How should 16 Self Improving Agent Proactive Self Reflection be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

16 Self Improving Agent Proactive Self Reflection

Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use befo... Skill: 16 Self Improving Agent Proactive Self Reflection Owner: smallkeyboy Summary: Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use befo... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-16T07:09:51.433Z | auto Self-Improving Agent (Proactive Self-Reflection) 1.2.10 introduces sharper proactive reflection a

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.7k

Updated

Oct 10, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.7K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.7K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.0release · observed Apr 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170nr6nxfsrk9n96c9r21ydcx83j5pf:16-self-improving-agent-proactive-self-reflection

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-smallkeyboy-16-self-improving-agent-proactive-self-refl/snapshot"

Documentation

CLAWHUB

31,252 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: Self-Improving Agent (Proactive Self-Reflection)
slug: self-improving
version: 1.2.10
homepage: https://clawic.com/skills/self-improving
description: Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use before starting work and after responding to the user.
changelog: "Sharper setup now lists relevant memory before non-trivial work, with a title that highlights proactive self-reflection."
metadata: {"clawdbot":{"emoji":"🧠","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/self-improving/"]}}
---

## When to Use

User corrects you or points out mistakes. You complete significant work and want to evaluate the outcome. You notice something in your own output that could be better. Knowledge should compound over time without manual maintenance.

## Architecture

Memory lives in `~/self-improving/` with tiered structure. If `~/self-improving/` does not exist, run `setup.md`.

```
~/self-improving/
├── memory.md          # HOT: ≤100 lines, always loaded
├── index.md           # Topic index with line counts
├── projects/          # Per-project learnings
├── domains/           # Domain-specific (code, writing, comms)
├── archive/           # COLD: decayed patterns
└── corrections.md     # Last 50 corrections log
```

## Quick Reference

| Topic | File |
|-------|------|
| Setup guide | `setup.md` |
| Memory template | `memory-template.md` |
| Learning mechanics | `learning.md` |
| Security boundaries | `boundaries.md` |
| Scaling rules | `scaling.md` |
| Memory operations | `operations.md` |
| Self-reflection log | `reflections.md` |

## Detection Triggers

Log automatically when you notice these patterns:

**Corrections** → add to `corrections.md`, evaluate for `memory.md`:
- "No, that's not right..."
- "Actually, it should be..."
- "You're wrong about..."
- "I prefer X, not Y"
- "Remember that I always..."
- "I told you before..."
- "Stop doing X"
- "Why do you keep..."

**Preference signals** → add to `memory.md` if explicit:
- "I like when you..."
- "Always do X for me"
- "Never do Y"
- "My style is..."
- "For [project], use..."

**Pattern candidates** → track, promote after 3x:
- Same instruction repeated 3+ times
- Workflow that works well repeatedly
- User praises specific approach

**Ignore** (don't log):
- One-time instructions ("do X now")
- Context-specific ("in this file...")
- Hypotheticals ("what if...")

## Self-Reflection

After completing significant work, pause and evaluate:

1. **Did it meet expectations?** — Compare outcome vs intent
2. **What could be better?** — Identify improvements for next time
3. **Is this a pattern?** — If yes, log to `corrections.md`

**When to self-reflect:**
- After completing a multi-step task
- After receiving feedback (positive or negative)
- After fixing a bug or mistake
- When you notice your output could be better

**Log format:**
```
CONTEXT: [type of task]
REFLECTION: [

_meta.json

{
  "ownerId": "kn7ez4h284sw1q2f7zvmge91wx83jdbt",
  "slug": "16-self-improving-agent-proactive-self-reflection",
  "version": "1.0.0",
  "publishedAt": 1776323391433
}

boundaries.md

# Security Boundaries

## Never Store

| Category | Examples | Why |
|----------|----------|-----|
| Credentials | Passwords, API keys, tokens, SSH keys | Security breach risk |
| Financial | Card numbers, bank accounts, crypto seeds | Fraud risk |
| Medical | Diagnoses, medications, conditions | Privacy, HIPAA |
| Biometric | Voice patterns, behavioral fingerprints | Identity theft |
| Third parties | Info about other people | No consent obtained |
| Location patterns | Home/work addresses, routines | Physical safety |
| Access patterns | What systems user has access to | Privilege escalation |

## Store with Caution

| Category | Rules |
|----------|-------|
| Work context | Decay after project ends, never share cross-project |
| Emotional states | Only if user explicitly shares, never infer |
| Relationships | Roles only ("manager", "client"), no personal details |
| Schedules | General patterns OK ("busy mornings"), not specific times |

## Transparency Requirements

1. **Audit on demand** — User asks "what do you know about me?" → full export
2. **Source tracking** — Every item tagged with when/how learned
3. **Explain actions** — "I did X because you said Y on [date]"
4. **No hidden state** — If it affects behavior, it must be visible
5. **Deletion verification** — Confirm item removed, show updated state

## Red Flags to Catch

If you find yourself doing any of these, STOP:

- Storing something "just in case it's useful later"
- Inferring sensitive info from non-sensitive data
- Keeping data after user asked to forget
- Applying personal context to work (or vice versa)
- Learning what makes user comply faster
- Building psychological profile
- Retaining third-party information

## Kill Switch

User says "forget everything":
1. Export current memory to file (so they can review)
2. Wipe all learned data
3. Confirm: "Memory cleared. Starting fresh."
4. Do not retain "ghost patterns" in behavior

## Consent Model

| Data Type | Consent Level |
|-----------|---------------|
| Explicit corrections | Implied by correction itself |
| Inferred preferences | Ask after 3 observations |
| Context/project data | Ask when first detected |
| Cross-session patterns | Explicit opt-in required |

corrections.md

# Corrections Log — Template

> This file is created in `~/self-improving/corrections.md` when you first use the skill.
> Keeps the last 50 corrections. Older entries are evaluated for promotion or archived.

## Example Entries

