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 metashape-reconstructiongit 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/metashape-reconstruction)<a href="https://agentmods.dev/skills/jenkinsm13/metashape-mcp/metashape-reconstruction"><img src="https://agentmods.dev/badge/skills/jenkinsm13/metashape-mcp/metashape-reconstruction/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/metashape-reconstruction"><img src="https://agentmods.dev/badge/skills/jenkinsm13/metashape-mcp/metashape-reconstruction.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 | $0.00059 | $0.01658 |
| Opus 5 | $0.00030 | $0.00829 |
| Sonnet 5 | $0.00012 | $0.00332 |
| Haiku 4.5 | $0.00006 | $0.00166 |
Grade A, and why
metashape-reconstruction 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 12d 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metashape Dense Reconstruction via MCP
Overview
Build dense products from aligned cameras in Metashape using MCP tools. This covers everything AFTER alignment: depth maps, dense point cloud, mesh, texture, DEM, and orthomosaic. All MCP tool calls block until complete — no polling, no timeouts.
When to Use
- Building depth maps, point cloud, mesh, or texture in Metashape
- Generating DEM or orthomosaic survey products
- Any processing step after photo alignment is complete
Golden Rules
- CPU OFF for all dense operations.
set_gpu_config(cpu_enable=False)before depth maps, point cloud, meshing, texturing, DEM, orthomosaic. CPU slows GPU operations. CPU is ONLY for alignment (match_photos, align_cameras). - NEVER write pipeline scripts. Call each MCP tool individually, check the result, adapt. This is an AGENT workflow.
- No timeouts. Dense operations can take hours or days. MCP calls block until done.
- No polling. Don't call
get_processing_statusin a loop. - Save after every step. The MCP tools auto-save, but verify with
save_project()after major operations. - Check prerequisites. Each step requires the previous step's output. Verify before proceeding.
Standard Dense Pipeline
Step 0: Verify alignment is complete
get_alignment_stats()
# Check: alignment_rate should be >95%
# Check: tie_point_count_valid should be reasonable
Step 1: GPU config — CPU OFF
set_gpu_config(cpu_enable=False)
Step 2: Build depth maps
build_depth_maps(
downscale=2, # 1=Ultra, 2=High, 4=Medium, 8=Low, 16=Lowest
filter_mode="mild", # mild for complex terrain, moderate default, aggressive for clean scenes
reuse_depth=True # Reuse existing depth maps for aligned cameras
)
Quality guide:
downscale=1(Ultra): Final production, small projects. Very slow.downscale=2(High): Standard production quality. Good balance.downscale=4(Medium): Quick results, large projects, testing.
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.
- 12d ago First seen · 189 lines · 59 tokens per session scan A 5f80028ba36a
metashape-reconstruction is a skill published in the GitHub repository jenkinsm13/metashape-mcp (34 stars, last pushed 4mo ago), licensed MIT. It adds 59 tokens to every session and 1,658 once invoked, about $0.0003 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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