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 dcc-mcp/dcc-mcp-shogun --skill shogun-processinggit clone --depth 1 https://github.com/dcc-mcp/dcc-mcp-shogunWrote 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/dcc-mcp/dcc-mcp-shogun/shogun-processing)<a href="https://agentmods.dev/skills/dcc-mcp/dcc-mcp-shogun/shogun-processing"><img src="https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-shogun/shogun-processing/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/dcc-mcp/dcc-mcp-shogun/shogun-processing"><img src="https://agentmods.dev/badge/skills/dcc-mcp/dcc-mcp-shogun/shogun-processing.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 | $0.00074 | $0.00438 |
| Opus 5.5 | $0.00030 | $0.00175 |
| Sonnet 5.5 | $0.00015 | $0.00088 |
| Haiku 4.5 | $0.00007 | $0.00044 |
Grade A, and why
shogun-processing 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 21d 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
Vicon Shogun Post Processing
Start with shogun-scene__inspect_scene, then inspect the relevant processing
settings. For selected_ranges, first establish an explicit range with
shogun-timeline__select_time_range.
Every processing tool is a closed wrapper over one official Offline method.
No arbitrary HSL or Python is accepted. General processing and QuickPost expose
only current_frame and selected_ranges. ROM labeling and calibration expose
the exact ranges documented for those vendor methods, but always require the
caller to state play_range explicitly; it is never an implicit default.
Reconstruction, occlusion, and solving settings expose small typed allowlists. Each update requires at least one field, applies documented numeric bounds, and attempts to restore all prior values if the official SDK setter fails.
Processing changes scene data and can affect many keys. Save to a new VDF path before a production run. A host may expose the SDK surface yet reject a command; the adapter reports that capability failure without UI fallback or partial-success claims.
What ships with it
15 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.
- scripts/_result.py 79 B runs code
- scripts/_run.py 189 B runs code
- scripts/auto_label.py 395 B runs code
- scripts/calibrate_subjects.py 464 B runs code
- scripts/fix_occlusion.py 404 B runs code
- scripts/inspect_processing_settings.py 426 B runs code
- scripts/label_rom.py 414 B runs code
- scripts/quick_post.py 423 B runs code
- scripts/reconstruct.py 375 B runs code
- scripts/retarget.py 366 B runs code
- scripts/solve.py 362 B runs code
- scripts/update_occlusion_settings.py 754 B runs code
- scripts/update_reconstruct_settings.py 716 B runs code
- scripts/update_solving_settings.py 710 B runs code
- tools.yaml 9.7 KB
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.
- 21d ago Changed 6caf2eba72d2
- 1mo ago Changed 5eb20fc55d7e
- 2mo ago First seen · 43 lines · 74 tokens per session scan A 434929980ae5
shogun-processing is a skill published in the GitHub repository dcc-mcp/dcc-mcp-shogun (1 stars, last pushed 14d ago), licensed MIT. It adds 74 tokens to every session and 438 once invoked, about $0.0003 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 2026-08-31.
shogun-processing compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| shogun-processingdcc-mcp/dcc-mcp-shogun | Skill | 1 | 14d ago | MIT | A | 74 | Claude Code, Codex |
| jupyter-notebookNousResearch/hermes-agent | Skill | 262k | today | MIT | A | 18 | Claude Code, Codex |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 6d ago | MIT | A | 44 | Claude Code |
| pennylaneK-Dense-AI/scientific-agent-skills | Skill | 56k | 6d ago | MIT | A | 74 | Claude Code |
| cuequivariance-jaxNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 38 | Claude Code, Codex |
| cuequivariance-torchNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 37 | Claude Code, Codex |
| pysrastroautomata/PySR | Skill | 4.3k | 2d ago | Apache-2.0 | A | 61 | Claude Code, Codex |