```markdown
## 2026-02-19

### 14:32 — Code style
- **Correction:** "Use 2-space indentation, not 4"
- **Context:** Editing TypeScript file
- **Count:** 1 (first occurrence)

### 16:15 — Communication
- **Correction:** "Don't start responses with 'Great question!'"
- **Context:** Chat response
- **Count:** 3 → **PROMOTED to memory.md**

## 2026-02-18

### 09:00 — Project: website
- **Correction:** "For this project, always use Tailwind"
- **Context:** CSS discussion
- **Action:** Added to projects/website.md
```

## Log Format

Each entry includes:
- **Timestamp** — When the correction happened
- **Correction** — What the user said
- **Context** — What triggered it
- **Count** — How many times (for promotion tracking)
- **Action** — Where it was stored (if promoted)

learning.md

# Learning Mechanics

## What Triggers Learning

| Trigger | Confidence | Action |
|---------|------------|--------|
| "No, do X instead" | High | Log correction immediately |
| "I told you before..." | High | Flag as repeated, bump priority |
| "Always/Never do X" | Confirmed | Promote to preference |
| User edits your output | Medium | Log as tentative pattern |
| Same correction 3x | Confirmed | Ask to make permanent |
| "For this project..." | Scoped | Write to project namespace |

## What Does NOT Trigger Learning

- Silence (not confirmation)
- Single instance of anything
- Hypothetical discussions
- Third-party preferences ("John likes...")
- Group chat patterns (unless user confirms)
- Implied preferences (never infer)

## Correction Classification

### By Type
| Type | Example | Namespace |
|------|---------|-----------|
| Format | "Use bullets not prose" | global |
| Technical | "SQLite not Postgres" | domain/code |
| Communication | "Shorter messages" | global |
| Project-specific | "This repo uses Tailwind" | projects/{name} |
| Person-specific | "Marcus wants BLUF" | domains/comms |

### By Scope
```
Global: applies everywhere
  └── Domain: applies to category (code, writing, comms)
       └── Project: applies to specific context
            └── Temporary: applies to this session only
```

## Confirmation Flow

After 3 similar corrections:
```
Agent: "I've noticed you prefer X over Y (corrected 3 times).
        Should I always do this?
        - Yes, always
        - Only in [context]
        - No, case by case"

User: "Yes, always"

Agent: → Moves to Confirmed Preferences
       → Removes from correction counter
       → Cites source on future use
```

## Pattern Evolution

### Stages
1. **Tentative** — Single correction, watch for repetition
2. **Emerging** — 2 corrections, likely pattern
3. **Pending** — 3 corrections, ask for confirmation
4. **Confirmed** — User approved, permanent unless reversed
5. **Archived** — Unused 90+ days, preserved but inactive

### Reversal
User can always reverse:
```
User: "Actually, I changed my mind about X"

Agent: 
1. Archive old pattern (keep history)
2. Log reversal with timestamp
3. Add new preference as tentative
4. "Got it. I'll do Y now. (Previous: X, archived)"
```

## Anti-Patterns

### Never Learn
- What makes user comply faster (manipulation)
- Emotional triggers or vulnerabilities
- Patterns from other users (even if shared device)
- Anything that feels "creepy" to surface

### Avoid
- Over-generalizing from single instance
- Learning style over substance
- Assuming preference stability
- Ignoring context shifts

## Quality Signals

### Good Learning
- User explicitly states preference
- Pattern consistent across contexts
- Correction improves outcomes
- User confirms when asked

### Bad Learning
- Inferred from silence
- Contradicts recent behavior
- Only works in narrow context
- User never confirmed

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