loop-engineering
Skill0 starsChanged 39 days agoNo licenceView on GitHub
Drive a big goal end-to-end as a multi-agent orchestrator — fan out as many haiku/sonnet/opus subagents as the goal needs, plan first, manage everything. Use when the user types /loop-engineering or /orchestrate-this, says "my goal is…", asks for "a workflow with subagents", or tells you to "be the orchestrator".
33 other projects use a version of this.
Adopted by
Most recently taken up by
- vpeetla-ai/venkat-ai-platformtheir own version, first seen Nov 8, 2026
- Mark393295827/graph-engineering-architecturestheir own version, first seen Nov 8, 2026
- victor-hac-work/loop-engineering-skillstheir own version, first seen Nov 8, 2026
- jggomez/expert-ai-developer-skillstheir own version, first seen Nov 6, 2026
- Hakkadaikon/hymmetheir own version, first seen Oct 26, 2026
- vpeetla-ai/swarm-agent-patterntheir own version, first seen Oct 20, 2026
Used with
Projects with it also use aegis-gateway, deploy-vercel-render and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
vpeetla-aiSkillaegis-gateway
Integrate AegisAI gateway before tool side effects (notify, publish, deploy). Use when adding Slack/Telegram/WhatsApp notify, content publish, or any irreversible external action in VAP, AegisLoop, or ai-content-factory.
Also in 6 projects that use this
vpeetla-aiSkilldeploy-vercel-render
Deploy vpeetla-ai demos: Vercel static/Next.js frontends, Render FastAPI backends, env vars, free tier gotchas. Use when shipping demos, fixing deploy failures, or adding render.yaml / vercel.json.
Also in 6 projects that use this
HermeticOrmusSkilllanggraph-orchestration
LangGraph stateful multi-agent graphs with categorical coordination patterns and cyclic workflows. Use when building stateful AI agent systems, implementing multi-agent orchestration with conditional routing, creating cyclic workflows with persistence, or designing graph-based AI pipelines with checkpointing and human-in-the-loop patterns.
Also in 6 projects that use this
junimnjwSkillagentic-engineering
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
Also in 7 projects that use this
- +8 more from vpeetla-ai
Other versions33 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 33
- bojieli/ai-agent-book-projects★ 53ksame name
- bojieli/ai-agent-book★ 53ksame name
- huytieu/COG-second-brain★ 1.3ksame name
- akitaonrails/my-skills★ 283same name
- jggomez/expert-ai-developer-skills★ 31same name
- aiconnai/agentshield★ 21same name
- sxivansx/loop-engineering★ 10same name
- victor-hac-work/loop-engineering-skills★ 5same name
- Mark393295827/graph-engineering-architectures★ 4same name
- aiconnai/engram★ 3same name
23 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository9 other files it gives its agents.
- demoSkill
- loopsSkill
- polymarket-monitorSkill
- prepSkill
- saeSkill
- security-auditSkill
- superchargeSkill
- tool-evalSkill
- vcSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 3 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 27 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
“Common” is measured: kept by at least 6 in 10 of the 27 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

