Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
Generated-checked block (scripts/build_index.py verifies anchors). Read the linked sections for full context — these lines are routing aids, not the rules themselves.
Face inconsistency, dead camera moves, ignored prompts, static i2v, blocked dark content — the per-problem fix list →
Kling 3.0 Motion Control failures are almost always upstream of the prompt: reference clip, character image, or orientation/scene-source settings →
Pre-generation checklist: subject, action, named camera preset, style, grade, aspect, <200 words (short-form regime) →
Seedance/Cinema Studio symptom table + diagnostic flowchart: blurry = overspecified; chaotic camera = One-Move Rule violated; wrong character = prompt re-describes the reference →
Every delivered take gets ONE of five verdicts before anything re-fires: keep / fix-in-post / edit / re-roll / rewrite →
Two takes with the same flaw = rewrite, by rule; different flaws per roll = stochastic → batch-and-cull, not rewrite →
Re-roll = same prompt again, unchanged — no seed parameter on this surface; every roll is a fresh sample →
Change exactly one variable between takes so causality stays readable →
Declare the take budget AND a written "good enough" bar before take one; half-budget with no progress forces a strategy change →
The shot log is the ledger row — one line per take, changed variable in notes→
Retry Ladder: 4 terminating rungs — re-run once verbatim → treat 2nd failure as over-packing → switch model for that shot → stop after 3 paid attempts with named options →
Log EVERY confirmed fix to learning memory, and check memory first before troubleshooting →
Vision-grounded diagnosis (stills only): vision proposes the reject_reason, the human confirms — advisory until a class clears the agreement gate →
Common Problems & Fixes
Problem: Character face is inconsistent or morphing
Cause: No Soul ID reference; prompt has conflicting appearance descriptions
Fix:
Create a Soul ID reference and use it in subsequent generations
Remove any appearance descriptions that contradict each other
For image-to-video: don't re-describe the face — let the input image carry it
Use Kling 3.0 for best character consistency (or Kling 2.6 if no audio needed)
Problem: Camera movement isn't working / is generic
Cause: Camera described vaguely, not using exact preset names
Fix:
Put the camera instruction on its own line or clearly labeled: "Camera: [name]"
Don't describe what the camera is doing in prose — name the control directly
Problem: Prompt is ignored / output doesn't match
Cause: Prompt too long, conflicting instructions, over-specified
Fix:
Cut prompt to under 200 words — trim the least essential details (short-form prompts only; block-scaffold production briefs are a different regime — root SKILL.md HARD RULE 8)
Remove any contradictory elements (don't say both "moving fast" and "frozen in place")
Lead with the most important element: Subject → Action → Camera → Style
Split complex scenes into multiple separate generations
Problem: Visual style looks wrong / generic
Cause: No style specified, or style description too vague
Fix:
Add one of the named styles: Cinematic / VHS / Super 8MM / Anamorphic / Abstract
Add a specific color grade description: "cold teal and orange", "warm golden amber"
Add specific lighting: "soft side-light", "overhead product lighting", "practical only"
Add texture cues: "camera reveals material grain", "surface catches light on edges"
Use Nano Banana Pro for maximum image sharpness on product images
Problem: Horror/dark content getting blocked
Cause: Platform safety filters triggering on explicit content
Fix:
Describe outcomes rather than explicit acts: "aftermath", "tension", "dread"
Use atmosphere language: "unsettling", "wrong", "something is off"
Use the motion presets for horror effects rather than explicit descriptions
Avoid direct descriptions of injury, gore, or explicit threat
Motion Control Failures (Kling 3.0)
When a Kling 3.0 Motion Control generation comes back wrong, the cause is almost
always upstream of the prompt — the motion reference clip, the character image,
or the orientation/scene-source settings. Walk this list before you regenerate.
