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-troubleshootWrote 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-troubleshoot)<a href="https://agentmods.dev/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-troubleshoot"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-troubleshoot/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-troubleshoot"><img src="https://agentmods.dev/badge/skills/osidemedia/higgsfield-ai-prompt-skill/higgsfield-troubleshoot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 421 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00039 | $0.08298 |
| Opus 5.5 | $0.00016 | $0.03319 |
| Sonnet 5.5 | $0.00008 | $0.01660 |
| Haiku 4.5 | $0.00004 | $0.00830 |
Grade A, and why
higgsfield-troubleshoot 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 4d 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 — 494 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Higgsfield Troubleshooting Guide
QUICK FACTS
Generated-checked block (scripts/build_index.py verifies anchors). Read the linked sections for full context — these lines are routing aids, not the rules themselves.
- Face inconsistency, dead camera moves, ignored prompts, static i2v, blocked dark content — the per-problem fix list →
- Kling 3.0 Motion Control failures are almost always upstream of the prompt: reference clip, character image, or orientation/scene-source settings →
- Pre-generation checklist: subject, action, named camera preset, style, grade, aspect, <200 words (short-form regime) →
- Seedance/Cinema Studio symptom table + diagnostic flowchart: blurry = overspecified; chaotic camera = One-Move Rule violated; wrong character = prompt text contradicts the reference — delete the contradicting text first; delete ALL appearance text only on a start-frame / I2V shot (P1-1) →
- Every delivered take gets ONE of five verdicts before anything re-fires: keep / fix-in-post / edit / re-roll / rewrite →
- Two takes with the same flaw = rewrite, by rule; different flaws per roll = stochastic → batch-and-cull, not rewrite — against Retry Ladder rung 2 when both takes failed: OPEN, no default (§ Stop-Rule Ladder) →
- Re-roll = same prompt again, unchanged — no seed parameter on this surface; every roll is a fresh sample →
- Change exactly one variable between takes so causality stays readable →
- Declare the take budget AND a written "good enough" bar before take one; half-budget with no progress forces a strategy change →
- The shot log is the ledger row — one line per take, changed variable in
notes→ - Continuation/extension defects: 12-row symptom → cause → single-repair-variable atlas (planned-vs-observed opening, motion-vector drop, prop contradictions, chain-depth drift…) →
- Retry Ladder: 4 terminating rungs — re-run once verbatim → treat 2nd failure as over-packing (when the two failures differ, Take Triage reads them as stochastic: OPEN, no default — § Stop-Rule Ladder) → switch model for that shot → stop after 3 paid attempts with named options →
- Six stop numbers, one ladder: 2 same-flaw re-rolls → rewrite · 3 paid attempts (no declared budget) → named options · half a declared budget with no progress → change strategy · 4 v2v batches (ceiling) → prompt/source fault · 10–15 surgical iterations (declared budget) → simplify the shot · 65–100 generations per kept shot is a project benchmark, never a stop rule. The earliest tripwire wins; where 2 and 4 count the same v2v runs it is OPEN, default the earlier stop →
- Log EVERY confirmed fix to learning memory, and check memory first before troubleshooting →
- Vision-grounded diagnosis (stills only): vision proposes the
reject_reason, the human confirms — advisory until a class clears the agreement gate →
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.
- 4d ago Changed · +1 lines 1e5d47c9967f
- 1mo ago Changed · +59 lines 2193246f5fdc
- 1mo ago First seen · 434 lines · 39 tokens per session scan A 4ff8c1ba1677
higgsfield-troubleshoot is a skill published in the GitHub repository OSideMedia/higgsfield-ai-prompt-skill (809 stars, last pushed 3d ago), licensed MIT. It adds 39 tokens to every session and 8,298 once invoked, about $0.0002 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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