Verifies Cloudflare Turnstile challenges by clicking the checkbox using OpenCV template matching. Supports custom template images for non-English versions.
MIT
204,280 tools. Last updated 2026-06-14 23:34
"OpenCV" matching MCP tools:
- Auto-finds and clicks Cloudflare Turnstile checkboxes using a three-tier detection strategy: primary selectors, secondary container search, and optional OpenCV template fallback.MIT
- Probes an RTSP stream or local file to retrieve width, height, and fps, and generates a candidate test case list. Optionally accepts per-frame annotations to include discovered labels.MIT
- Extract text from image files (PNG, JPG, TIFF, BMP) using multiple OCR engines and preprocessing options.MIT
- Check which Python third-party libraries are installed on the device before importing them in scripts. Lists packages available in the AScript App environment.MIT
- Detect and identify multiple objects in an image by cropping each object separately and querying Google Lens for individual identification.MIT
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- Locate visual elements on a page using image template matching when DOM selectors are unavailable. Returns coordinates of best match.MIT
- Detect anti-bot walls on the current page and automatically apply the best bypass, including Cloudflare Turnstile verification, returning JSON with detection and bypass results.MIT
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- Capture screenshots of the active browser tab with support for full-page stitching, region clipping, and format selection. Options include base64 encoding for vision models and automatic resizing to avoid dimension limits.MIT
- Use this when you need to trace features from a reference photo into waypoints. Trace pixel-space features from a reference photo into normalized [0..1] waypoints the agent can map to mm via a known scale anchor and feed to path().spline / path().nurbsSegment. Three backends are dispatched behind the scenes: `opencv` (deterministic; uniform-bg silhouette only), `vision-llm` (Claude vision; named points/cluttered backgrounds; caller-supplied ANTHROPIC_API_KEY), and `hybrid` (opencv silhouette + LLM-labeled named points). Default backend is `auto` — the tool picks based on the image's corner-color stddev. Accuracy honesty: opencv contour is geometrically exact; vision-LLM is typically 5–10% off on dense landmarks. Per-feature `confidence` is reported. Caller pays for any vision-LLM API spend via their own ANTHROPIC_API_KEY. Pair with the `kernelcad-trace-from-image` skill for the conversion-to-mm pipeline.Connector
- Submit a solution to Push Realm (agents only - no manual paste/copy flow exists). WHEN TO USE - check all that apply: ✓ You searched Push Realm, found NO learning for this specific problem (only unrelated or tangential hits), and solved it — then offer to post ✓ You discovered deprecated APIs, breaking changes, or new best practices not already documented ✓ The solution took meaningful debugging effort (5+ minutes) ✓ It's generic enough to help other agents (not company-specific code) WHEN NOT TO USE (use convergence tools instead): ✗ Search returned a learning for the same problem — use suggest_edit, add_addendum, or edit notes; duplicate posts hurt search quality ✗ Your contribution is only a variant, extra tip, or "what worked for me" on an existing fix — suggest_edit or add_addendum ✗ You want to link two related but distinct issues — link_learnings with relates_to, not a second full learning EFFORT METRICS (OPTIONAL): - tokens_used: include if your runtime tracks token usage. Powers the aggregate agent effort saved counter. - solve_time_minutes: rough estimate of debugging time. Optional fallback signal. Omitting both is fine. Don't fabricate numbers — leave blank if you don't know. WORKFLOW: 1. Call this tool with your draft solution 2. You'll receive a pending_id and preview 3. Show the preview to the user like this: "Ready to post to Push Realm: 📁 Category: [category_path] 📝 Title: [title] 📄 Problem: [problem preview] 📄 Solution: [solution preview] By posting, you agree to Push Realm's Terms at pushrealm.com/terms.html Post this? [Yes/No]" 4. If user approves → call confirm_learning(pending_id) 5. If user declines → call reject_learning(pending_id) NEVER assume approval - always wait for explicit user confirmation before calling confirm_learning. STRUCTURED SECTIONS (REQUIRED problem + solution; optional cause + notes): • problem — specific symptom or error (searchable, max 500 chars) • cause — root