Agent skill · Testing & QA

swe-loop

Use when the user has a coding prompt — a feature, bug fix, or refactor — and wants it implemented end to end by a self-checking software loop, not a single pass. It refines the prompt into an executable plan (running the plan-loop internally), then executes the plan task by task: an Engineer subagent implements each PR-sized task editing only source, a separate QA subagent authors the tests that prove the task's acceptance criteria (or grades quality only when tests already cover them), runs the task's tests plus the full accumulated regression suite, and grades the code against a strict conc

gaashergithub.com/gaasherGitHub ↗
claude-codeships scriptsMIT
Install
npx skills add gaasher/Agent-Loop-Skills --skill swe-loop --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 8
SKILL.md size: 13 KB
Bundled scripts: yes
Version: 0.1.0
Requires: Requires Python 3.9+ and a runnable test command in the target repo.
Path: loops/swe-loop/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 146
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# SWE Loop The **execute** stage of the `prompt → plan → execute → debug` pipeline: it turns a coding prompt into working, tested, well-organised code. It runs the **plan-loop** to get an executable `tasks.json`, then walks the tasks in dependency order. Each task is driven by two isolated subagents — an **Engineer** that writes the code and a **QA** that owns the tests and the quality bar — looping until the gate holds. The feedback signal is two-part, like the repo's other evaluator loops: an **objective gate** (the task's tests pass, the full regression suite stays green, and `tools/quality_check.py` reports no threshold violation) and a **qualitative gate** (QA's score against `rubrics/quality-rubric.md` — simplicity, readability, comment hygiene, organisation, style-match). One task is kept per outer step; one change is proposed per inner round, so every delta is attributable. ## When to use Use to implement a prompt in a real repository when you want the work decomposed, built, and tested rather than written in one shot — and when "done" means a downstream engineer would accept it: tests green and the code clean. Default to executing the plan-loop's `tasks.json` task by task;

What's inside
Steps it walks through
  1. When to use
  2. Setup
  3. The loop
  4. Phase 0 — Plan (once)
  5. Per task (outer loop — tasks in tasks.json.order)
  6. Pull requests (per task)
  7. Ledger
  8. Constraints
  9. Roles
  10. Stops
Ships with 7 files
  • examples/run.example.yaml
  • roles/engineer.md
  • roles/qa.md
  • rubrics/quality-rubric.md
  • schemas/change.schema.json
  • schemas/verdict.schema.json
  • tools/quality_check.py
More from Agent-Loop-Skills
All skills →
About this skill
What does the swe-loop skill do?

Use when the user has a coding prompt — a feature, bug fix, or refactor — and wants it implemented end to end by a self-checking software loop, not a single pass. It refines the prompt into an executable plan (running the plan-loop internally), then executes the plan task by task: an Engineer subagent implements each PR-sized task editing only source, a separate QA subagent authors the tests that prove the task's acceptance criteria (or grades quality only when tests already cover them), runs the task's tests plus the full accumulated regression suite, and grades the code against a strict conc

How do I install it?

Run `npx skills add gaasher/Agent-Loop-Skills --skill swe-loop --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From gaasher/Agent-Loop-Skills, a repository with 146 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going