Symptom
Root cause
Fix
Output suddenly jumps or snaps mid-clip
The motion reference contains a hidden cut, dissolve, or hard transition
Re-trim the reference to a single continuous shot. If the source clip can't be cleaned up, reshoot or pick a different reference
Output is shorter than the reference clip
The source motion is too fast or too dense for clean transfer
Slow the source (50–75% playback baked in), reshoot at a calmer pace, or pick a reference with simpler motion
Character face drifts or warps across the clip
The character image doesn't have a clearly readable face — bad framing, low light, or the face is too small in frame
Re-shoot or re-generate the character image with closer framing, even lighting, and a neutral or slight expression
Body motion looks correct but the face is dead or frozen
Wrong orientation mode for the shot — Image Orientation when you needed Video Orientation, or vice versa
Switch modes: Video Orientation for full-body movement (dance, action); Image Orientation for camera-driven shots with a mostly static body. Regenerate
Generated character feels detached from the environment
Scene source is set incorrectly — pulling the wrong background
Decide whether the environment should come from the motion video or the character image, then set Scene source accordingly
Motion transfers but identity drifts across the clip
The character image isn't full enough — head or body is cut off, or framing is too tight to anchor identity
Re-upload a character image that shows both head AND body fully; this is what Element Binding needs to keep the face stable through movement
For the full Motion Control workflow and pre-flight input checklist, see ../higgsfield-motion/SKILL.md → "Kling 3.0 Motion Control — When and How to Run It" and "Motion Reference Input Checklist".
Problem: Audio/lip-sync not working or out of sync
Cause: Head motion tokens competing with lip engine, non-MP3 format, clip too long
Fix:
Remove all head/face motion tokens (nodding, turning head, looking around)
Use MP3 format only for Seedance 2.0 (when available) (WAV/AAC fail silently)
Lock camera: medium close-up, static or slow Dolly In only
One face per shot — multiple faces break audio routing
For detailed audio guidance → higgsfield-audio skill
Problem: Background music overrides uploaded dialogue
Cause: Ambient/music tokens in prompt invite generative audio engine to replace your audio
Fix:
Add timestamp anchoring: "Audio @Audio1 plays exactly as uploaded from 0s to end"
Remove ALL ambient/SFX/music tokens from the prompt
Keep the prompt focused on visual description + dialogue only
Pre-Generation Checklist
Before generating, verify:
Subject described clearly (who/what)
Action described specifically (what happens)
Camera named with exact preset name
Visual style specified (Cinematic / VHS / etc.)
Color grade or lighting mentioned
Aspect ratio included
Model selected (or let Higgsfield default)
Prompt is under 200 words (short-form regime — skip for block-scaffold briefs)
No conflicting instructions
Soul ID referenced if character consistency needed
Motion preset named at end if using one
Identity Block separated from Motion Block (if Soul ID active)
Full negative constraints reference: For a comprehensive, categorized list of all
generation artifacts and the prompt phrasing to prevent them, see
../shared/negative-constraints.md. This troubleshooting guide covers diagnosis and fixes;
the shared constraints file covers prevention.
Cinema Studio 3.0 / Seedance 2.0 Diagnostic Tree
These diagnostics apply to Cinema Studio 3.0's generation engine (Business/Team plan only). For Cinema Studio 2.5 issues, see the general troubleshooting section above.
Quick Diagnostic
Symptom
Likely Cause
Fix
Output blurry, jittery, or morphing
Overspecification — prompt too long or too detailed
Short-form: cut to 30–100 words; use @reference images/videos instead of 50+ words of description. Block-scaffold briefs: don't shorten — tighten structure instead (one axis per clause, HARD RULE 8 regime)
Camera chaotic, spinning, or jittering
Violated the One-Move Rule — multiple camera moves in one shot
Rewrite to ONE primary camera move per shot. Use Cinema Studio 3.0's Smart mode, or split into multi-shot
Character doesn't match reference
Prompt is re-describing the character's appearance
Delete ALL physical descriptions. Describe ONLY action and emotion. The @reference carries identity
Run Anti-Slop Check: replace beautiful, stunning, epic, amazing, dynamic with observable, measurable details
Audio not matching video
Audio description conflicting with visual description, or uploaded audio being overridden
Use timestamp anchoring for uploaded audio. Remove ambient/SFX tokens when using @Audio references
Diagnostic Flowchart
Output bad?
