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mediapipe-tasks-vision-web

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Actualizado29 de marzo de 2026 a las 20:44

Implement and debug MediaPipe Tasks Vision gesture recognition in Amy's Echo webapp. Use when changing webcam capture, landmark extraction, gesture recognizer setup, model loading, runtime confidence logic, or detection diagnostics tied to MediaPipe behavior.

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Fuente

voku

voku/AmysEcho

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Ocupaciones relacionadasSOC

Basado en la clasificación ocupacional SOC

Desarrolladores de softwareOcupaciones informáticas y matemáticas·SOC 15-1252

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SKILL.md

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name	mediapipe-tasks-vision-web
description	Implement and debug MediaPipe Tasks Vision gesture recognition in Amy's Echo webapp. Use when changing webcam capture, landmark extraction, gesture recognizer setup, model loading, runtime confidence logic, or detection diagnostics tied to MediaPipe behavior.

MediaPipe Tasks Vision Web

Use this skill when a change touches gesture detection runtime behavior in the webapp.

1) Confirm the runtime surface

Inspect these files first:

webapp/src/gesture/core/GestureDetector.ts
webapp/src/hooks/useSignLanguageDetector.ts
webapp/src/gesture/installMlp.ts
webapp/src/gesture/modelClient.ts

2) Follow a deterministic debug sequence

Reproduce with fixture-based tests before camera-based manual testing.
Verify detector init order (resource loading, model path resolution, warmup).
Verify confidence threshold and abstention logic.
Verify fallback behavior when profile model is unavailable.
Verify diagnostics output stays actionable and German for user-facing text.

3) Apply MediaPipe-specific guardrails

Keep frame processing latency predictable (avoid extra heavy sync work in hot loop).
Preserve handedness/landmark shape contracts across transforms.
When changing ROI/cropping behavior, capture before/after benchmark evidence.

4) Validate end-to-end behavior

Run focused tests for touched detection files.
Run npm --prefix webapp run diagnose:fixtures.
Run npm test --prefix webapp for regression confidence.

References

Official library docs: references/official-mediapipe-docs.md
Runtime troubleshooting flow: references/runtime-troubleshooting.md

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Execute Amy's Echo model training workflows end-to-end with reproducible evidence. Use when changing trainer code, training metadata contracts, model artifact handling, few-shot evaluation, or release-readiness checks for training outputs.

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Diagnose and improve Amy's Echo web sign-language ML detection quality. Use when working on MediaPipe landmarks, MLP inference thresholds, confidence/margin behavior, detector telemetry, fixture diagnostics, or profile-aware recognition regressions.

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testing-quality-gate

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Run Amy's Echo testing and quality gates with clear evidence reporting. Use when preparing a change for commit/PR, diagnosing regressions, or deciding the minimum safe check set for touched webapp, server, or integration areas.

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name	mediapipe-tasks-vision-web
description	Implement and debug MediaPipe Tasks Vision gesture recognition in Amy's Echo webapp. Use when changing webcam capture, landmark extraction, gesture recognizer setup, model loading, runtime confidence logic, or detection diagnostics tied to MediaPipe behavior.

MediaPipe Tasks Vision Web

Use this skill when a change touches gesture detection runtime behavior in the webapp.

1) Confirm the runtime surface

Inspect these files first:

webapp/src/gesture/core/GestureDetector.ts
webapp/src/hooks/useSignLanguageDetector.ts
webapp/src/gesture/installMlp.ts
webapp/src/gesture/modelClient.ts

2) Follow a deterministic debug sequence

Reproduce with fixture-based tests before camera-based manual testing.
Verify detector init order (resource loading, model path resolution, warmup).
Verify confidence threshold and abstention logic.
Verify fallback behavior when profile model is unavailable.
Verify diagnostics output stays actionable and German for user-facing text.

3) Apply MediaPipe-specific guardrails

Keep frame processing latency predictable (avoid extra heavy sync work in hot loop).
Preserve handedness/landmark shape contracts across transforms.
When changing ROI/cropping behavior, capture before/after benchmark evidence.

4) Validate end-to-end behavior

Run focused tests for touched detection files.
Run npm --prefix webapp run diagnose:fixtures.
Run npm test --prefix webapp for regression confidence.

References

Official library docs: references/official-mediapipe-docs.md
Runtime troubleshooting flow: references/runtime-troubleshooting.md