/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: 3.0.1
  updated: 2026-09-26
  parent: higgsfield
  compatibility:
    tools: [bash]
    scripts: [scripts/higgsfield_memory.py]
    databases: [filter-memory.json, quality-memory.json]

Higgsfield Recall — Pre-Generation Memory Check

<!-- CONTENTS — generated by scripts/build_index.py; do not hand-edit --> **Contents**

• [Purpose](#purpose)
• [When to Run](#when-to-run)
• [Recall Workflow](#recall-workflow)
• [Step 1: Extract search terms from the prompt intent](#step-1-extract-search-terms-from-the-prompt-intent)
• [Step 2: Query both databases](#step-2-query-both-databases)
• [Step 3: Evaluate relevance](#step-3-evaluate-relevance)
• [Step 4: Apply findings silently](#step-4-apply-findings-silently)
• [Step 5: Surface findings only when material](#step-5-surface-findings-only-when-material)
• [Manual Recall (User-Initiated)](#manual-recall-user-initiated)
• [Pre-Generation Checklist (run mentally before every prompt)](#pre-generation-checklist-run-mentally-before-every-prompt)
• [Log the Generation Result — One Question, One Command](#log-the-generation-result--one-question-one-command)
• [Optional: log the routing (usage telemetry)](#optional-log-the-routing-usage-telemetry)
• [Read the verdict before re-rolling](#read-the-verdict-before-re-rolling)
• [Database Status Check](#database-status-check)
• [Related skills](#related-skills)

<!-- /CONTENTS -->

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 — **except where it breaks a

hard engine rule.** A stored substitution that describes a character by age (the real-person entry says "age range", and its example names one) loses to `../higgsfield-seedance/ENGINE-RULES.md` rule 1: keep the archetype, drop the age, and describe by role, build and visible markers. The memory record is data and is not rewritten; the rule is applied when the substitution is used.

• 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 r