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Scrape Changing Websites with Anansi Self-Healing Selectors and MCP

#web scraping#mcp#python#crawler#ai agents#data extraction#automation

A Python crawler for unstable or JavaScript-heavy sites, with selector healing, structured-data extraction, adaptive rate limiting, and an MCP server for agent-driven crawling. Use only for authorized scraping. Anansi is a Python web scraping toolkit designed for sites that change often or need browser rendering. It combines adaptive parsing, structured-data extraction, incremental crawling, proxy support, and an MCP server so an LLM or agent workflow can drive fetch, extract, crawl, pause, resume, export, and metrics actions. Why it is useful Self-healing selectors: stores selector confidence and attempts fallback strategies when a layout changes. Structured extraction first: pulls JSON-LD, Open Graph, and Microdata before relying on brittle CSS selectors. Browser upgrade path: can switch from HTTP fetching to Playwright rendering for JavaScript-heavy pages. Crawler durability: includes an async crawler, SQLite-backed queue, incremental recrawls, ETag/Last-Modified handling, and resumable jobs. Agent-ready interface: ships with an MCP server so compatible LLM tools can operate crawls through tool calls. Best fit Use Anansi when you need a resilient research or data-extraction crawler for websites you are allowed to access, especially where pages change structure or require JavaScript rendering. It is most relevant for developers building data pipelines, monitoring workflows, competitive research dashboards, or agentic browsing systems. Quick evaluation checklist Confirm the target website permits your intended crawling use case. Start with structured data extraction before custom selectors. Enable browser rendering only where HTTP fetching is insufficient. Keep adaptive rate limiting active and respect Retry-After responses. Use the MCP server when you want an agent to orchestrate crawl tasks instead of manually scripting every step. Source notes The GitHub repository describes Anansi as a self-healing web scraper with selector repair, browser rendering fallback, Chrome-like TLS fingerprinting, Pydantic validation, incremental crawling, and an MCP server. The project is written primarily in Python and is licensed under Apache-2.0.

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Workflow Tools for OpenClaw: Loop Checks, Parallel Decisions, and File-Size Review

#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

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This Turns Any Coding Agent Into a Video Studio

#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. This premium Loot gives you a ready-to-run workflow for using HyperFrames as an agent-first video production engine. The core idea is simple: let an AI coding agent write normal HTML/CSS, wire frame-accurate animation timing, preview it locally, then render a deterministic MP4. What the source confirms HyperFrames is open source and Apache 2.0 licensed. It turns HTML, CSS, media, and seekable animations into deterministic MP4 videos. It supports local CLI preview/render flows and AI-agent skill workflows. It requires Node.js 22+ and FFmpeg. The npm package exposes the hyperframes CLI. Agent Brief: Product Launch Video Use this prompt when you want a coding agent to create a short launch clip from scratch. Agent Brief: Website-to-Video Explainer Use this when you have a landing page, docs page, or product URL and want a social-ready explanation video. Agent Brief: Data Story Clip Use this prompt for chart races, metric reveals, or launch traction videos. QA Checklist Before Rendering Copy this into your agent session before final render. Fast start When not to use it You need a no-code editor only. You do not want to install Node.js and FFmpeg. Your video depends on complex manual editing, live camera work, or a traditional timeline-first workflow. You need a fully hosted SaaS render pipeline without touching code. Source links GitHub: heygen-com/hyperframes Docs: HyperFrames introduction npm: hyperframes

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Agent Browser for OpenClaw: Ref-Based Browser Automation Candidate

#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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#automation

More from this topic

Scrape Changing Websites with Anansi Self-Healing Selectors and MCP

Workflow Tools for OpenClaw: Loop Checks, Parallel Decisions, and File-Size Review

This Turns Any Coding Agent Into a Video Studio

Agent Browser for OpenClaw: Ref-Based Browser Automation Candidate

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