opus-clip

opus-clip

The clip-pipeline tool skill — choose the source, let AI find candidates, inspect and rank honestly, polish the top few, ship on a drip. The agent plans and QA-checks; the human reviews every clip; WoopSocial publishes. (Ships with tools/integrations/opus-clip.md; pairs with the captions-and-clipping craft skill.)

The POV: a first-pass clip finder, not a one-click viral machine

OpusClip genuinely compresses 3–5 hours of manual clipping into minutes of processing plus review — its ClipAnything engine is multimodal (visuals + audio sentiment + facial expressions), so it finds moments even in wordless content, and natural-language moment search pulls one moment from a 2-hour VOD without scrubbing. But the top-1% operator holds two facts the marketing won't volunteer. (1) The Virality Score is triage, not truth: the only credible independent test found ~40% of generated clips get discarded and the score regularly mispredicts real performance — low scorers blow up, high scorers flop. Use it to rank where your editing time goes; never as an auto-post threshold or a guarantee ("85% faster" is a self-published number with no method — the ~40% discard is the figure you can plan around). (2) The human gate is non-negotiable: every clip passes three questions — does it stand alone, does it represent the speaker fairly (the interview meaning-spine applies to clips), does it serve the audience — before anything ships. And the billing mechanic that catches everyone: 1 credit = 1 minute of SOURCE video, regardless of clips out — trim before you upload.

Read these first

1. captions-and-clipping — the general clipping/repurposing craft this tool executes.
2. The source skill (podcast-and-audiograms / youtube-long-form) + brand-profile/design-and-templates

(the clip template).

The framework: CLIPS

(Depth: references/the-clips-framework.md.)

• C — Choose the source: conversational long-form clips best (~8–12 candidates/hour); non-verbal genres work

via multimodal analysis with higher discard — test on Free first; clean audio, no burned-in subs, trim before upload (credits bill on source minutes).

• L — Let AI find candidates: genre + length + AI-hook configured; natural-language moment search; negative

prompting (exclude intros/sponsor reads).

• I — Inspect and rank honestly: score = triage; plan for ~40% discard; the human three-question pass —

stands alone / fair / serves; the score never overrides judgment.

• P — Polish the top few: edit only the top 3–5, approve clean mid-scorers, discard without guilt; fix the

hook's first line, caption errors, reframe drift, boundaries; one brand template across the batch; auto-music OFF → licensed/native audio at publish.

• S — Ship on a drip: batch-schedule spread across the week → WoopSocial; download exports promptly; study

real retention vs the scores monthly and recalibrate.

The reality (verify-quarterly)

2026 OpusClip: ~10M users / ~172M clips; ClipAnything (multimodal + moment search + negative prompts); speaker diarization layouts; ReframeAnything tracking; ~97% caption accuracy vendor-claimed across 20+ languages (QA anyway); AI B-roll; XML export to Premiere/Resolve (Pro). The score: 0–99 across Hook/Flow/Engagement/Trend — independently found to mispredict; ~40% discard (BIGVU test, attributed); no independent benchmarks. Credits: 1 = 1 source minute; +1 per direct X post; failures reported eating credits; projects reported vanishing after subscription ends — download promptly. Tiers (≈, conflict — verify in-app): Free = evaluation only (watermark, 3-day expiry, no score/editor); Starter ≈ $15/150 min; Pro ≈ $29 (~$14.50 annual)/300 min = the production tier; Business = custom, API reported Business-only; editor gating conflicts across sources. Reliability: reported processing hangs, no public status page/SLA — build slack. Trustpilot 4.0 with ~22% one-star (attribute). Full detail: references/opus-clip-2026-reality.md. The weekly loop, per-clip QA, credit-math worksheet, score recalibration, and two worked examples: references/workflows-and-templates.md.

Honest scope (never violate)

• The agent plans (pipeline, prompts, triage, QA, credit math, drip) and drafts fixes; the **human reviews

every clip and approves (the agent can't see video; never fabricates "that clip works"). Automation honesty: API reported Business-only; Zapier/Make exist lower; no unreviewed auto-post pipeline on any tier. WoopSocial publishes approved exports (its scheduler-vs-OpusClip's is a stated choice); it does not** clip/edit/score video; native audio/stickers are added in-app by the human.

• The score spine: a prediction — triage, never truth, never quoted as a metric. The fairness spine:

clips keep context and meaning; no manufactured endorsements or gotchas (deception/defamation); consent/ likeness rules apply. Never fabricate tiers, gates, capabilities, or metrics — verify in-app; build slack for the reliability reality. (Full scope: references/scope-and-connections.md.)

Distinct from its siblings (route correctly)

opus-clip (this) = the OpusClip-specific pipeline · captions-and-clipping = the general clipping craft (read first; tools/integrations/clipping.md covers the multi-tool layer — tools/integrations/opus-clip.md goes deep on this tool) · descript = the long-form talk edit (master there; its Underlord clip flags overlap) · capcut = short-form styling/polish beyond the built-in editor · short-form-video-script = the retention craft the QA applies · tiktok-video-publishing / instagram-reels-publishing / youtube-shorts = platform publish specifics.

Where this connects

Reads first: captions-and-clipping + the source skill + brand-profile/design-and-templates. Pulls the master from: descript (the edited episode), livestream-and-realtime (VODs), webinars. Feeds: capcut (polish), the platform publishing skills, content-recycling, scheduling-and-queue. Publishes via: approved exports → scheduling-and-queue → WoopSocial. Tool file: `tools/integrations/opus-clip.md`. Measure with: native + analytics-and-reporting on per-clip retention vs score (recalibrate monthly) — never fabricated.

Definition of done

A clip pipeline that treats OpusClip as a first-pass finder: source chosen and trimmed for the credit mechanic (1 credit = 1 source minute), candidates generated with genre/negative prompts set, the batch triaged by score as ranking only with a planned ~40% discard, every clip passed through the human three-question gate (stands alone / fair to the speaker with context intact / serves the audience), only the top 3–5 edited (hook line, caption accuracy on names/jargon, reframe drift, boundaries) with one brand template across the batch and auto-music off in favor of licensed/native audio, the approved set drip-scheduled and published via WoopSocial with exports downloaded promptly, and real retention tracked against the scores monthly to recalibrate; tier/editor/API gating verified in-app before subscribing (API automation claimed only at the reported Business tier; no unreviewed auto-posting anywhere); no score treated as truth, no out-of-context clips, no fabricated tiers/capabilities/ metrics; and correctly distinguished from captions-and-clipping, descript, capcut, and the platform publishing skills.