├── Blurry/morphing → Is it a short-form prompt > 100 words?
│ ├── Yes → Cut to 30–100 words, use @reference
│ │ (block-scaffold briefs: tighten structure, never truncate)
│ └── No → Too many action beats? (>2 per 5s) → Split into multi-shot
├── Camera wrong → How many camera moves specified?
│ ├── Multiple → Reduce to ONE move (One-Move Rule)
│ └── One → Try Smart mode instead, or use @Video camera transfer
├── Character wrong → Does prompt describe character appearance?
│ ├── Yes → Delete appearance, keep only action/emotion
│ └── No → Use better reference (frontal + 3/4 + profile shots)
├── Action weak → Does prompt have physics language?
│ ├── No → Add degree adverbs + physical consequences
│ └── Yes → Reduce beat density (1–2 beats per 5s)
└── Just bad → Run Anti-Slop Check
├── Found slop words → Replace with specific observables
└── Clean → Try different genre setting, or use @reference
Success Rate Note
Cinema Studio 3.0's generation engine produces ~90% usable output. If outputs are consistently bad across multiple attempts, the prompt is almost certainly the problem — not the model. Apply the diagnostic tree systematically before regenerating.
Take Triage — Five Verdicts for a Delivered Take
[FIELD — community, Emily2040/seedance-2.0 skill (MIT), re-derived 2026-08-09]
The sections above repair outright failure. Most real takes land in between —
partially good — and the expensive habit is treating every flaw as a
regeneration. Before anything re-fires, every delivered take gets exactly one
of five verdicts:
Verdict
When
Next move
Keep
The thing this shot is FOR is delivered and nothing is fatal
Lock it, log it, move on. Perfection in secondary details is post's job
Fix in post
The flaw lives in the editor's domain: color, on-screen text, sound mix, trim, a few unstable frames at the ends
Never burn takes on what an edit fixes in minutes
Edit, don't regenerate
Composition and timing are right; exactly one layer is wrong and an edit surface supports it
Repair only the failing layer — the editor-not-regenerator mindset (../higgsfield-seedance/SKILL.md § Keyframe Workflow; ../higgsfield-pipeline/SKILL.md Pipeline E Stage 2)
Re-roll
The prompt is right; the sample was unlucky
Same prompt again, unchanged — every roll is fresh on this surface (no seed parameter; ../higgsfield-seedance/SKILL.md § Drafts Validate the Prompt, Not the Take). With enough ledger history, let the fork verdict decide iterate-vs-batch instead of eyeballing (higgsfield-recall § Read the verdict)
Rewrite
The same flaw appears in two takes
Systematic, not luck — two takes with the same flaw = rewrite, by rule. Diagnose (tables above; ../higgsfield-seedance/FAILURE-MODES.md), change the prompt
The rewrite tripwire cuts both ways: the same flaw twice means stop re-rolling
into the same wall, but different flaws on every roll mean the miss is
stochastic — that's batch-and-cull territory, not a rewrite
(../higgsfield-prompt/SKILL.md § Before You Iterate). When the verdict is
re-roll or rewrite and the failure keeps recurring, escalation is governed by
the Retry Ladder below.
One variable per retake
Whatever the verdict changes — one prompt clause, OR the mode, OR one
reference — change exactly one thing between takes so causality stays
readable. Full mechanics: ../higgsfield-prompt/SKILL.md § The Iteration
Rule — Change One Variable at a Time (and DISCIPLINE.md § Single-Variable
Iteration). The shot log below records which variable, per take.
Attempt budget — declared before take one [heuristic]
Write two things down before the first fire:
A take budget — a number, sized against the acceptance-rate reality in
../../production-benchmarks.md (draft-tier exploration stretches it —
§ Drafts Validate the Prompt, Not the Take).
A written "good enough" bar — the primary thing delivered, secondary
flaws postable. Without it written down, the bar silently becomes
"perfect," and no budget survives that.
