continuous-agent-loop
by affaan-mcontinuous-agent-loop helps agents run repeatable autonomous loops with quality gates, evals, recovery steps, and clear stop rules for reliable task completion.
This skill scores 64/100, which means it is acceptable to list for directory users, but only as a lightweight pattern note rather than a fully operational skill. The repository gives a recognizable trigger and a few useful control concepts for autonomous loops, yet it lacks enough concrete execution detail to remove much guesswork versus a strong generic prompt.
- Clear top-level purpose: it explicitly targets continuous autonomous agent loops with quality gates, evals, and recovery controls.
- Provides a simple selection flow that helps an agent choose among related loop patterns such as continuous-pr, rfc-dag, infinite, and sequential.
- Includes practical failure and recovery cues like freezing the loop, auditing, reducing scope, and replaying with explicit acceptance criteria.
- Operational detail is thin: there are no step-by-step procedures, decision rules, install instructions, or support files to show how to run the loop reliably.
- It depends heavily on named companion skills and commands like ralphinho-rfc-pipeline, eval-harness, and /harness-audit without explaining them here.
Overview of continuous-agent-loop skill
What continuous-agent-loop does
The continuous-agent-loop skill is for building a repeatable agent workflow that keeps generating, checking, and recovering until a task is actually done. It is most useful when you need autonomous iteration with quality gates, evals, and a way to stop loop churn before it wastes time or cost.
Who should install it
Use continuous-agent-loop if you are orchestrating multi-step coding work, CI-adjacent agent runs, or long-running task completion where one prompt is not enough. It is a strong fit for people who need continuous-agent-loop for Agent Orchestration, especially when the work needs decomposition, validation, and recovery instead of a single-shot answer.
What makes it different
This skill is not a generic “keep trying” prompt. It gives a selection flow for choosing the right loop style, then points you toward a production stack that includes decomposition, quality gates, eval loops, and session persistence. That matters because the main risk is not lack of output; it is uncontrolled repetition without measurable progress.
How to Use continuous-agent-loop skill
Install and confirm the right entry point
For continuous-agent-loop install, add the skill from the repository path and then read the skill file first. The install command shown in the source is:
npx skills add affaan-m/everything-claude-code --skill continuous-agent-loop
After that, start with skills/continuous-agent-loop/SKILL.md. In this repo, that is the only source file, so there is no larger support tree to chase.
Turn a rough goal into usable input
The skill works best when your request includes a clear target, a success check, and a failure boundary. A weak prompt says, “make this better.” A stronger prompt says, “iterate on this endpoint until tests pass, keep changes small, stop if the same assertion fails twice, and report the blocker.” That is the kind of input the continuous-agent-loop usage pattern can actually execute.
Read the workflow before you run it
The most useful source content is the loop selection flow, the combined pattern, the failure modes, and the recovery steps. Read those in that order if you are deciding whether the skill fits. The selection flow tells you when not to use it; the combined pattern tells you what a production setup looks like; the failure and recovery sections tell you how the loop should stop or reset.
Practical prompt shape
For best results, specify:
- the task boundary: feature, bug, refactor, research, or CI recovery
- the quality gate: tests, lint, review criteria, or eval output
- the stop rule: when to freeze the loop or escalate
- the recovery action: narrow scope, replay, or audit
Example input style:
“Use continuous-agent-loop to fix the failing auth tests. Keep changes minimal, run a quality gate after each iteration, stop after two identical failures, and narrow to the failing unit before retrying.”
continuous-agent-loop skill FAQ
Is continuous-agent-loop for autonomous coding only?
No. It is broader than coding, but its value is highest when a task can be checked against explicit gates. If you cannot define success or failure, the loop will be weaker and may drift.
When should I not use it?
Do not use continuous-agent-loop when the work is already well-bounded by a strict CI/PR workflow, when you need RFC-style decomposition first, or when the task is mostly exploratory parallel generation. The selection flow points to other loop styles for those cases.
Is this beginner-friendly?
Yes, if you already know the goal and can name a pass/fail condition. It is not ideal for vague brainstorming because the skill assumes you can provide operational constraints, acceptance criteria, and a reason to stop iterating.
How does it compare with a normal prompt?
A normal prompt usually asks for one answer. continuous-agent-loop is better when the task needs repeated generation, verification, and recovery. The gain is less guesswork and fewer dead-end retries, but only if you provide concrete gates and a clear stop condition.
How to Improve continuous-agent-loop skill
Give stronger acceptance criteria
The biggest quality lift comes from telling the loop what “done” means. Include test names, expected outputs, performance thresholds, or review rules. If you only ask for improvement without a measurable finish line, the loop can keep producing changes that look active but do not converge.
Expose the likely failure mode early
The skill explicitly calls out loop churn, repeated retries with the same root cause, merge queue stalls, and cost drift. If you already suspect one of these, say so in the prompt. For example: “Assume this may be a stale caching issue; if the same test fails twice, freeze the loop and audit the harness.” That improves the continuous-agent-loop guide behavior because it changes the recovery path.
Iterate using the smallest failing unit
If the first pass fails, do not widen the scope. Reduce the task to the smallest failing unit, then replay with a tighter prompt and explicit acceptance criteria. That is the most practical way to improve continuous-agent-loop usage without turning the loop into endless escalation.
Pair it with the right surrounding stack
The repository’s recommended stack matters: decomposition, quality gates, evals, and persistence each cover a different failure point. If your environment cannot support those pieces, expect weaker results from the skill. If it can, continuous-agent-loop becomes much more reliable for Agent Orchestration than a standalone prompt.