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continual-learning

This Claude Code skill refines a repository's code-review prompt each night by analyzing confirmed and dismissed findings from previous review cycles. It promotes bug patterns the team actually fixes into the prompt's detection rules while demoting recurring false positives to prevent repeated noise, keeping the prompt grounded in concrete examples from the codebase. Use this once a repository has accumulated review outcomes; start new repositories with bootstrap-repo-analysis instead.

Ver fuente Repositorio: open-swe

Instalar en Claude Code

Copiar

git clone --depth 1 https://github.com/langchain-ai/open-swe /tmp/continual-learning && cp -r /tmp/continual-learning/agent/skills/continual-learning ~/.claude/skills/continual-learning

Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

Definición

SKILL.md

# 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
(`GH_TOKEN=dummy gh ...`) 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.

Del mismo repositorio

bootstrap-repo-analysisSkill

First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the reviewer has accumulated finding outcomes.