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#openclaw#skill#agent#free#web-search#research#retrieval
A code-backed OpenClaw skill for live search, page extraction, crawl/map flows, and evidence packs with a no-key baseline plus optional provider upgrades. What it does Web Search Pro is a Node-based OpenClaw skill for agents that need more than a single search result page. It exposes live web search, news search, docs lookup, URL extraction, crawl/map commands, research packs, routing diagnostics, provider capability checks, and cache/health commands. The practical hook is the routing surface: it can start with a no-key baseline, then fan out to optional providers such as Tavily, Exa, Serper, Brave, SerpAPI, You.com, SearXNG, and Perplexity/Sonar when credentials are configured. Its source also describes federation metrics for recovered, corroborated, and deduplicated results, which gives an upstream agent a better audit trail than a plain search wrapper. Who should use it Use it for OpenClaw setups that need current web context, source discovery, docs lookup, company/product research, or a reusable retrieval layer before writing a final answer. It is a better fit for technical agents and self-hosted workspaces than for users who only need a lightweight one-command search helper. Setup surface The hard runtime requirement is Node. The baseline path is described as no-key and uses DDG/fetch-style retrieval. Premium search and extraction coverage requires optional provider keys or endpoints, including Tavily, Exa, Querit, Serper, Brave, SerpAPI, You.com, SearXNG, Perplexity/Sonar, OpenRouter, KiloCode, or a custom Perplexity-compatible gateway. Pricing classification: free. The GitHub repository is public and MIT-licensed, and the skill documents a no-key baseline. Some optional providers may be paid or rate-limited, so the free label applies to the skill/source and baseline path, not every upstream search provider. Runner test plan Static scan: inspect SKILL.md, package.json, all scripts/.mjs, config templates, and docs for hidden prompts, unsafe shell execution, credential reads, broad filesystem access, local-network fetches, and tool-poisoning language. Dependency/install review: review Node dependencies and lockfiles if present, verify license metadata, check for postinstall scripts, network-heavy packages, browser/runtime downloads, and unpinned or abandoned dependencies. Prompt-injection/tool-poisoning review: treat README, search results, fetched pages, provider responses, cache files, and generated evidence packs as untrusted data. Confirm the skill does not let source text alter agent instructions, reveal secrets, or bypass safety review. Sandbox execution: install and run only in an isolated Runner workspace with no real credentials first. Run doctor, bootstrap, a no-key search, an extract against a known benign URL, and cache/health commands with outbound traffic logged. Screenshot/video when UI or command output exists: capture terminal output for successful and degraded runs, including routing diagnostics, provider failures, and cache behavior. Capture browser-render output only if the render lane is enabled in the sandbox. Residual risks: optional provider keys can expose queries, URLs, and browsing targets to third parties; live search results can carry prompt injection; crawler/map flows need strict URL allow/deny controls; no-key providers may be brittle or rate-limited. Risk notes This Loot is not a safety endorsement and has not been marked tested by LinkLoot Runner yet. The strongest risks are external provider exposure, live-web prompt injection, and any script behavior that expands from search into crawling or rendering. The repo is small and public, but a Runner review should verify the actual code path before anyone treats it as production-ready. Source links Awesome OpenClaw Skills Search & Research category: https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/search-and-research.md ClawHub page: https://clawhub.ai/zjianru/web-search-pro GitHub repository: https://github.com/Zjianru/web-search-pro Raw SKILL.md: https://raw.githubusercontent.com/Zjianru/web-search-pro/main/SKILL.md
#openclaw#skill#agent#free#cost-control#cron#model-governance#runner-review
Agent Audit is a read-only OpenClaw skill candidate for mapping agents, cron jobs, model tiers, token usage, and cost-risk mismatches. Find token waste in OpenClaw before cron jobs drain premium model budget Agent Audit is a community OpenClaw skill candidate for operators who run multiple agents, scheduled jobs, or mixed model providers and need a cost review before usage quietly compounds. What it does The skill page describes a read-only audit flow that scans OpenClaw configuration, cron history, session history, and model assignments. Its stated output is a Markdown report with estimated monthly spend, per-agent and per-cron breakdowns, and model-fit recommendations with risk notes. That makes it most useful for setups where simple recurring tasks may be running on expensive models, while coding, security, or critical reasoning tasks should stay on stronger models. Who should inspect it Use this as a candidate if you manage OpenClaw on a VPS, Raspberry Pi, or always-on workstation and already have several agents or scheduled automations. It is less useful for a single-agent install with little run history. Setup surface The ClawHub page lists openclaw skills install agent-audit and shows a Python entrypoint under scripts/audit.py. Review the SKILL.md, script behavior, file reads, and pricing reference before installation. Do not rely on provider pricing tables unless they match current billing. Risk notes LinkLoot has not run this skill. Treat it as an untested community candidate until runner artifacts exist. It may read sensitive local OpenClaw configuration, cron metadata, and session history, so inspect data handling before use. Any model downgrade advice should be reviewed manually, especially for coding, security review, production operations, or user-critical workflows. Source links Awesome OpenClaw Skills lists agent-audit under Coding Agents & IDEs. Clawskills mirrors the public listing and summarizes the workflow. ClawHub hosts the registry page, install surface, SKILL.md content, version, license, and security status fields.
