| name | hydro-embodied-verify |
| description | Score a first-person (egocentric) task video against its instructions with a vision model and return a bounded {approved, score, reason} verdict. Use to gate crowdsourced embodied-data uploads or to evaluate an embodied-video deliverable. |
Hydro Embodied-Data Verifier
Judge whether a short first-person video genuinely performs a stated real-world
task (e.g. "pick up the cup, pour water, place it back"), and return a single
bounded verdict. This is the quality gate behind Hydro's embodied-data
marketplace, packaged so any agent can reuse it — including as the evaluator for
an embodied-video deliverable on the Agent Commerce Protocol (ACP).
When to use this skill
- You need to decide if a demonstration clip actually completes the task it
claims, before paying or crediting the contributor.
- You are named as the evaluator on an ACP job whose deliverable is a
first-person task video, and you must accept or reject it.
- You want a consistent, spam-resistant quality score for embodied-video data.
When NOT to use this skill
- The deliverable is not a task video (text, code, on-chain action) — use a
matching evaluator instead.
- You need to custody funds or move tokens. This skill only produces a verdict;
settlement is handled by the caller / ACP escrow.
- You need identity, personal data, or biometric analysis of the person in the
clip. Out of scope and not permitted.
Inputs
video_url — a readable URL (or local path) to the clip to judge.task_instructions — the exact task the clip is supposed to perform.threshold (optional, default 55) — minimum 0-100 score required to approve.
Tools, credentials, preconditions
- A vision-capable model API key (e.g. an OpenAI
gpt-4o-mini vision key) set in
the environment as OPENAI_API_KEY. No other credentials are required.
- Frame extraction: 3 evenly-spaced frames are sampled from the clip and passed
to the model with the task instructions. No full-video upload is needed.
- The model is asked to return strict JSON:
{ "approved": bool, "score": int, "reason": string }.
Approval gates
- This skill performs no spending, posting, account creation, deployment, or
production mutation. It only reads a video and returns a verdict.
- If used inside an ACP job to release or refund escrow, the escrow action is a
separate step owned by the caller and requires explicit authorization of the
job id and settlement rail before it runs.
Stop conditions and handoff
- Stop and return
needs_review if frames cannot be extracted, the model output
is not valid JSON, or the model expresses low confidence. Do not fabricate a
pass.
- If the task instructions are missing or empty, stop and ask for them rather
than guessing.
Procedure
- Extract 3 representative frames from
video_url.
- Prompt the vision model with the frames +
task_instructions, asking it to
judge task completion and quality against the rubric in soul.md.
- Parse the strict-JSON response into
{approved, score, reason}.
- Apply the decision rule below.
Validation checks
- Response must parse as JSON with
approved (bool), score (int 0-100), and a
non-empty reason string; otherwise → needs_review.
score is clamped to 0-100.approved is only honored when score >= threshold; a high-score reject or a
low-score approve is downgraded to needs_review.
Output contract
Return exactly:
{
"approved": true,
"score": 82,
"reason": "Hands pick up the cup, pour water, and set it back; stable framing, clear lighting.",
"verdict": "approved"
}
verdict is one of approved | rejected | needs_review.- On any failure to judge confidently, return
verdict: "needs_review" with
approved: false and a reason explaining what was missing.
- Never include API keys, wallet material, or raw contributor identity in the
output.
