OpenClaw is an AI assistant that runs on a user's own devices and connects to chat services such as Discord, Slack, Telegram, and WhatsApp. Individuals or teams use it for assistant tasks while keeping its state, memory, and credentials on their hardware and choosing the models or agent harnesses it uses. The catalogue entries are skills, agents, instructions, hooks, and settings that define or extend OpenClaw's workflow.
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 openclaw/openclaw --skill feishu-permgit clone --depth 1 https://github.com/openclaw/openclawWrote 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/openclaw/openclaw/feishu-perm)<a href="https://agentmods.dev/skills/openclaw/openclaw/feishu-perm"><img src="https://agentmods.dev/badge/skills/openclaw/openclaw/feishu-perm/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/openclaw/openclaw/feishu-perm"><img src="https://agentmods.dev/badge/skills/openclaw/openclaw/feishu-perm.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 | $0.00029 | $0.00206 |
| Opus 5.5 | $0.00012 | $0.00082 |
| Sonnet 5.5 | $0.00006 | $0.00041 |
| Haiku 4.5 | $0.00003 | $0.00021 |
Grade A, and why
feishu-perm 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 1mo 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.
What it actually says
Feishu permissions
Use the single feishu_perm tool and its current action schema. This tool is disabled by default because it changes access to user data; if it is unavailable, explain that channels.feishu.tools.perm must be enabled.
Workflow
- Resolve the exact file token and type.
- Use
listto inspect current collaborators before changing access. - For
add, resolve the collaborator's exact identifier and choose the least permission that satisfies the request. - For
remove, confirm the exact collaborator and file when the request is ambiguous or broad. - Report the resulting permission change without exposing unrelated collaborator data.
Never infer an email, user ID, department, or chat from a display name alone. Follow the current schema for supported member types, token types, and permission levels.
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.
- 1mo ago First seen · 20 lines · 29 tokens per session scan A cac96b74d35f
feishu-perm is a skill published in the GitHub repository openclaw/openclaw (392,124 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 206 once invoked, about $0.0001 per session on Opus 5.5. 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.
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