At half the budget with no progress on the same flaw, stop iterating and
change strategy: a different mode, a shot split, or the Retry Ladder's rung-4
named options. Iteration without a stop condition is how a cheap shot becomes
an expensive one. The budget is not a promise of success — it is the tripwire
that forces the strategy change.
The shot log is the ledger row
One line per take — what changed, what resulted — and the repo already has
the surface for it: the generation ledger (../../db/ledger/, § Log the
Outcome below, 5-second rule). Put the one changed variable in notes
("changed: lens lock line"); prompt_hash already dedupes identical
re-rolls. Two rows sharing a flaw is the rewrite tripwire made auditable —
re-reading the log beats re-living it.
Sequence & Continuation Failure Atlas
[FIELD — community, Emily2040/seedance-2.0 skill (MIT), re-derived 2026-08-09]
Symptom → likely cause → single repair variable for chained work:
continuations, extensions, and start-frame-pinned handoffs. One repair
variable per retake — the one-variable rule applied to sequences. Handoff
mechanics live in ../higgsfield-pipeline/SKILL.md § Continuation & Extension
Handoff; prompt templates in ../higgsfield-seedance/SKILL.md § Continuation
Prompt Formula. This table is the symptom-side index into both.
Symptom
Likely cause
Repair variable (change this one thing)
Continuation opens from the planned ending, not the delivered one
Prompt written from the shot plan; the accepted take's actual end state was never reviewed
Rewrite the opening from what the parent clip actually shows — the source carries state, the prompt carries only the delta (higgsfield-pipeline § Source-carries-state rule)
Action restarts from the top
Completed beat never marked as already done
State the beat as completed ("the door already stands open") and prompt only what happens next
A later beat shows up early
Future-beat material leaked into this clip's prompt
Strip every future beat from prompt and endpoint — one clip owns one beat
Identity drifts across extensions
The chain tail displaced the canonical identity reference
Re-anchor from the ORIGINAL character refs, never a frame from the drifted tail (higgsfield-pipeline § Chain management)
Screen direction flips at the join
Axis never locked, or reset unintentionally
State the direction ("walks screen-left to screen-right") or declare the axis change as intentional coverage
Mid-flight motion stops dead
Open motion vector not carried across a still-frame handoff
Carry subject/camera speed and direction in prose — one of the three things a frame cannot carry (higgsfield-pipeline § Source-carries-state rule)
Camera move restarts from rest
Parent's camera-move phase missing from the prompt
Open from the observed camera phase ("mid-dolly, continuing in")
Prop contradicts the prior clip
Prop owner / position / condition not tracked across the handoff
Add a prop-state line (who holds it, where it sits, what condition); prop sheet for recurring props (higgsfield-pipeline Pipeline E Stage 3)
Dialogue repeats a delivered line
Audio phase not carried at the cut point
Mark the line as delivered and continue from the audio phase — a frame cannot carry it
Each extension looks worse than the last
Expected chain-depth drift — each generation re-ingests the previous one's artifacts
Re-anchor from canonical refs or cut intentionally; cap chains at 2 extensions, hard ceiling 3 (higgsfield-pipeline § Chain management)
A reference bleeds into the wrong role
Transfer / ignore clauses absent
Split the roles: per-image role line plus explicit exclusions (higgsfield-seedance § Reference Roles)
Too much happens; nothing lands
Several beats compiled into one prompt
Reassign future beats to later clips (higgsfield-seedance § Single-vs-multi-shot decision)
A repair that works gets logged (§ Log the Outcome). Two takes failing on the
SAME row is the rewrite tripwire in § Take Triage — stop re-rolling into the
same wall.
Retry Ladder — a failed take edits the plan, not just the dice
[EMPIRICAL — MiniMax H3 skill corpus, re-derived] When a take fails or drifts and the
diagnostic tree confirms the references and mappings were right, escalate in this order.
Each rung terminates — never loop on one rung:
Re-run once, quoting the reference map verbatim. The original role + exclusion
lines, unedited. If the mapping was right, one clean re-roll is legitimate variance.
