/higgsfield-recall

name: higgsfield-recall
description: >
  Use this skill AUTOMATICALLY before writing any Higgsfield prompt. Query the memory
  databases for relevant past failures and pre-apply known fixes before the user even
  hits generate. Triggers include: any request to write a Higgsfield prompt, any use
  of the higgsfield-prompt skill, any mention of generating a video or image on
  Higgsfield, any MCSLA prompt construction. This skill should run SILENTLY in the
  background — don't announce it, just apply what's known. If the databases are empty,
  skip silently and proceed with normal prompt generation.
user-invocable: true
metadata:
  tags: [higgsfield, recall, memory, pre-check, filter, quality, prompt, generate]
  version: 3.0.0
  updated: 2026-04-06
  parent: higgsfield
  compatibility:
    tools: [bash]
    scripts: [scripts/higgsfield_memory.py]
    databases: [db/filter-memory.json, db/quality-memory.json]

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.

---

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.

---

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")

---

Step 2: Query both databases

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

# Check for relevant quality failures:
python3 scripts/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

---

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.

---

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

---

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.

---

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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Log the Generat