Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/OSideMedia/higgsfield-ai-prompt-skillnpx agentmods add skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-recallWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-recall)<a href="https://agentmods.dev/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-recall"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-recall/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-recall"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-recall.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00121 | $0.02811 |
| Opus 5.5 | $0.00048 | $0.01124 |
| Sonnet 5.5 | $0.00024 | $0.00562 |
| Haiku 4.5 | $0.00012 | $0.00281 |
Grade A, and why
higgsfield-recall scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Higgsfield Recall — Pre-Generation Memory Check
Contents
- Purpose
- When to Run
- Recall Workflow
- Manual Recall (User-Initiated)
- Pre-Generation Checklist (run mentally before every prompt)
- Log the Generation Result — One Question, One Command
- Database Status Check
- Related skills
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago Changed · +21 lines 735f4b144ea5
- 29d ago Changed · +5 lines 7fc6fb7969e7
- 1mo ago First seen · 267 lines · 121 tokens per session scan A 8e757e5c02cc
higgsfield-recall is a skill published in the GitHub repository OSideMedia/higgsfield-ai-prompt-skill (800 stars, last pushed 2d ago), licensed MIT. It adds 121 tokens to every session and 2,811 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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