rules, from other repositories
replen
Replen integration — session-start matches, portfolio recall, silent auto-reground.
cursorrules
You are building an AI/ML project with Python. The project uses PyTorch for model training, handles data pipelines with proper validation, tracks experiments systematically, and follows production ML engineering practices. Code is type-hinted, tested, and reproducible.
cursorrules
You have the AI Mind Map MCP server connected. It is a persistent code memory system that eliminates redundant file re-reading and context loss between sessions.
rust-clap
Rust CLI Best Practices with clap.
rust-observability
Rust Observability and Logging Best Practices.
devenv-containers
Devenv Container and Docker Integration.