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 skills add jenkinsm13/metashape-mcp --skill quality-settingsgit clone --depth 1 https://github.com/jenkinsm13/metashape-mcpWrote 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/jenkinsm13/metashape-mcp/quality-settings)<a href="https://agentmods.dev/skills/jenkinsm13/metashape-mcp/quality-settings"><img src="https://agentmods.dev/badge/skills/jenkinsm13/metashape-mcp/quality-settings/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/jenkinsm13/metashape-mcp/quality-settings"><img src="https://agentmods.dev/badge/skills/jenkinsm13/metashape-mcp/quality-settings.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00033 | $0.00435 |
| Opus 5 | $0.00016 | $0.00217 |
| Sonnet 5 | $0.00007 | $0.00087 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
quality-settings 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 11d 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
Optimize Quality Settings
Recommend processing parameters tuned to the user's dataset and hardware.
Information Needed
Ask the user (or read from the project):
- Photo count: Number of images in the dataset
- Available RAM: System memory in GB
- Priority: Quality vs speed
Guidelines by Dataset Size
| Setting | <100 photos | 100–500 | 500–2000 | 2000+ |
|---|---|---|---|---|
| Match downscale | 1 (High) | 1 (High) | 2 (Medium) | 2–4 |
| Keypoint limit | 60000 | 40000 | 40000 | 40000 |
| Depth map quality | 2 (High) | 4 (Medium) | 4 (Medium) | 8 (Low) |
| Depth filter | mild | mild | moderate | moderate |
| Face count | high | high | medium | medium/custom |
RAM Considerations
- <16 GB: Use downscale 4+ for depth maps, medium face count
- 16–32 GB: Use downscale 2–4, high face count up to ~500 photos
- 32–64 GB: Use downscale 1–2, high face count up to ~1000 photos
- 64+ GB: Can use ultra quality for smaller datasets
GPU/CPU Rule
set_gpu_config(cpu_enable=True)— BEFORE alignment (match_photos, align_cameras)set_gpu_config(cpu_enable=False)— BEFORE everything else (depth maps, meshing, texturing, DEM, ortho)
CPU slows GPU operations. It is ONLY beneficial for alignment.
Output
Read the chunk info, then provide specific parameter values for each processing step as ready-to-use tool calls.
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.
- 11d ago First seen · 45 lines · 33 tokens per session scan A 7187306ae972
quality-settings is a skill published in the GitHub repository jenkinsm13/metashape-mcp (34 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 435 once invoked, about $0.0002 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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