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name: error-driven-evolution description: Structured error-to-rule learning system for AI agents. Activate when an agent makes a mistake, receives a correction from the user, or needs to check past lessons before making a decision. Converts errors into executable rules (not reflections) stored in lessons.md, and enforces pre-decision rule scanning to prevent repeat mistakes. Supports sharing anonymized lessons to a community repository.

Error-Driven Evolution

Turn mistakes into rules. Not reflections, not apologies — rules.

Core Concept

When an agent makes an error or gets corrected, it must:

1. Extract a rule (not a story)
2. Write it to lessons.md in its workspace
3. Scan relevant rules before future decisions in that domain
4. Optionally share anonymized rules to the community repo

lessons.md Format

File location: {workspace}/lessons.md

Each rule follows this structure:

### [CATEGORY] Short imperative title

- **When**: The specific situation/trigger
- **Do**: The correct action (imperative, specific)
- **Don't**: The wrong action that was taken
- **Why**: One sentence — what went wrong
- **Added**: YYYY-MM-DD

Categories

Tag	Scope
DATA	Querying, interpreting, presenting data
COMMS	Messaging, tone, audience, channels
SCOPE	Role boundaries, doing others' work
EXEC	Task execution, tools, file ops
JUDGMENT	Decisions, priorities, assumptions
CONTEXT	Memory, context window, info management
SAFETY	Security, privacy, destructive ops
COLLAB	Multi-agent coordination, handoffs

When to Record

Record a rule when:

1. User corrects you — explicit feedback
2. User overrides your output — they redo your work
3. Same error twice — second occurrence MUST become a rule
4. Near miss — you catch yourself about to repeat a mistake

Do NOT record: one-off technical glitches, user preference changes (those go in MEMORY.md).

How to Record

1. Stop. Don't apologize at length.
2. Identify the category.
3. Write the rule in imperative form.
4. Append to lessons.md (never overwrite).
5. Confirm briefly: "Added to lessons: [title]"

Pre-Decision Scan

Before acting, scan lessons.md for applicable rules:

About to...	Check
Present data	[DATA]
Send message / write report	[COMMS] + [SCOPE]
Make suggestion	[JUDGMENT] + [SCOPE]
Execute multi-step task	[EXEC] + [CONTEXT]
Start new session	All (skim titles)

Scan = read ### [TAG] headers, check if any When matches your situation.

Community Sharing

Share anonymized lessons to help other agents: https://github.com/anthropic-ai/agent-lessons

See references/community-sharing.md for the anonymization and submission process.

Setup

1. Create lessons.md in your workspace:

# Lessons
Rules extracted from mistakes. Append after failing, scan before deciding.

2. Copy community/top-100.md to your workspace as top-100.md — this is your pre-installed immune system. Small enough to skim on startup, covers the most common and costly mistakes across all agent deployments.

3. Add to your startup instructions:

- On startup: skim top-100.md titles (pre-installed community lessons)
- On correction/failure: append rule to lessons.md
- Before decisions: scan lessons.md + top-100.md for [CATEGORY] rules

Loading Strategy

Your agent has two rule files:

File	Source	Load on startup	Size target
lessons.md	Your own mistakes	Yes, fully	Grows organically
top-100.md	Community top picks	Yes, skim titles	~8KB, curated

For deeper community search (beyond top-100), query community/{category}.md files on-demand when facing an unfamiliar situation.

Maintenance

When lessons.md exceeds 50 rules: review for duplicates, retire obsolete rules (mark don't delete), consider splitting by category.