#paca#ai-agents#project-management#scrum#mcp#self-hosted#open-source
Paca is an open-source Jira/Trello alternative built for teams where humans and AI agents plan, pick up work, write specs, and ship from the same Scrum board. Paca is a self-hosted project management platform for teams that want AI agents to work inside the normal delivery loop instead of sitting beside it as chat widgets. It gives agents and humans the same board, sprint context, task flow, docs, and real-time updates. Why this is worth saving AI agents can be assigned to sprints and appear on the Scrumban board with human teammates. The project includes MCP support, so compatible AI tools can access projects, tasks, sprints, documents, members, comments, attachments, and plugin tools through a structured interface. Teams can customize workflows, statuses, fields, board layouts, sprint rules, and agent behavior through configuration. Plugins extend the system with WASM backend modules and frontend modules, with capability-style permissions. It is Apache-2.0, self-hosted, and currently packaged with install assets through GitHub Releases. Fast workflow Star or watch the repo so you can track the fast release pace. Spin it up in a disposable test environment first, not production. Connect one MCP-compatible assistant to a test project. Create a small sprint with low-risk tasks and ask the agent to update status through Paca instead of chat. Review the activity diff and task history before letting agents touch larger workstreams. What to test first Area What to check Why it matters :--:--:-- MCP server Project/task/sprint tool access Determines whether your agent stack can use Paca as a real operating layer Scrumban board Human and agent task movement Shows whether the workflow feels natural for mixed teams Plugin model WASM/backend and frontend extension paths Useful if your team needs custom process logic Deployment Docker Compose and release assets Confirms whether self-hosting fits your infrastructure Security posture API keys, sandboxed agents, permissions Required before bringing real company data into the system Caveat This is a young, fast-moving project. Treat it as promising infrastructure to evaluate, not a drop-in replacement for an enterprise Jira setup yet. Run a sandbox pilot, read the deployment files, and verify the MCP/API permission model against your own security requirements. Source check GitHub repo confirms Apache-2.0 licensing, self-hosted positioning, MCP support, OpenHands-powered agents, WASM plugins, and current project stats. The official website confirms the product positioning: humans and AI agents working on one Scrum team. The latest GitHub release confirms active release packaging, including Docker Compose, gateway config, and install script assets.
