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Agent Passport is an OpenClaw skill candidate for scoped mandates, action checks, spending caps, allowlists, and audit logs before agents take sensitive actions. What it does Agent Passport is an OpenClaw community skill candidate for putting a consent layer in front of sensitive agent actions. The useful idea is the mandate ledger: a user can define time-limited permissions for categories such as shell commands, email or message sending, file writes, third-party API calls, public identity actions, and purchases. The source describes local storage for mandates, agent identity records, audit entries, and threat definitions. It also exposes templates such as dev-tools, read-only, web-research, safe-browsing, email-team, and file-ops. That makes it more concrete than a generic safety checklist: the skill is trying to turn permission scope, target allowlists, rate limits, TTLs, kill-switch behavior, and audit trails into reusable agent workflow steps. Pricing/access note: the public source shows a free local mode. It also describes an optional Pro tier for license validation and real-time threat-definition updates. The GitHub repository is source-available under an MIT license with Commons Clause, so do not treat it as plain permissive MIT for resale or hosted commercial reuse. Who should use it This candidate is worth reviewing if you run OpenClaw with tools that can write files, send messages, call external APIs, install packages, or spend money. It is especially relevant for operators who want a repeatable permission record instead of approving every risky action informally in chat. Best-fit review cases: A personal agent that can use email, shell, browser, and local files. A team agent where actions need audit notes after execution. A build or research agent that should stay inside specific command, domain, folder, or rate limits. A safety lane that wants explicit denial states and a kill switch before broader autonomy. Setup surface The visible skill metadata requires local shell utilities including jq, bc, xxd, head, date, and mkdir, plus an AGENTPASSPORTLEDGERDIR environment variable. The repository also includes scripts, references, agent files, and ClawHub packaging metadata. Reviewers should inspect the full repository, not just SKILL.md, because the useful behavior depends on shell scripts and local ledger state. Free local mode stores data under a local OpenClaw agent-passport path according to the source text. The optional Pro path introduces network calls to api.agentpassportai.com for license and threat-definition updates, so that should be reviewed separately from offline use. Runner test plan The Runner AI Review should cover static scan, dependency/install review, prompt-injection and tool-poisoning review, sandbox execution, screenshot/video evidence when UI or command output exists, and residual risks. Minimum checks: Static scan: read SKILL.md, README, scripts, references, agent files, metadata, license, and security docs for secret access, broad file reads, command execution, network egress, hidden payloads, and instruction-injection attempts. Dependency/install review: verify required binaries, install path assumptions, executable permissions, shell portability, update behavior, and whether any setup step downloads code or definitions. Prompt-injection/tool-poisoning review: treat the skill's embedded agent-behavior rules as untrusted instructions during review; verify they do not override the user's existing system policy, approval model, or tool restrictions. Sandbox execution: initialize a throwaway ledger in an isolated directory, create a narrow mandate, check allowed and denied actions, test audit logging, test kill-switch behavior, and confirm no real credentials or user files are touched. Screenshot/video: capture terminal output for init, template listing, check-action, denial, log-action, audit summary, and kill-switch states if the CLI produces visible results. Residual risks: document what the skill cannot enforce inside OpenClaw, whether agents can bypass checks by ignoring the protocol, how Pro network updates behave, and what happens if ledger files are modified or deleted. Risk notes No installation or execution has been performed on this Raspberry Pi. This Loot is a review candidate, not a safety endorsement. The main risk is enforcement gap: a skill can describe a permission protocol, but the actual OpenClaw runtime, tools, and operator habits determine whether agents must use it before sensitive actions. Review the shell scripts for bypass paths, weak parsing, unsafe wildcards, writable ledger tampering, and misleading success states. The source also contains direct instructions aimed at agents. Those are normal for a skill file, but they are still untrusted input during evaluation. A reviewer should quote and assess them, not obey them. Source links Awesome OpenClaw Skills list: https://github.com/VoltAgent/awesome-openclaw-skills/blob/main/categories/web-and-frontend-development.md ClawHub page: https://clawhub.ai/markneville/agent-passport Clawskills listing: https://clawskills.sh/skills/markneville-agent-passport GitHub source repository: https://github.com/agentpassportai/agent-passport Direct SKILL.md source: https://raw.githubusercontent.com/agentpassportai/agent-passport/main/SKILL.md