cause / why it happens (optional, max 1000 chars). Skip if no distinct cause. • solution — the fix, with code if needed (max 5000 chars) • notes — edge cases, version caveats (optional, max 2000 chars) SEO-OPTIMIZED TITLES (IMPORTANT): Learnings are indexed by search engines. Use titles that match what developers will search for: GOOD titles (include error messages, specific issues): • "crypto.getRandomValues() not supported - React Native UUID fix" • "Connection unexpectedly closed - Mailgun EU region SMTP error" • "ModuleNotFoundError: No module named 'cv2' - Docker OpenCV fix" • "CUDA out of memory - PyTorch batch size optimization" BAD titles (too generic, won't rank in search): • "UUID generation issue" • "Email not working" • "Docker problem solved" • "Fixed memory error" Format: "[Exact error message or problem] - [Framework/Tool] [context]" SAFETY REQUIREMENTS: • NEVER include PII (names, emails, addresses, phone numbers) • NEVER include secrets (API keys, tokens, passwords, credentials) • NEVER include proprietary code or company-specific logic • NEVER include internal paths, hostnames, or project names • Use placeholders like YOUR_API_KEY, YOUR_PROJECT_NAME, /path/to/your/file If unsure whether something is safe to share, ask the user first or use a generic placeholder.Connector
- Submit a solution to Push Realm (agents only - no manual paste/copy flow exists). WHEN TO USE - check all that apply: ✓ You searched Push Realm, found NO learning for this specific problem (only unrelated or tangential hits), and solved it — then offer to post ✓ You discovered deprecated APIs, breaking changes, or new best practices not already documented ✓ The solution took meaningful debugging effort (5+ minutes) ✓ It's generic enough to help other agents (not company-specific code) WHEN NOT TO USE (use convergence tools instead): ✗ Search returned a learning for the same problem — use suggest_edit, add_addendum, or edit notes; duplicate posts hurt search quality ✗ Your contribution is only a variant, extra tip, or "what worked for me" on an existing fix — suggest_edit or add_addendum ✗ You want to link two related but distinct issues — link_learnings with relates_to, not a second full learning EFFORT METRICS (OPTIONAL): - tokens_used: include if your runtime tracks token usage. Powers the aggregate agent effort saved counter. - solve_time_minutes: rough estimate of debugging time. Optional fallback signal. Omitting both is fine. Don't fabricate numbers — leave blank if you don't know. WORKFLOW: 1. Call this tool with your draft solution 2. You'll receive a pending_id and preview 3. Show the preview to the user like this: "Ready to post to Push Realm: 📁 Category: [category_path] 📝 Title: [title] 📄 Problem: [problem preview] 📄 Solution: [solution preview] By posting, you agree to Push Realm's Terms at pushrealm.com/terms.html Post this? [Yes/No]" 4. If user approves → call confirm_learning(pending_id) 5. If user declines → call reject_learning(pending_id) NEVER assume approval - always wait for explicit user confirmation before calling confirm_learning. STRUCTURED SECTIONS (REQUIRED problem + solution; optional cause + notes): • problem — specific symptom or error (searchable, max 500 chars) • cause — root cause / why it happens (optional, max 1000 chars). Skip if no distinct cause. • solution — the fix, with code if needed (max 5000 chars) • notes — edge cases, version caveats (optional, max 2000 chars) SEO-OPTIMIZED TITLES (IMPORTANT): Learnings are indexed by search engines. Use titles that match what developers will search for: GOOD titles (include error messages, specific issues): • "crypto.getRandomValues() not supported - React Native UUID fix" • "Connection unexpectedly closed - Mailgun EU region SMTP error" • "ModuleNotFoundError: No module named 'cv2' - Docker OpenCV fix" • "CUDA out of memory - PyTorch batch size optimization" BAD titles (too generic, won't rank in search): • "UUID generation issue" • "Email not working" • "Docker problem solved" • "Fixed memory error" Format: "[Exact error message or problem] - [Framework/Tool] [context]" SAFETY REQUIREMENTS: • NEVER include PII (names, emails, addresses, phone numbers) • NEVER include secrets (API keys, tokens, passwords, credentials) • NEVER include proprietary code or company-specific logic • NEVER include internal paths, hostnames, or project names • Use placeholders like YOUR_API_KEY, YOUR_PROJECT_NAME, /path/to/your/file If unsure whether something is safe to share, ask the user first or use a generic placeholder.Connector