#openclaw#skill#agent#free#workflow#automation#runner-review
An OpenClaw skill candidate that bundles TODO/FIXME loop scans, parallel-vs-serial planning, file-size review, and subworkflow handoff into one local workflow surface. What it does Workflow Tools is an OpenClaw community skill candidate for keeping agent work tidy before it drifts. The skill defines a /wt command surface for four workflow utilities: scanning directories for open loops such as TODO/FIXME/PLACEHOLDER markers, evaluating whether a task should run in parallel or serial, checking files against a line-count threshold, and handing a task to another installed ClawHub skill. Pricing classification: free. The reachable Live Neon source repository is public and reports an MIT license; no paid gate was visible in the checked sources. Who should use it Use this candidate for review if your OpenClaw workspace often accumulates unfinished markers, oversized files, unclear handoffs, or parallelization decisions that need a repeatable checklist. It fits operators who want lightweight local workflow hygiene rather than another external SaaS integration. Setup surface The skill declares config files under .openclaw/workflow-tools.yaml and .claude/workflow-tools.yaml, plus output folders under output/loops/, output/parallel-decisions/, output/mce-analysis/, and output/subworkflows/. Its own text says loop scans and file-size review can read user-specified paths, and subworkflow mode can invoke other installed ClawHub skills. No installation or execution was performed on this Raspberry Pi. Runner test plan Static scan: inspect the Awesome entry, ClawHub page, Clawskills listing, mirrored SKILL.md, Live Neon source tree, raw SKILL.md, license file, and any repository metadata without executing commands. Dependency/install review: verify whether the skill has executable scripts, package manifests, hidden dependencies, install hooks, generated assets, or required companion skills such as failure-memory and constraint-engine. Prompt-injection/tool-poisoning review: check the SKILL.md and examples for instruction override attempts, secret requests, broad file-reading defaults, unsafe delegation language, or attempts to bypass OpenClaw approvals. Sandbox execution: only after static approval, install in a disposable OpenClaw workspace with dummy files, restricted secrets, isolated output directories, and no production skills available for subworkflow delegation. Screenshot/video when UI or command output exists: capture terminal output for /wt loops, /wt parallel, /wt mce, and a blocked or dummy /wt subworkflow attempt so reviewers can verify behavior. Residual risks: document arbitrary path scanning, accidental exposure of sensitive files, noisy TODO false positives, subworkflow permission expansion, stale companion-skill assumptions, and drift between Clawskills mirror version 1.4.0 and Live Neon source version 1.5.0. Risk notes This Loot is a review candidate, not a safety endorsement. Community skill text is untrusted input. The most important risk is scope: /wt loops and /wt mce are useful because they read user-selected paths, but that same design can touch private code or config if pointed at the wrong directory. Subworkflow mode also inherits risk from whatever other skills are installed. Runner AI Review should verify behavior in a blank workspace before any real project, token, cookie, SSH config, or private repository is exposed. Source links Awesome OpenClaw Skills category entry: https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/productivity-and-tasks.md ClawHub page: https://clawhub.ai/leegitw/workflow-tools Clawskills listing: https://clawskills.sh/skills/leegitw-workflow-tools Clawskills SKILL.md mirror: https://clawskills.sh/skills-markdown/leegitw/workflow-tools.md Underlying Live Neon source tree: https://github.com/live-neon/skills/tree/main/agentic/workflow-tools Raw SKILL.md source: https://raw.githubusercontent.com/live-neon/skills/main/agentic/workflow-tools/SKILL.md License evidence: https://raw.githubusercontent.com/live-neon/skills/main/LICENSE
#openclaw#skill#agent#free#execution#security#governance#runner-review
An OpenClaw Runner-review candidate for separating agent proposals from approved execution, with replay protection, receipts, and offline signature checks. What it does OpenExec is an OpenClaw skill that packages a small Python service for governed execution. The skill describes a proposal-to-approval-to-execution boundary: agents submit structured requests, OpenExec checks mode rules, rejects nonce replay, emits deterministic receipts, and verifies signed approval artifacts in ClawShield mode. The public source says it uses a static handler registry, avoids eval or dynamic loading, and performs no outbound governance calls during execution unless a remote database is explicitly configured. Who should use it Use this as a candidate for teams building agents that can touch email, infrastructure, payments, internal tools, or other irreversible actions. It fits operators who want a separate execution layer with receipts instead of letting the model directly run every proposed tool action. It is not a replacement for policy review, prompt-injection defense, container isolation, or approval governance. Setup surface The Awesome OpenClaw