QwenPaw is a personal AI assistant that runs on a local machine or in the cloud and connects to multiple chat applications. It provides memory, file workspaces, multiple agents, skills, plugins, and integrations with language-model providers and external tools. The catalogue entries are skills that extend its capabilities.
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 skills add agentscope-ai/QwenPaw --skill guidance-engit clone --depth 1 https://github.com/agentscope-ai/QwenPawWrote 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/agentscope-ai/qwenpaw/guidance-en)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/guidance-en"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/guidance-en/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/guidance-en"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/guidance-en.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.01331 |
| Opus 5 | $0.00021 | $0.00665 |
| Sonnet 5 | $0.00008 | $0.00266 |
| Haiku 4.5 | $0.00004 | $0.00133 |
Grade A, and why
guidance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- guidance — 91% identical, 34 lines differ
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QwenPaw Installation and Configuration Q&A Guide
Use this skill when the user asks about QwenPaw installation, initialization, environment configuration, dependency requirements, or common configuration options.
Core principles:
- Check local documentation first, then answer
- Base answers on what has actually been read, do not speculate
- Answer in the same language the user used to ask
Standard Flow
Step 1: Locate the Documentation Directory
Use built-in path resolution (works for all install methods)
DOCS_DIR=$(python3 -c "from qwenpaw.constant import DOCS_DIR; print(DOCS_DIR or '')" 2>/dev/null)
If the above returns a non-empty path and the directory exists, use it directly and skip to Step 2.
If it fails (e.g., older version without DOCS_DIR), fall back in the following order:
Check for documentation directory in memory
First, check whether there is a documentation directory in memory. If found, use it directly; otherwise, proceed to the next step.
# Get the documentation directory from memory
DOCS_DIR=$(find ~/.qwenpaw/memory/ -type d -name "docs")
If there is no documentation directory in memory, continue with the following logic.
Check the documentation directory in the project source code
Run the following script logic to obtain the variable $QWENPAW_ROOT:
# Get the absolute path of the binary
QWENPAW_PATH=$(which qwenpaw 2>/dev/null || whereis qwenpaw | awk '{print $2}')
# Logical deduction: if the path contains .qwenpaw/bin/qwenpaw, the root is three levels up
# Example: /path/to/QwenPaw/.qwenpaw/bin/qwenpaw -> /path/to/QwenPaw
if [[ "$QWENPAW_PATH" == *".qwenpaw/bin/qwenpaw" ]]; then
QWENPAW_ROOT=$(echo "$QWENPAW_PATH" | sed 's/\/\.qwenpaw\/bin\/qwenpaw//')
else
# Fallback: try to get the parent of the parent directory
QWENPAW_ROOT=$(dirname $(dirname "$QWENPAW_PATH") 2>/dev/null || echo ".")
fi
echo "Detected QwenPaw Root: $QWENPAW_ROOT"
Verify and list the documentation directory: Use the derived $QWENPAW_ROOT to locate the documentation:
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.
- 12d ago First seen · 148 lines · 42 tokens per session scan A f07e74a89781
guidance is a skill published in the GitHub repository agentscope-ai/QwenPaw (34,741 stars, last pushed yesterday), licensed Apache-2.0. It adds 42 tokens to every session and 1,331 once invoked, about $0.0002 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.
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