Treat the second failure as evidence the shot is over-packed. Shorten the
envelope and/or split the surplus beats into a new adjacent prompt (the shotlist
density split triggers apply), then re-run the preflight linter on both halves
before firing either. A second identical re-roll pays twice for the same overload.
Switch models for that one shot. One shot on a different engine beats bending
the whole piece around a shot the current engine won't hold.
Stop after three paid attempts and present named options — accept the best
take, re-scope the shot, defer it, or ship with an explicit placeholder: missing clip note in the deliverable. Silent omission is never one of the options.
Log the rung that resolved it (§ Log the Outcome) — rung-2 resolutions are shotlist
authoring lessons, not generation luck.
Log the Outcome — Always
Troubleshooting that isn't logged is troubleshooting the next session repeats.
After ANY confirmed fix from this skill, write it to the learning memory
(../../scripts/higgsfield_memory.py, databases in ../../db/):
Filter workaround confirmed (the rewritten prompt passed in a real
generation): python3 scripts/seedance_lint.py --confirmed "<prompt that passed>"
Quality fix confirmed (the improved prompt fixed motion / identity /
blocking / audio): python3 scripts/higgsfield_memory.py add-quality '<json>' with
original_prompt, failure_description, improved_prompt, model_used —
then update-quality <id> improved once verified.
Outcome learned later for an entry that already exists:
python3 scripts/higgsfield_memory.py update-filter <id> <fixed|workaround|still-blocked>
Project-specific lessons: add --project <name> to keep them scoped
under ../../db/projects/ instead of global memory.
Before troubleshooting, also CHECK memory first — that's higgsfield-recall's
job (query-filter / query-quality); the preflight's MEMORY RECALL section
does it automatically.
Vision-Grounded Diagnosis — Classify the Rejected Still, Don't Guess
The reject_reason you log feeds the iterate-vs-batch fork (higgsfield-recall
§ Read the verdict). Logged from memory it's hearsay — "I think the face
drifted." When you can actually see the rejected output, classify it from the
frame instead of from recall. This is an opt-in assist ("diagnose this
rejected shot"), and it is advisory: vision proposes, the human confirms.
Scope (v1): stills only — an image, or a single representative frame the user
picks from a video. Full-clip motion failures (FPS drift, temporal de-dup,
multi-motion) are out of scope here; they need frame-by-frame review
(../higgsfield-seedance/FAILURE-MODES.md), not a single-frame classify.
The chain:
Capture. Get the still in hand. Local image → read it directly. Web URL →
media_import_url (never pass a raw URL). Cowork local file → the upload
widget. Outputs are not auto-saved, so capture is an explicit step.
Classify against the reject_reason enum (the table below). Note what you
see in one line (the vision_evidence).
No clean home → other + note. Some visible failures (warped hand, FPS
drift) have no exact enum value. Route to other with the evidence note;
never force-fit a near-miss. If the other pile grows, that's the data
that justifies a future enum-extension PR.
Confirm, then log. Surface the proposal — "vision says physics (warped
left hand, center frame); confirm or correct?" — then:
--reason is the human verdict (drives the fork); --vision-reason is the
proposal (feeds the agreement gate). Logging both is what lets the tool learn.
Mapping table — what vision sees → reject_reason:
Vision observes
reject_reason
face / identity changed vs reference
identity-drift
wardrobe or colour shifted vs reference
wardrobe-contamination
extra cuts / unwanted scene breaks
extra-cuts
staging or blocking broken
blocking-broken
flat / wrong performance
performance
wrong camera move
camera-wrong
physics or anatomy violation (incl. warped hand)
physics
garbled on-screen text
text-render
provider content-filter block
filter-flagged
bad framing / composition
composition
FPS drift, temporal de-dup, or no clean home
other + evidence note
Measure before trusting. Vision is the fork's accuracy backstop only once
proven. python3 ../../scripts/higgsfield_memory.py agreement <project> reports, per
reject_reason class, how often the proposal matched the confirmed verdict. A
class is trusted (vision may be logged without confirmation) only above the
agreement gate over enough confirmed diagnoses; until then, confirm every one.