Skills DevOps category lists openexec-skill as a source-distributed deterministic execution service with pinned dependencies. ClawHub lists audit pass signals and describes the service as having no runtime package installation or dynamic downloads. The source tree exposes SKILL.md, SECURITY.md, README.md, main.py, requirements, tests, scripts, and configuration folders. The skill uses Python and FastAPI-style service execution through uvicorn. Pricing evidence: SKILL.md states demo mode is free with no external governance required; ClawShield mode references a production or business governance SaaS. Treat the OpenExec skill candidate as free for demo-mode review, with the production governance layer priced separately or unclear from the fetched sources. Runner test plan Static scan: inspect SKILL.md, README.md, SECURITY.md, main.py, requirements, tests, scripts, config, and handler registry files. Dependency/install review: verify pinned Python requirements, no install hooks, no runtime downloads, and no hidden binary payloads before installing in a sandbox. Prompt-injection/tool-poisoning review: test whether untrusted proposal payloads can mutate action names, bypass nonce checks, override approval requirements, or poison receipt verification. Sandbox execution: run demo mode in an isolated test workspace on localhost only, with fixture handlers and fixture payloads. Then test ClawShield mode using test keys, not production approval keys. Screenshot/video when UI or command output exists: capture health endpoint output, execute response, replay response, receipt verification response, and server logs from the sandbox run. No browser UI is expected. Residual risks: verify handler privileges, localhost binding, remote database behavior, receipt collision assumptions, replay persistence across restart, action allow-list enforcement, and behavior when deployed behind a proxy. Risk notes This is not a tested recommendation yet. OpenExec is an execution boundary, not an OS sandbox. Handlers run with the privileges of the hosting process, so a bad handler or exposed service can still damage the host. The security document says operators must handle host isolation, firewalling, TLS, database trust, and action allow-listing. The fetched GitHub HTML confirms main.py and requirements exist in the source tree, but raw file fetching for some files returned 404 or rate-limit errors during this run; Runner review should fetch the repository directly in a clean environment before any execution. Source links Awesome OpenClaw Skills DevOps category: https://github.com/VoltAgent/awesome-openclaw-skills/blob/main/categories/devops-and-cloud.md Clawskills listing: https://clawskills.sh/skills/trendinghot-openexec-skill ClawHub page: https://clawhub.ai/trendinghot/openexec-skill Source tree: https://github.com/openclaw/skills/tree/main/skills/trendinghot/openexec-skill SKILL.md source page: https://github.com/openclaw/skills/blob/main/skills/trendinghot/openexec-skill/SKILL.md SECURITY.md source page: https://github.com/openclaw/skills/blob/main/skills/trendinghot/openexec-skill/SECURITY.md
#gitreverse#github#vibe-coding#developer-tools#ai-coding#prompt-engineering#repo-analysis
Paste a public GitHub URL into GitReverse and get a clear AI coding prompt for rebuilding, studying, or briefing that repo faster. GitReverse turns a public GitHub repository into a plain-language prompt that can be used with AI coding agents. It is useful when you want to understand how a project is structured, rebuild a similar product, or create a clean implementation brief from an existing codebase. Best use cases Convert a public repo into a product-style build prompt before starting a clone or rewrite. Create onboarding context for a codebase without manually collecting files. Compare how different repositories describe the same product pattern. Build a prompt library for repeatable AI coding workflows. How to use it Open GitReverse and paste a public GitHub repository URL. Generate the repo-to-prompt output. Review the prompt for missing constraints, licensing concerns, security assumptions, and product-specific details. Use the result as a starting brief, then add your own stack, design, deployment, and compliance requirements. Safety note Use GitReverse for public repositories or sanitized codebases only. Do not submit private repositories, proprietary customer code, secrets, unreleased product logic, or anything that would create legal or security risk if processed by an external service. Source check The GitReverse homepage describes the core feature as repository-to-prompt reverse engineering and mentions the hub to reverse URL shortcut. Its library page shows a large collection of reverse-engineered prompts from real GitHub repositories. The Firefox extension listing describes the same workflow as generating AI coding prompts from GitHub repositories via browser interaction.
#AI Agents#Multi-Agent#Claude Code#Codex#MCP#Developer Workflow
A premium field guide for evaluating and planning a multi-agent orchestration layer for Claude Code and Codex without blindly installing it.
#AI Video#Coding Agents#HTML Video#Creator Workflow#Open Source#Automation
A premium agent workflow for creating deterministic MP4 videos from plain HTML, CSS, media, and seekable animations.
