Higgsfield Recall — Pre-Generation Memory Check

Purpose

Before writing any Higgsfield prompt, query both memory databases to find relevant past failures. Apply known fixes silently — the user should never have to remember what broke before. The system remembers for them.

This skill runs automatically as part of any Higgsfield prompt generation. It does not interrupt the workflow unless it finds something relevant.

Bootstrap status: The databases ship with seed entries covering the most common failure patterns (character drift, VHS style ignored, I2V static output, camera conflicts, lip-sync desync, content filter blocks for real persons and IPs). These grow automatically as the user logs new failures.

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When to Run

Run a recall check whenever:

• Writing or improving a Higgsfield prompt (any type)
• The user mentions a topic, character, action, or style that could match past failures
• The prompt contains terms that historically triggered content filters
• The model being selected has previously produced poor results for this type of shot

Do NOT announce running the recall check. Just run it, apply what's relevant, and proceed. Only surface findings when they directly change the prompt.

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Recall Workflow

Step 1: Extract search terms from the prompt intent

Before querying, pull the key semantic terms from what the user wants:

Extract:
- Subject/character (person type, appearance)
- Action (what they're doing)
- Location/environment
- Style (visual style, model, camera)
- Topic (the general category: "car chase", "product shot", "horror scene")

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Step 2: Query both databases

# Check for relevant filter blocks:
python3 higgsfield_memory.py query-filter "<key terms from prompt>" 5

# Check for relevant quality failures:
python3 higgsfield_memory.py query-quality "<key terms from prompt>" 5

Query strategy:

• Use 3–6 of the most specific nouns from the prompt
• Run separate queries for the subject, action, and style if needed
• Prioritize entries with

  fix_confirmed: true

  — these are proven solutions

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Step 3: Evaluate relevance

For each result returned, assess:

Question	If yes →
Does this entry's topic/category directly overlap with this prompt?	Apply the known fix
Is a blocked term present in my draft prompt?	Remove/substitute it now
Did this model fail on this type of shot before?	Consider switching models
Is there a confirmed improved prompt for this scenario?	Use it as the base

Relevance threshold: Only act on entries with a relevance score > 0 from the query. Ignore entries that only match on generic words.

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Step 4: Apply findings silently

For filter block matches:

• Remove or substitute the blocked terms before presenting the prompt
• If a substitution was confirmed to work, use it directly
• Do not tell the user "I removed X because it was blocked before" unless they ask — just present the clean prompt

For quality failure matches:

• Use the confirmed improved prompt structure as the base
• Apply the specific fix that worked (e.g. explicit artifact description for VHS)
• Adjust the model if a better one was identified for this scenario

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Step 5: Surface findings only when material

Only mention the recall results if:

• A significant change was made to avoid a known filter block
• A model switch is recommended based on past failures
• The recall found a directly relevant confirmed fix that substantially changes the prompt

How to surface findings (when needed):

"⚠️ Filter note: Previous attempts with [term] were blocked on [date].
Using '[substitution]' instead — this was confirmed to pass."

"📋 Quality note: [Model] produced [failure type] for this scenario before.
Switching to [better model] based on past results."

If nothing relevant found: proceed silently, no mention of the recall check.

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Manual Recall (User-Initiated)

The user can also request a recall check directly:

"What do we know about [topic] failing?"
"Has [model] had issues with [scenario] before?"
"What got blocked when we tried [type of content]?"
"What's our substitution for [blocked term]?"

For these queries, surface the full relevant entries with:

• The original failure
• The substitution or fix that was tried
• Whether it was confirmed to work
• The date it was logged

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Pre-Generation Checklist (run mentally before every prompt)

Before finalizing any prompt, check:

• Named real person in prompt? → Check filter-memory for real-person blocks
• Weapon, drug, or violence language? → Check filter-memory for violence/substance blocks
• Brand or IP name? → Check filter-memory for brand-ip blocks
• Using a model that has failed for this scenario type? → Check quality-memory
• Using VFX/style keywords that were previously ignored? → Check quality-memory
• Character consistency required? → Check quality-memory for character-drift entries

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Database Status Check

To see current knowledge base size:

python3 higgsfield_memory.py stats

Empty databases = no recall benefit yet. Start logging failures with

higgsfield-troubleshoot

and the recall system gets smarter with every entry.

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Negative constraints: The recall system complements

../shared/negative-constraints.md

. The shared file covers universal prevention rules; this recall system covers user-specific past failures and confirmed fixes.

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Related skills

• higgsfield-troubleshoot

  — Diagnose and fix specific failures (feeds recall DB)

• higgsfield-prompt

  — MCSLA formula, Identity/Motion separation

• higgsfield-soul

  — Character drift prevention (common recall topic)

• higgsfield-models

  — Model-specific failure patterns