OpenClaw Master Skills is a curated, regularly updated collection of skills that extends an AI personal assistant platform with capabilities such as research, browser automation, presentation creation, and prompt work. It is intended for people using OpenClaw or MyClaw.ai to give their agents additional tasks and workflows. The catalogue contains many skills and agents from this collection.
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 LeoYeAI/openclaw-master-skills --skill acl-rule-analysisgit clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skillsWrote 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/leoyeai/openclaw-master-skills/acl-rule-analysis)<a href="https://agentmods.dev/skills/leoyeai/openclaw-master-skills/acl-rule-analysis"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/acl-rule-analysis/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/leoyeai/openclaw-master-skills/acl-rule-analysis"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/acl-rule-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 161 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
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 | $0.00064 | $0.04065 |
| Opus 5.5 | $0.00026 | $0.01626 |
| Sonnet 5.5 | $0.00013 | $0.00813 |
| Haiku 4.5 | $0.00006 | $0.00407 |
Grade A, and why
acl-rule-analysis 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 2mo 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 — 414 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL and Firewall Rule Analysis
Vendor-agnostic rule analysis for access control lists and firewall policies. Unlike vendor-specific firewall audit skills that evaluate platform features (App-ID, Security Profile Groups, zone protection), this skill focuses on universal rule patterns that apply across all platforms: shadowed rules, redundant rules, overly permissive rules, and unused rules.
Covers ACL-based platforms (Cisco IOS/IOS-XE/ASA, Juniper JunOS, Arista EOS) and policy-based firewalls (Palo Alto PAN-OS, Fortinet FortiGate, Check Point). The analysis algorithms are vendor-agnostic — only the rule retrieval commands differ by platform.
Commands use inline labels [Cisco], [JunOS], [EOS], [PAN-OS],
[FortiGate], [CheckPoint] where syntax diverges. Unlabeled statements
apply universally. See references/cli-reference.md for full command tables
and references/rule-patterns.md for detection algorithm details.
When to Use
- Post-migration rule cleanup after converting from one platform to another
- Periodic rulebase hygiene to remove accumulated technical debt
- Compliance preparation requiring rule-level justification and minimal privilege
- Incident investigation — determining whether a rule permitted malicious traffic
- Change validation after rulebase modifications to confirm no shadowed rules
- Capacity optimization — reducing rule count to improve lookup performance
- Merger/acquisition integration — consolidating overlapping rulebases
Prerequisites
- Read-only access to the target device via SSH, console, or API
- Rulebase with hit counters enabled (most platforms enable by default)
- For unused rule detection: hit count data accumulated over an extended period (30+ days minimum, 90 days recommended for seasonal traffic patterns)
- Knowledge of intended security policy — which traffic should be permitted and which should be denied between network segments
- Understanding of implicit deny behavior for the platform (varies — see Troubleshooting)
What ships with it
3 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.
- 2mo ago First seen · 414 lines · 64 tokens per session scan A 410666b52d95
acl-rule-analysis is a skill published in the GitHub repository LeoYeAI/openclaw-master-skills (2,168 stars, last pushed 6d ago), licensed MIT. It adds 64 tokens to every session and 4,065 once invoked, about $0.0003 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 2026-08-30.
acl-rule-analysis compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| acl-rule-analysisLeoYeAI/openclaw-master-skills | Skill | 2.2k | 6d ago | MIT | A | 64 | Claude Code, Codex |
| pentest-api-deepjd-opensource/JoySafeter | Skill | 315 | 17d ago | Apache-2.0 | A | 43 | Claude Code, Codex |
| agent-forgeAlekseiUL/agentforge-openclaw | Skill | 44 | 8d ago | MIT | A | 111 | Claude Code, Codex |
| investlongsizhuo/openInvest | Skill | 139 | yesterday | MIT | B | 280 | Claude Code |
| aeonagent-skills-hub/agent-skills-hub | Skill | 128 | 1mo ago | MIT | A | 74 | Claude Code, Codex |
| halo-cli-shophalo-dev/cli | Skill | 30 | 3d ago | MIT | A | 78 | Claude Code, Codex |
| halo-cli-moderation-notificationshalo-dev/cli | Skill | 30 | 3d ago | MIT | A | 57 | Claude Code, Codex |