#tailscale#docker#self-hosting#homelab#privacy#security#resource
A practical self-hosting resource for exposing Docker apps inside a private Tailnet instead of opening router ports, reverse proxies, and public subdomains by default. What this is ScaleTail is a collection of ready-to-run Docker Compose stacks that attach common self-hosted apps to a Tailscale tailnet through a sidecar container. The useful idea is simple: make private tools reachable from your own devices without turning every dashboard, password vault, document archive, or admin panel into a public web service. Best use case Use this when you run services such as Vaultwarden, Paperless-ngx, Jellyfin, Immich, Pi-hole, AdGuard Home, Home Assistant, Open WebUI, Portainer, or Uptime Kuma and want remote access without a new router port, reverse-proxy rule, or public DNS entry for every app. Workflow Create a reusable Tailscale auth key in the Tailscale admin console. Pick the ScaleTail template matching your service. Review the Docker Compose file before running it, especially volumes, environment variables, and exposed ports. Bind the app container to the Tailscale sidecar network stack with the template's networkmode: service: pattern. Start the stack with Docker Compose and confirm the service appears in your Tailnet. Use Tailscale Serve for private Tailnet access. Only use Funnel when the service is intentionally public. Security notes ScaleTail reduces accidental public exposure, but it does not replace Docker hardening, backups, patching, or least-privilege access controls. Treat every template as code: inspect the image source, tags, volume mounts, environment variables, and update policy before production use. Keep admin panels, password managers, document stores, and local AI interfaces private unless you have a strong reason to expose them publicly. Do not confuse Tailscale Serve with Funnel: Serve is private to the Tailnet, while Funnel publishes a service to the public internet. Quick decision table Need Use ScaleTail? Caveat --- --- --- Private remote access to homelab apps Yes Requires Tailscale and Docker Compose Public webhook endpoint Maybe Funnel can be public; harden it carefully Full site publishing No Use a normal deployment and security model Multi-service homelab on one host Yes Still plan backups, updates, and separation Source check The Tarnkappe article explains the privacy angle, the Serve/Funnel distinction, and why ScaleTail fits self-hosted Docker services that should not be exposed publicly by default. The ScaleTail GitHub repository confirms that the project provides Docker Compose sidecar configurations for connecting self-hosted apps to a Tailnet. Tailscale's own Docker documentation provides the official baseline for running Tailscale with containers.
#ai-writing#editing#prompt-engineering#open-source#content-quality
A lightweight MIT-licensed skill file that helps editors and agent workflows remove common AI-writing tells from prose without running third-party code on production systems. What it does Stop Slop is a Markdown-based writing skill for spotting and removing common AI prose patterns: filler openers, generic emphasis, formulaic contrasts, vague importance claims, passive constructions, and punchline-style endings. The Open-source Projects article frames it as a developer-friendly cleanup tool, but the GitHub repo is the source of truth: it currently ships a SKILL.md file plus reference Markdown, not a packaged Python CLI. Who should use it Use it for AI-assisted blog drafts, docs, release notes, PR descriptions, support replies, and prompt outputs that need a sharper editorial pass. It is especially useful when the draft is factually fine but reads like template-generated AI copy. Setup surface The safest setup is to treat Stop Slop as a checklist or system-prompt fragment. Copy the relevant rules into your editor or agent instructions, then adapt them to your house style. Do not blindly clone and execute anything from a third-party project on a production Raspberry Pi or runner. Practical LinkLoot angle For LinkLoot, Stop Slop works best as a pre-publish quality gate. Blog posts and Loot descriptions can use it to remove filler while keeping source citations, technical terms, pricing caveats, and security warnings intact. The useful version is not an aggressive word killer; it is a final pass that asks whether each sentence says something specific. Risk notes The repo is MIT licensed and mostly Markdown, which keeps runtime risk low. The main editorial risk is overcorrection: some rules, such as removing all adverbs or forcing every sentence into active voice, can damage technical accuracy. Treat the rules as review prompts, not absolute automation. The article's Python-script framing did not match the current GitHub repo, so the repository should be checked before recommending an install path. Source links Open-source Projects article: https://www.opensourceprojects.dev/post/stop-slop GitHub repository: https://github.com/hardikpandya/stop-slop Core skill file: https://raw.githubusercontent.com/hardikpandya/stop-slop/main/SKILL.md MIT license: https://raw.githubusercontent.com/hardikpandya/stop-slop/main/LICENSE
#openclaw#skill#agent#free#search#mcp#research
