MMinaraiVideo-to-skill workspace
日本語

Choose source video

Video 1 · Workshop filingOpen · 8:04Video 5 · Kyoto engraverSelected · 5:40Video 6 · Dayel grinderOpen · 1:52Video 7 · DONGTAI compact grinderOpen · 2:45Video 8 · Titans ID wheelOpen · 0:59

Open questions · Human–AI collaboration

Return what the video could not tell us to the skill graph.

The AI formulates questions from the gap between what the source supports and what a learner still needs. An expert answer becomes a provenance-bearing graph node and updates the related process context.

Progress

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01 · QUESTION FORMULATION

How an open question is formulated

The system does not plausibly fill gaps. It fixes the source-supported boundary, then asks only for learning-critical fields that remain unstated.

  1. 01
    Perceive video and speech

    Ground speech, on-screen text, entities, and motion in time

  2. 02
    Structure the graph

    Separate process, action, attention, and criteria

  3. 03
    Detect a missing field

    Find absent criteria, values, exceptions, rationale, or identity

  4. 04
    Formulate a neutral question

    Avoid leading the answer and retain source links

  5. 05
    Apply the human answer

    Add accepted evidence and mark the question resolved

02 · OPEN QUESTIONS

Open questions in the skill graph

Select a question to inspect why it was generated, its evidence, and the exact graph patch an answer will create.

Applicability boundary or exception

How do material and stock allowance affect the choice of coarse, medium or fine cut?

Open
Why it was generated

A baseline method is shown, but the conditions that change it across materials, geometry, or state are unstated.

What the source establishes

Choose a file for the area — Files differ in size, coarse/medium/fine cut, and profiles such as flat, half-round, triangular and square. The practitioner matches the shape to the area and identifies precision files for fine locations.

Learning-critical field missing from the source

Applicability boundary or exception

Evidence IDs
v5-speech-shape
0:27

Expert answer

03 · GRAPH UPDATE

Live graph patch

Applying an answer immediately changes the question state and adds a human-verified evidence node.

LEARNING UNITChoose a file for the areaRelated learning unit
RAISES_QUESTION
OPENQUESTIONHow do material and stock allowance affect the choice of coarse, medium or fine cut?Awaiting an answer
SUPPORTED_BY
EVIDENCEITEMAwaiting an answerHuman-verified evidence

04 · SOURCE CHECKS

Supporting source checks

Transcript, OCR, and neutral-ID checks are a separate provenance gate—not answers to the skill questions. They remain available below.

Progress0 / 3

1. Audio spot checks

Listen and confirm whether the displayed source-language text matches the recording.

Audio 1

「知ってもらえたらいいかなと思いますので」

segment 16 · 56.42s

Audio 2

「もう少し削ってみますね」

segment 40 · 171.66s

Audio 3

「スリータって僕ら言うんです」

segment 64 · 266.18s

The result is bound to this video hash

4883d39c1c44bcf7d8f82448bef1a32a745de194b2622549b0d005985bdf4eab

The mechanical gate remains pending until the JSON is imported with apply-human-review. Formalizing the SOP requires a separate practitioner review of every instruction.

Continue with this source

One source, five connected views

Switching views preserves the same source hash across instruction, analysis, skill structure, evidence, and human review.

01Learning guideLearn the demonstration as a visual procedure02Vision labInspect model outputs, live graph, and teaching visuals03Skill mapExplore process, tools, judgment, and learning04EvidenceReturn to speech, source text, and observations
05Open questionsAnswer knowledge gaps and update the skill graphYou are here