Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/valkor-ai/loom/particlesnpx skills add valkor-ai/loom --skill particlesgit clone --depth 1 https://github.com/valkor-ai/loomWrote 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/valkor-ai/loom/particles)<a href="https://agentmods.dev/skills/valkor-ai/loom/particles"><img src="https://agentmods.dev/badge/skills/valkor-ai/loom/particles.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00085 | $0.00345 |
| Opus 5 | $0.00043 | $0.00172 |
| Sonnet 5 | $0.00017 | $0.00069 |
| Haiku 4.5 | $0.00009 | $0.00034 |
Grade A, and why
particles-review 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 5d 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.
What it actually says
Particles Review
Post-implementation reviewer for Godot particle code. Checks against known gotchas that LLMs consistently get wrong.
When to trigger
After particle-related code is written or modified. Look for:
- Particle nodes (GPUParticles2D, GPUParticles3D, CPUParticles2D, CPUParticles3D)
- ParticleProcessMaterial or
shader_type particles - Trail configuration (
trail_enabled,use_particle_trails) - Sub-emitter setup (
sub_emitter_mode,sub_emitter_*) - Collision nodes (GPUParticlesCollisionBox3D, GPUParticlesCollisionSphere3D, etc.)
- Runtime particle property changes (
.amount,.emitting)
Review process
- Read
gotchas.md - Scan the implemented code against each gotcha
- For each hit:
- Cite the gotcha ID (e.g. G1)
- Show the offending code
- Provide the fix
- If no hits, report clean
- Optionally run
checklist.mdstatic checks for automated verification
When you need specific API details, delegate to the godot-api skill.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 37 lines · 85 tokens per session scan A 96dafefdb211
particles-review is a skill published in the GitHub repository valkor-ai/loom (949 stars, last pushed 18d ago), licensed Apache-2.0. It adds 85 tokens to every session and 345 once invoked, about $0.0004 per session on Opus 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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