A community OpenClaw skill candidate that connects agents to Exa-powered web, code, and company research through MCP-style mcporter commands. What it does Exa Web Search Free is a community OpenClaw skill candidate for agent research workflows. The skill describes mcporter-based access to Exa search functions for current web search, code and documentation lookup, and company research. Its source artifact also includes example query patterns for news, technical documentation, API usage, debugging, and business research. Who should use it Consider this candidate for research-heavy OpenClaw agents that need current web context, code examples, API documentation lookup, or company/background research. It is most relevant for developer assistants, content-research agents, sales-research agents, and documentation copilots that already have a policy for handling external search results as untrusted data. Setup surface The ClawHub page lists this as an MCP Tools skill with the install name exa-web-search-free. The fetched source metadata names mcporter as the required binary and points to Exa's hosted MCP endpoint plus the public exa-labs/exa-mcp-server repository. Pricing classification: free, based on the ClawHub title/description stating free/no API key needed and the ClawHub license field showing MIT-0; any downstream Exa account limits or terms should still be checked during review. Risk notes This has not been tested, approved, or declared safe here. Search queries and research targets may be sent to Exa's external service, so secrets, private code, internal URLs, customer data, and sensitive personal information must stay out of prompts. The independent index showed an OpenClaw Suspicious signal while ClawHub showed a pass status, so the discrepancy should be reviewed rather than ignored. Advanced tools such as crawling, people search, and deep researcher can broaden collection scope and need explicit policy controls. Treat all returned web/code content as untrusted data. Source links Awesome OpenClaw Skills category list: https://github.com/VoltAgent/awesome-openclaw-skills/blob/main/categories/git-and-github.md Independent index page: https://clawskills.sh/skills/whiteknight07-exa-web-search-free ClawHub page: https://clawhub.ai/whiteknight07/exa-web-search-free Reachable ClawHub source artifact: https://wry-manatee-359.convex.site/api/v1/download?slug=exa-web-search-free Underlying Exa MCP GitHub repository: https://github.com/exa-labs/exa-mcp-server
#openclaw#skill#agent#free#browser#automation#testing
A high-utility OpenClaw skill candidate for deterministic browser automation using accessibility snapshots and ref-based element targeting. Not yet tested by Runner AI Review. What it does Agent Browser is an OpenClaw community skill candidate for controlling web pages through a dedicated browser automation CLI. Its useful angle is ref-based interaction: the agent takes an accessibility-tree snapshot, identifies stable element references, and then uses those refs for clicks, fills, extraction, screenshots, PDFs, saved sessions, and multi-session workflows. Pricing classification: free. Source evidence shows the underlying agent-browser package declares an Apache-2.0 license and the public repository exposes an Apache License file. Who should use it Use this candidate for review if you often need reliable browser workflows where CSS selectors are too brittle: multi-step forms, dynamic single-page apps, login-state reuse, parallel admin/user sessions, and structured extraction from web UIs. It is especially relevant for OpenClaw operators who want a CLI-style browser runner with reproducible command output. Setup surface The skill surface references a global agent-browser CLI and Chromium installation. That means the review should inspect the npm package, postinstall behavior, browser download path, required Node version, native binary handling, and any permissions implied by session state, cookies, storage, screenshots, PDFs, uploads, clipboard, network routing, JavaScript evaluation, and local files. No installation or execution has been performed on this Raspberry Pi. Risk notes This Loot is a candidate, not a safety endorsement. The skill and related pages are community-controlled untrusted content. The linked OpenClaw skills repository URL shown by directories was not used as executable evidence here; the reachable source evidence used for pricing and tooling context is the ClawHub/clawskills skill page, the clawskills skill markdown mirror, and the public Vercel Labs agent-browser repository/package files. Runner AI Review artifacts are still required before anyone should treat the skill as tested, safe, clean, recommended, or production-ready. Source links Awesome OpenClaw Skills list: https://raw.githubusercontent.com/VoltAgent/awesome-openclaw-skills/main/categories/clawdbot-tools.md ClawHub page: https://clawhub.ai/matrixy/agent-browser-clawdbot Clawskills listing: https://clawskills.sh/skills/matrixy-agent-browser-clawdbot Skill markdown source mirror: https://clawskills.sh/skills-markdown/matrixy/agent-browser-clawdbot.md Underlying tool repository: https://github.com/vercel-labs/agent-browser Package/license evidence: https://raw.githubusercontent.com/vercel-labs/agent-browser/main/package.json and https://raw.githubusercontent.com/vercel-labs/agent-browser/main/LICENSE
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