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-awarenessnpx skills add UnicomAI/wanwu --skill self-awarenessgit 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-awareness)<a href="https://agentmods.dev/skills/unicomai/wanwu/self-awareness"><img src="https://agentmods.dev/badge/skills/unicomai/wanwu/self-awareness.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 | $0.00121 | $0.03646 |
| Opus 5 | $0.00060 | $0.01823 |
| Sonnet 5 | $0.00024 | $0.00729 |
| Haiku 4.5 | $0.00012 | $0.00365 |
Grade A, and why
self-awareness scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-awareness — Claude Science's own database and SDK
host.query(sql, params=[], limit=None, df=False) runs read-only SQLite
against Claude Science's own metadata DB. It is only available via the repl
tool (not python/r). Results are automatically scoped to the current
project, so SELECT * FROM frames returns only frames in this project. The
repl tool is stdlib-only — df=True returns the raw dict there (use
json.dump(..., open("handoff/q.json","w")) and load in a python cell if
you want pandas).
Dialect and limits
- SQLite. Epoch-milliseconds for all timestamps
(
created_at > strftime('%s','now','-1 day')*1000). Booleans are0/1. JSON columns are TEXT — usejson_extract(col, '$.key'). Recursive CTEs OK. SELECT/WITH/PRAGMA/EXPLAINonly; one statement per call;?placeholders withparams=[...].- Scoping. Most tables are transparently filtered to the current
project (and
memoriesto the current user) via CTEs that shadow the real tables —session_claims,verification_checks, andpoller_leaseare unscoped. You therefore cannot usemain.table/temp.table— schema-qualified names are rejected. - Caps. Default 200 rows (max
limit=1000); cells >2000 chars are clipped in place with a…[+N chars]marker; total serialized output capped at ~100k chars (truncated=True,truncation_reason="total_size_cap"— narrow your columns). 5-second timeout. - Schema introspection:
host.query("PRAGMA table_info(frames)")orhost.query("SELECT name, sql FROM sqlite_master WHERE type='table'").
Queryable tables
Session / conversation
frames — one row per agent frame (a root conversation or a delegated
sub-agent). The frame you are running in now is one of these rows.
Key columns: id, parent_frame_id, root_frame_id, agent_name,
delegate_name, status (processing/completed/failed/cancelled/
awaiting_user_response/awaiting_plan_approval), model, effort, input_tokens,
output_tokens, cache_read_tokens, cache_write_tokens, total_cost,
task_summary, status_description, conversation_type, name,
project_id, created_at, updated_at, completed_at,
last_user_message_at, is_hidden.
JSON columns: input_data (what started the frame), output_data
(json_extract(output_data,'$.response') is the final response text),
context_data (the full serialized runner state — see below),
mentioned_artifact_ids, specialists_used.
What ships with it
1 file 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.
- 6d ago First seen · 263 lines · 121 tokens per session scan A 9cef2a4ddfd6
self-awareness is a skill published in the GitHub repository UnicomAI/wanwu (2,458 stars, last pushed 2d ago), licensed Apache-2.0. It adds 121 tokens to every session and 3,646 once invoked, about $0.0006 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 2026-08-30.
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.
ai-skills
Use when building LLM applications, RAG knowledge bases, AI agents, terminal coding agents, multi-model orchestration, plugin-based agent harnesses, or file translation. Index of 9 skills: Dify, Hermes Agent, OpenClaw, OpenCode, Pi, DocuTranslate, Oh-My-OpenAgent, Superpowers-zh, DeepSeek Harness.
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-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-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.