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Profile a dataset, choose and interpret statistical methods, then validate calculations and conclusions before sharing.

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01 · Category

What the MCP returns at this step

03 · Image & Video · Visual Prompting

Visual Prompt Diagnostics

Diagnose a failed image/video result and propose controlled prompt revisions.

promptdiagnoseprompt-iteration-and-diagnostics

Visual Prompt Diagnostics · Original SKILL.md

---
name: prompt-iteration-and-diagnostics
description: "Diagnose failed AI image or video outputs, identify root causes, revise prompts with single-variable iteration, and produce structured prompt revision reports."
---

# prompt-iteration-and-diagnostics

## When to use
- The user shows or describes a failed output and asks how to fix it.
- The user wants prompt diagnostics, iteration, A/B variants, failure analysis, or a revision report.
- The output has drift, bad anatomy, wrong motion, weak realism, camera chaos, text/logo errors, prompt collapse, or model transfer failure.
- The prose, dialogue, or film package has generic AI voice, weak story logic, rights/provenance gaps, or release-quality concerns.

## When not to use
- The user has no output or failure description and only needs first-draft prompting.
- The issue is a tool outage or account/billing problem.
- The user asks for unsafe bypass instructions.

## Required inputs
- Original prompt
- Observed output or failure description
- Target model/tool
- Desired result

## Optional inputs
- Reference images
- Settings/parameters
- Seed/version
- Previous attempts
- Continuity bible

## Workflow
1. Restate the intended result and the actual failure.
2. Classify failure: prompt ambiguity, contradiction, overload, model limitation, continuity drift, temporal overload, reference conflict, safety/policy rejection, or parameter mismatch.
3. Identify the smallest change likely to improve the result.
4. Apply the one-variable rule for iterative tests unless the prompt is fundamentally broken.
5. Rewrite using the relevant skill: image, video, continuity, style, or model-adaptation.
6. Produce a revision report with diagnosis, changed fields, unchanged anchors, expected improvement, and next test.
7. When the failure is a model limitation, redesign the shot or route to a better model instead of forcing the same prompt.

## Decision logic
- If output is 80 percent correct, make minimal edits.
- If identity drift appears, strengthen references and bible anchors.
- If motion fails, reduce actions and camera moves.
- If text/logos fail, choose a text-capable model or simplify typography.
- If a prompt is rejected, remove unsafe/IP-sensitive content and do not provide bypass tactics.

## Output formats
- Failure diagnosis
- Revised prompt
- A/B test variants
- Prompt revision report
- Continuity update recommendations
- Model-routing note
- Story/voice/release QC report

## Quality checks
- Diagnosis maps to a specific prompt or model cause.
- Revisions preserve what already worked.
- Only meaningful variables change per test.
- The report distinguishes fixable prompt issues from model limitations.
- Story, voice, continuity, performance, rights, and provenance checks are separated when evaluating a full AI film package.
- No unsafe bypass methods are included.

## Anti-patterns
- Rewriting the entire prompt after a small failure
- Adding long negative lists without prioritization
- Blaming the model before checking contradictions
- Ignoring uploaded reference conflicts
- Treating policy rejection as a prompt-engineering puzzle.

## Exit criteria
- The user has a revised prompt and a clear next test or model-routing decision.

## Supporting files
Read only the supporting file needed for the active task:
- `references/iteration_diagnostics.md`
- `references/failure_modes.md`
- `references/revision_loops.md`
- `references/story_voice_release_qc.md`
- `templates/prompt_revision_report.md`
- `templates/failed_output_intake.md`

When to use

Diagnose a failed image/video result and propose controlled prompt revisions.

What you get

The complete original workflow, with source attribution and the limitations below.

How it works

Confirm the task, check actual dependencies, then apply the relevant original instructions within authorized scope.

Requirements

Original prompt, observed result, desired change, model and settings.

Delivery and review notes

Published after an obvious-danger screen under the user-requested policy, not a functional test. Supporting files required conditionally by the original are not supplied: references/iteration_diagnostics.md, references/failure_modes.md, references/revision_loops.md, references/story_voice_release_qc.md, templates/prompt_revision_report.md, templates/failed_output_intake.md. Related model-adaptation/continuity handoffs must be available for the actual task; loading this source does not load them. No media generation, model-capability verification or code execution was performed. Treat named-model parameters as unknown until checked and use authorized references/assets.

Starting prompt

Use Visual Prompt Diagnostics for [TASK]. Ask for missing inputs: Original prompt, observed result, desired change, model and settings. Check the declared dependencies and limitations before execution. Return the original output structure with known facts, assumptions and unresolved requirements separated.

Not for

Claiming that unavailable source dependencies are bundled or that generated outputs, integrations or model behavior have been tested. Actual media generation without a separately available authorized tool.

What M11 added

M11 added German routing, task inputs and explicit source-package limitations; the original author remains separate from M11 curation. Published after an obvious-danger screen under the user-requested policy, not a functional test. Supporting files required conditionally by the original are not supplied: references/iteration_diagnostics.md, references/failure_modes.md, references/revision_loops.md, references/story_voice_release_qc.md, templates/prompt_revision_report.md, templates/failed_output_intake.md. Related model-adaptation/continuity handoffs must be available for the actual task; loading this source does not load them. No media generation, model-capability verification or code execution was performed. Treat named-model parameters as unknown until checked and use authorized references/assets.

Original authorship remains with 0xhughs/director-skills · Original source ↗

Original license & copyright

MIT License

Copyright (c) 2026 0xhughs

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Source SHA-256: bbbb34552d722477424a76cf771ce7ca6598c28ab270f1ba91cb658b1cdb99bd
Snapshot checked: 2026-09-28T15:16:05.544Z

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