Video Frame Digesting & Temporal Memory Skill (video-ingest)
SkillDocs & knowledgeTeaches the agent to process, ingest, analyze, and compare WebM, MP4, and GIF video recordings using vision-memory-mcp and state-memory-mcp.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Video Frame Digesting & Temporal Memory Skill (video-ingest) skill
What this skill tells your AI
The instructions your AI receives, as published by putervision/world-model-mcp in .agents/skills/video-ingest/SKILL.md and read by ahel’s review.
This skill provides step-by-step guidance, best practices, and operational patterns for digesting WebM, MP4, and GIF video recordings using @putervision/vision-memory-mcp.
1. When to Use Video Ingestion
Use video ingestion whenever you encounter:
- E2E Playwright / Cypress / Selenium Test Artifacts: Recorded
.webmscreenchunks or.mp4test run videos. - Bug Reproduction Videos: User-uploaded screen recordings demonstrating UI glitches or crashes.
- UI Walkthrough Recordings: Demonstrations of complex multi-step user workflows.
- Visual Regression Diagnostics: Comparing a passing baseline video run against a failing test run.
2. Ingestion Strategies & Parameter Tuning
| Scenario | Recommended Parameters | Why |
|---|---|---|
| Action Event Timestamps (Highest Precision) | action_timestamps: [1.2, 3.5, 7.0] | Samples keyframes at exact interaction timestamps (clicks, types, navigation events) from test runners or state-memory logs. |
| Dynamic UI / Animations | scene_threshold: 0.3, fps: 1 | Combines scene-change detection (gt(scene,0.3)) with 1 fps background sampling to capture major screen transitions without frame bloat. |
| High-Speed Test Runs | fps: 2 or fps: 5 | Increases frame rate sampling for rapidly switching UI test steps. |
| Long Screen Recordings | fps: 0.5, scene_threshold: 0.4 | Lowers sampling rate to conserve storage while extracting unique keyframe states. |
3. Mandatory Dual-MCP Evidence Workflow
When diagnosing bugs or linking test runs to task nodes:
- Ingest Video: Call
manage_video(action: "ingest") with file path and action timestamps. - Extract Evidence Payload: Read the returned
evidence_payloadcontainingsource_video_id,frame_range, andtimestamps_ms. - Build Evidence Pack: Call
create_evidence_packlinkingkeyframe_state_idswithstate-memory-mcptask or blocker node IDs. - Compare Trajectories (On Failure): Call
compare_states(video_a_id: "...", video_b_id: "...")to pinpoint exact frame divergence points.
4. CLI Quick Reference
# Ingest WebM / MP4 video recording with custom category
vision-memory-mcp video ingest ./recording.webm --category e2e_test
# Ingest with explicit action timestamps
vision-memory-mcp video ingest ./bug.mp4 --category bug_repro --action-timestamps 1.2,3.5,8.0
# Inspect chronological timeline & keyframes for a video ID
vision-memory-mcp video inspect vid_d7fc21c0ad52f861
# List all ingested video memory records
vision-memory-mcp video list
5. Consolidated Video MCP Tools Reference
manage_video(action: "ingest"): Ingests video file/base64, extracts keyframes, dHash deduplicates, and generates CLIP vector embeddings.manage_video(action: "timeline"): Fetches step-by-step keyframes, exact timestamps (timestamp_ms), OCR snippets, and grounded target handles.manage_video(action: "search"): Searches video memory by description query, category, tags, or file path.compare_states(video_a_id,video_b_id): Calculates similarity score between two recordings and pinpoints exact timestamp divergence.create_evidence_pack: Produces an immutable, cryptographically hashable evidence pack payload linking keyframes to task graph nodes.
Signals
- GitHub stars
- 16
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
video-ingest-putervision- Source
- github.com/putervision/world-model-mcp