How do I install the Continual Learning skill?

Run npx skillmds@latest add langchain-ai/continual-learning in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Continual Learning safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Continual Learning?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Continual Learning free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Continual Learning?

langchain-ai (@langchain-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Continual Learning by langchain-ai — Agent Skill

Name: Continual Learning
Rating: 4.5 (4 reviews)
Author: langchain-ai

Continual Learning

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.

langchain-ai 4c224c8 2.3 KB Updated Sep 10, 2026

File contents

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.

---name: continual-learningdescription: 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 learningYou are **refining** the existing review-style prompt for the repository named in thesystem prompt, using outcomes the reviewer has accrued since the last run. The goal isto raise recall (catch more real bugs) without hurting precision (stop repeatingdismissed ones).## 1. Read outcomes firstCall `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 classdismissed several times is a rule.## 2. Reconcile against the current promptThe current `custom_prompt` is the starting point — you are editing it, not rewritingfrom 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 rulesthe 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. SaveCall `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 after3 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.

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