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 corridor-alignment-pipelinegit 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/corridor-alignment-pipeline)<a href="https://agentmods.dev/skills/jenkinsm13/metashape-mcp/corridor-alignment-pipeline"><img src="https://agentmods.dev/badge/skills/jenkinsm13/metashape-mcp/corridor-alignment-pipeline/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/corridor-alignment-pipeline"><img src="https://agentmods.dev/badge/skills/jenkinsm13/metashape-mcp/corridor-alignment-pipeline.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 | $0.00048 | $0.01335 |
| Opus 5 | $0.00024 | $0.00668 |
| Sonnet 5 | $0.00010 | $0.00267 |
| Haiku 4.5 | $0.00005 | $0.00134 |
Grade A, and why
corridor-alignment-pipeline 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.
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Corridor Alignment Pipeline with Drift Detection
Overview
Align large road corridor captures incrementally in batches, with automatic drift detection between every batch. The pipeline stops if alignment diverges from GPS, preventing hours of wasted processing.
When to Use
- Aligning road corridor captures (100+ cameras along a linear path)
- Any linear capture where drift is a concern (railways, pipelines, coastlines)
- When GPS reference data is available for cameras
Prerequisites
- Photos imported into a chunk with GPS reference data (EXIF or imported CSV)
- Sensors configured (fisheye, rolling shutter, axes) per the
metashape-alignmentskill - GPU config:
set_gpu_config(cpu_enable=True)for alignment
Pipeline Steps
For each batch of cameras (recommended ~200 per batch):
1. Enable batch cameras
enable_cameras(labels=batch_labels, enable=True)
2. Match and align
match_photos(
generic_preselection=True,
reference_preselection=True,
keep_keypoints=True, # ALWAYS True for incremental
reset_matches=False # True only for very first batch
)
align_cameras(
reset_alignment=False # True only for very first batch
)
save_project()
3. Check drift (CRITICAL — do this after EVERY batch)
get_camera_spatial_stats()
Evaluate the error_gradient_per_100m field:
| Gradient | Assessment | Action |
|---|---|---|
| < 0.5 m/100m | PASS | Continue to next batch |
| 0.5 - 2.0 m/100m | WARN | Alert user. Suggest placing GCPs in the drifting region before continuing. |
| > 2.0 m/100m | FAIL | STOP. Report the problem. Do NOT continue alignment. |
4. Check continuity with previous batch
check_alignment_continuity(new_camera_labels=batch_labels)
If continuous is False:
- Report which cameras have position jumps or rotation breaks
- STOP and let the user investigate before continuing
5. Repeat for next batch
After all batches:
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 · 153 lines · 48 tokens per session scan A 97503553fd44
corridor-alignment-pipeline is a skill published in the GitHub repository jenkinsm13/metashape-mcp (34 stars, last pushed 4mo ago), licensed MIT. It adds 48 tokens to every session and 1,335 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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