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":"2026-04-06T00:00:00.000Z","parent":"higgsfield","compatibility":{"tools":["bash"],"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.

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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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Log the Generation Result — One Question, One Command

Every generation attempt belongs in the generation ledger (../../db/ledger/ — kept AND rejected; the denominator is what makes takes-per-kept ratios possible). The write path is agent-side and obeys the 5-second rule: at most one short question, then the agent runs one command. The human never formats JSON, never fills a form.

When the user reports a generation result (pastes a link, says "that one worked", "trash", "the face drifted again"):

1. If the verdict and reason are already clear from what they said, ask nothing — log it directly.
2. Otherwise ask exactly one question: "keep or reject — what failed?" If they don't answer, drop it. Never ask twice, never nag.
3. Write the row yourself:

python3 ../../higgsfield_memory.py log-gen <project> \
  --model seedance_2_0 --tags dialogue-cu,two-char \
  --outcome rejected --reason extra-cuts --credits 160

• --tags and --reason come from the controlled vocabularies in ../../db/ledger/README.md — map the user's words to the nearest vocab value ("face drifted" → identity-drift); never invent new values.
• Add --draft for 480p exploration rolls (excluded from headline ratios).
• Wrong verdict logged? python3 ../../higgsfield_memory.py amend-gen <id> outcome=kept — corrections are superseding rows, history stays.
• Project name: the user's production name if one is established in the conversation, else default.
• Logging --method quick|mcsla tags the row for the framework-lift A/B (ab <project> --tag <shot_tag>); omit it to leave the row unlabeled and out of the comparison — never guess a method.

Optional: log the routing (usage telemetry)

HARD RULE #1 already makes you name the sub-skills you routed to on the first line of every response. When a production is tracking which skills actually earn their keep, persist that declaration:

python3 ../../higgsfield_memory.py log-route --skills higgsfield-prompt,higgsfield-camera

python3 ../../higgsfield_memory.py routing then ranks sub-skills by opens and lists the never-opened long tail. This is instrumentation, not a verdict — it makes "which skills are load-bearing, which to prune" answerable from data once enough requests accumulate; a small sample is not evidence a skill is dead.

Read the verdict before re-rolling

After a few logged rows, python3 ../../higgsfield_memory.py ratio <project> prints a per-shot-tag verdict that decides iterate-vs-batch:

• iterate (structural-dominant) → the prompt is wrong; hand off to higgsfield-prompt § The Iteration Rule (one variable at a time).
• batch+sel (stochastic-dominant) → the prompt is right; stop re-rolling one at a time — lock it, roll a batch, cull (see higgsfield-prompt § Batch-and-Select).
• low-n → fewer than five rows; don't trust the split, call it by eye.

A ⚠ plausibility line means a tag is beating its planning default by a wide margin — either real lift or under-logged failures; surface it, let the user decide. The verdict is only as good as the reject_reason labels, so map the user's words to vocab honestly — and when the rejected output is in hand, classify it from the frame instead of from memory (higgsfield-troubleshoot § Vision-Grounded Diagnosis logs a --vision-reason alongside the human verdict, advisory until the agreement command proves it).

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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