Agent Skills › langchain-ai/open-swe › continual-learning

continual-learning

GitHub

基于历史代码审查结果（确认/驳回）自动优化仓库特定的 Review Prompt，提升缺陷检出率并减少误报。

agent/skills/continual-learning/SKILL.md langchain-ai/open-swe

Trigger Scenarios

需要迭代优化代码审查规则根据过往审查反馈调整提示词

Install

npx skills add langchain-ai/open-swe --skill continual-learning -g -y

More Options

Non-standard path

npx skills add https://github.com/langchain-ai/open-swe/tree/main/agent/skills/continual-learning -g -y

Use without installing

npx skills use langchain-ai/open-swe@continual-learning

指定 Agent (Claude Code)

npx skills add langchain-ai/open-swe --skill continual-learning -a claude-code -g -y

安装 repo 全部 skill

npx skills add langchain-ai/open-swe --all -g -y

预览 repo 内 skill

npx skills add langchain-ai/open-swe --list

SKILL.md

Frontmatter

{
    "name": "continual-learning",
    "description": "Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit \/ thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt, and save the refined version. Use this once outcomes exist; use bootstrap-repo-analysis for a cold-start repo."
}

Continual learning

You are refining the existing review-style prompt for the repository named in the system prompt, using outcomes the reviewer has accrued since the last run. The goal is to raise recall (catch more real bugs) without hurting precision (stop repeating dismissed ones).

1. Read outcomes first

Call read_finding_outcomes once. It returns this repo's past findings split into:

confirmed — resolved by a follow-up commit or 👍'd. These are real bug patterns this team fixes. Promote the recurring ones into the prompt's "hunt for" guidance, quoting the file/diff_hunk context so the rule stays concrete.
dismissed — dismissed or 👎'd. These are false-positive patterns. Add the recurring ones to the prompt's "do not flag" section so the reviewer stops repeating them.

Look for repetition, not one-offs. A single dismissed finding is noise; the same class dismissed several times is a rule.

2. Reconcile against the current prompt

The current custom_prompt is the starting point — you are editing it, not rewriting from scratch. Read it (it is summarized for you / available via the dashboard record). Keep what still holds, strengthen rules the outcomes confirm, and remove or soften rules the outcomes contradict. Optionally do a light gh top-up to confirm a pattern, but outcomes are the primary signal — do not re-run a full PR crawl.

Stay aligned with the reviewer-agent themes in the system prompt.

3. Save

Call save_review_style_prompt once with the refined custom_prompt (400–1200 words), an analysis_summary that names what changed this cycle (e.g. "promoted N-pattern after 3 confirmed fixes; dropped M-pattern after repeated dismissals"), and the top_reviewers / counts you have. If outcomes were empty and nothing changed, say so in analysis_summary and re-save the existing prompt unchanged rather than degrading it.