Wanwu is an enterprise platform for building AI agents, workflows, retrieval-augmented applications, and managing models in multi-tenant environments. It is designed for developers and enterprise teams delivering AI applications and integrations. The catalogue entries provide skills and agents for using the platform.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/unicomai/wanwu/self-improvingnpx skills add UnicomAI/wanwu --skill self-improvinggit clone --depth 1 https://github.com/UnicomAI/wanwuWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/unicomai/wanwu/self-improving)<a href="https://agentmods.dev/skills/unicomai/wanwu/self-improving"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/self-improving.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00105 | $0.02112 |
| Opus 5 | $0.00053 | $0.01056 |
| Sonnet 5 | $0.00021 | $0.00422 |
| Haiku 4.5 | $0.00011 | $0.00211 |
Grade A, and why
Self-Improving + Proactive Agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- Self-Improving + Proactive Agent — 100% identical, 0 lines differ
- self-improving — 94% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
Workspace setup should add the standard self-improving steering to the workspace AGENTS, SOUL, and HEARTBEAT.md files, with recurring maintenance routed through heartbeat-rules.md.
~/self-improving/
├── memory.md # HOT: ≤100 lines, always loaded
├── index.md # Topic index with line counts
├── heartbeat-state.md # Heartbeat state: last run, reviewed change, action notes
├── 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 |
| Heartbeat state template | heartbeat-state.md |
| Memory template | memory-template.md |
| Workspace heartbeat snippet | HEARTBEAT.md |
| Heartbeat rules | heartbeat-rules.md |
| Learning mechanics | learning.md |
| Security boundaries | boundaries.md |
| Scaling rules | scaling.md |
| Memory operations | operations.md |
| Self-reflection log | reflections.md |
| OpenClaw HEARTBEAT seed | openclaw-heartbeat.md |
Requirements
- No credentials required
- No extra binaries required
- Optional installation of the
Proactivityskill may require network access
Learning Signals
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..."
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 251 lines · 105 tokens per session scan A 11109b39d0f7
Self-Improving + Proactive Agent is a skill published in the GitHub repository UnicomAI/wanwu (2,458 stars, last pushed yesterday), licensed Apache-2.0. It adds 105 tokens to every session and 2,112 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
dify
Use when building LLM applications with visual workflow — RAG knowledge bases, AI agents, chatbots with drag-and-drop orchestration. Dify: open-source LLM app platform supporting 30+ models (OpenAI, Claude, DeepSeek, Ollama, Qwen, GLM) with Docker deployment.
pinecone-research
Agent RAG and long-term memory with Pinecone.
memory-triage
Persistent long-term memory protocol powered by mem0. Evaluate conversations for durable facts worth storing via memoryadd. Handles identity, preferences, decisions, configurations, rules, projects, and relationships. Loaded by the openclaw-mem0 plugin when skills mode is active.
mem0-status
Diagnoses mem0 connectivity, API key validity, and memory read/write functionality. Use when memory operations fail, searches return empty, addmemory errors occur, or to verify the plugin is working correctly.
mem0-dream
Consolidates stored memories by merging duplicates, resolving contradictions, and pruning stale entries. Use when memory count is high, search results feel noisy or repetitive, or periodic cleanup is needed to maintain memory quality.
mem0-tour
Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.