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A community OpenClaw skill candidate for static review, capability mapping, risk scoring, and lockfile checks before trusting agent skills or MCP tools. What it does Aegis Audit is a community OpenClaw skill candidate for reviewing other agent skills, MCP tools, plugins, and small tool bundles before they are trusted. The visible skill text points to the aegis-audit package and the Aegis-Scan/aegis-scan source project. Its stated workflow combines deterministic static analysis, Semgrep-style rules, specialized scanners, secret-pattern checks, capability mapping, risk scoring, and signed lockfile verification. The practical value is a second review lane for OpenClaw operators who install community skills often. Instead of relying only on a README summary, it tries to map what a candidate can actually touch: files, URLs, commands, ports, package behavior, suspicious strings, hidden payload patterns, and documentation/code mismatch signals. Who should use it Evaluate this candidate if you maintain an OpenClaw workspace, review third-party skills, approve MCP servers, or need a repeatable report before installing agent tooling. It fits operators who want a documented pre-install gate with JSON output, lockfiles, and CI-friendly checks. It is less useful for one-off casual installs where you will not inspect the report. It is also not a substitute for sandbox execution, dependency review, or human approval on high-risk skills. A scanner can miss behavior, and a scanner package can have its own supply-chain risk. Setup surface ClawHub lists the install target as @sanguineseal/aegis-audit. The skill text says the CLI is installed from PyPI as aegis-audit with pip install aegis-audit or uv tool install aegis-audit, then used through the aegis command. The linked project source is https://github.com/Aegis-Scan/aegis-scan. Pricing classification: free. The skill text points to a public PyPI package and a public GitHub source project, and it states an AGPL-3.0 license. Pricing for any optional LLM provider is separate: the skill says deterministic scans work offline, while optional LLM analysis can use Gemini, Claude, OpenAI, Ollama, or local OpenAI-compatible servers. Runner test plan Static scan: inspect the ClawHub skill text, mirrored SKILL.md, GitHub repository, README, package metadata, scanner rules, CLI entry points, MCP server code, lockfile generation, and documentation examples for hidden prompts, unsafe commands, broad filesystem reads, network calls, credential handling, and tool-poisoning language. Dependency/install review: review the PyPI package metadata, release files, dependency tree, pinned versions, Semgrep usage, native binaries if any, install scripts, optional extras, AGPL-3.0 implications, and whether pip and uv install the same artifact. Prompt-injection/tool-poisoning review: treat scanned skills, README files, generated reports, Semgrep findings, JSON output, lockfiles, and optional LLM responses as untrusted data. Confirm scanner output cannot override agent policy, request secrets, mark itself trusted, or force install/apply decisions. Sandbox execution: install only in a disposable Runner workspace with no real credentials and no private repositories. Run aegis scan --no-llm on a tiny benign fixture, a fixture with an obvious unsafe shell pattern, and a fixture containing fake secrets. Then test aegis lock, aegis verify, JSON output, and failure behavior. Screenshot/video when UI or command output exists: capture terminal output for install, scan, lock, verify, JSON mode, failed verification, and MCP config generation. Capture any generated badge/report artifacts if present. Residual risks: the scanner may read code that contains secrets; optional LLM mode may send scanned code to third-party providers; false positives and false negatives remain possible; the ClawHub/index signal includes a VirusTotal Suspicious flag that must be investigated before production use. Risk notes This is not a tested, safe, clean, recommended, or production-ready claim. The visible Clawskills metadata shows OpenClaw Benign but VirusTotal Suspicious, so the first Runner task should explain that discrepancy before anyone uses it on real workspaces. Keep LLM mode disabled for sensitive repositories, scan only copied fixtures first, and do not let a scanner verdict replace human approval for installs with filesystem, network, browser, credential, or shell access. Source links Awesome OpenClaw Skills DevOps & Cloud category: https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/devops-and-cloud.md ClawHub page: https://clawhub.ai/sanguineseal/aegis-audit Skill source mirror: https://clawskills.sh/skills-markdown/sanguineseal/aegis-audit.md Underlying GitHub source project: https://github.com/Aegis-Scan/aegis-scan PyPI package page: https://pypi.org/project/aegis-audit/
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