Identify the source of an image or video frame — TV show episode, movie scene, geographic location, or original publication. This skill should be used when the user asks to identify where an image is from, trace a screenshot back to its source, geolocate a photo, find what show or movie a frame is from, or do a reverse image search. Chains Google Vision, Picarta geolocation, and Gemini in parallel with graceful degradation. Triggers on: reverse image search, identify source, what show is this, where was this taken, trace image, identify video, what movie, which episode, geolocate photo, image source.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Identify the source of images and videos by running multiple reverse search APIs in parallel and synthesizing results into a confidence-ranked report. Each engine contributes a different signal — web entity matching, AI geolocation, multimodal LLM identification — and the orchestrator merges them because no single API reliably covers all identification scenarios.
At least one API credential must be set. Missing keys cause the orchestrator to skip that engine, not crash.
| Engine | Env Var | Free Tier | Signal |
|---|---|---|---|
| Google Vision | GOOGLE_APPLICATION_CREDENTIALS or ADC | 1K/mo | Web entities, matching pages, similar images, best-guess labels |
| Picarta (geospy) | PICARTA_API_KEY or GEOSPY_API_KEY | Yes | Lat/lng, city, country, confidence score |
| Gemini | GOOGLE_API_KEY or GEMINI_API_KEY | Yes | Media type, title, season/episode, characters, actors |
To set up Vision ADC: gcloud auth application-default login
Run rt_trace.py to execute all available engines in parallel. For video input, keyframes are extracted first via ffmpeg.
python3 scripts/rt_trace.py image.jpg
python3 scripts/rt_trace.py video.mp4 --max-frames 3
python3 scripts/rt_trace.py image.jpg --json
python3 scripts/rt_trace.py image.jpg --skip geospy
python3 scripts/rt_trace.py image.jpg --engines vision gemini
Use a single engine when only one type of identification is needed, to conserve API quota, or to debug a specific engine's output.
python3 scripts/rt_vision.py image.jpg # Web entities + matching pages
python3 scripts/rt_geospy.py photo.jpg --top-k 3 # AI geolocation
python3 scripts/rt_gemini.py frame.jpg # LLM media identification
python3 scripts/rt_extract.py video.mp4 --keyframes # Frame extraction only
| Goal | Engines to use | Why |
|---|---|---|
| Identify TV show / movie | gemini + vision | Gemini recognizes characters from training data; Vision finds matching web pages that name the episode |
| Find where an image was published | vision | Web Detection returns pages hosting the image with titles and URLs |
| Geolocate a photo | geospy | Picarta AI geolocation from visual cues (architecture, vegetation, signage) |
| Full automated identification | rt_trace.py (all) | Parallel execution, merged synthesis, confidence ranking |
Default output is a human-readable report with sections: BEST GUESS, MEDIA IDENTIFICATION, GEOLOCATION, WEB ENTITIES, MATCHING PAGES, ENGINE STATUS. Add --json for structured JSON suitable for piping or programmatic consumption.
The orchestrator uses a single ENGINE_REGISTRY dict. To add an engine:
scripts/rt_<name>.py following the existing pattern (argparse CLI, --json flag, env var auth, engine field in JSON output)ENGINE_REGISTRY in rt_trace.py with script and env keysPlanned Phase 2 engines (paid APIs): SerpAPI Google Lens, TinEye, Lenso.ai, Yandex. See references/expansion-roadmap.md for API details, pricing, and implementation notes.
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Search academic papers, build literature reviews, and synthesize research findings — combines Exa MCP (research_paper category, arxiv filtering) with arxiv-mcp-server for paper discovery, download, and deep analysis. Triggers on academic paper, literature review, research synthesis, arxiv, find papers, scholarly search.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when creating, auditing, or maintaining AGENTS.md files and Codex CLI configuration for any project—including initializing AGENTS.md for cross-agent compatibility (Codex, Cursor, Copilot, Devin, Jules, Amp, Gemini CLI), generating config.toml or .rules files, scaffolding .agents/skills/, converting CLAUDE.md to AGENTS.md, or auditing existing agent configs for bloat and staleness. Complementary to codex-orchestrator (which executes subagents; this skill creates the config files they consume).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Academic research skill for Biblical Hebrew, Semitic linguistics, cuneiform studies, and comparative Ancient Near Eastern research. Provides Sefaria API for Hebrew Bible, CDLI/ORACC for cuneiform databases, and web discovery via Omnisearch, Exa, Firecrawl, and Obscura for finding scholarly sources across JSTOR, Perseus, Persée, Google Scholar, and academia.edu. Triggers on Hebrew quotes, cuneiform, Sefaria, ANE research, Minoan, search for scholarship, find papers, literature review, scholarly search, academic search, Genesis/Tehom, Ugaritic, Talmudic sources, extract from PDF, OCR academic.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build comprehensive ARCHITECTURE.md files following matklad's canonical guidelines — bird's-eye views, ASCII/Mermaid diagrams, codemaps, invariants, and layer boundaries. Triggers on document the architecture, create ARCHITECTURE.md, map this codebase, architectural overview.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate physically-based atmospheric scattering shaders — sky domes, planetary atmospheres, LUT-optimized pipelines, depth-aware post-processing. Four modes — sky-dome, atmosphere-post, planet, lut. Triggers on atmospheric scattering, sky shader, sunset rendering, planet atmosphere, Rayleigh scattering, Mie scattering, volumetric sky, sky dome, atmosphere post-processing, aerial perspective, transmittance LUT, sky rendering, realistic sky, planetary rendering.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.