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 calesthio/generative-media-skills --skill neural-reality-capturegit clone --depth 1 https://github.com/calesthio/generative-media-skillsWrote 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/calesthio/generative-media-skills/neural-reality-capture)<a href="https://agentmods.dev/skills/calesthio/generative-media-skills/neural-reality-capture"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/neural-reality-capture/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/calesthio/generative-media-skills/neural-reality-capture"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/neural-reality-capture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00128 | $0.06998 |
| Opus 5 | $0.00064 | $0.03499 |
| Sonnet 5 | $0.00026 | $0.01400 |
| Haiku 4.5 | $0.00013 | $0.00700 |
Grade A, and why
neural-reality-capture 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 — 518 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neural Reality Capture
Neural reality capture is the production craft of reconstructing or rendering a real subject from many overlapping observations. In this skill, the deliverable is one of three families:
- a photogrammetric mesh with textures, usually for game, VFX, GIS, cultural heritage, ecommerce, or DCC handoff;
- a NeRF or related neural radiance field, usually for high-quality novel-view rendering from known camera poses;
- a 3D Gaussian splat scene, usually for real-time free-viewpoint playback with radiance-field appearance.
Stay provider-independent. Choose tools by evidence, constraints, and handoff needs, not by brand habit. Do not promise metrology unless the request includes surveyed control, calibrated capture, uncertainty reporting, and a qualified measurement workflow. Do not expand into text-to-3D, generative resculpting, animation rigging, material lookdev beyond captured handoff, or broad finishing work that belongs downstream.
Evidence Labels
Use these labels in plans, reviews, and troubleshooting:
- Paper fact: a claim from a peer-reviewed paper or original technical report, such as the original NeRF or 3D Gaussian Splatting papers.
- Standard fact: a claim from a specification or standard, such as Khronos glTF or OpenUSD documentation.
- Official documentation fact: a claim from tool, capture, or production documentation maintained by the relevant organization.
- Empirical observation: a result from a documented test or reconstruction run.
- Production heuristic: a practical rule that usually improves capture or handoff, but is not a guarantee.
When the user asks for a recommendation, combine fact and heuristic explicitly: "Standard fact: glTF 2.0 uses meters and a right-handed coordinate system. Production heuristic: deliver GLB for lightweight web review and USD for layered VFX or DCC assembly."
Representation Choice
Choose the representation from the final use, not from capture novelty.
Photogrammetric mesh
What ships with it
1 file 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.
- 12d ago First seen · 518 lines · 128 tokens per session scan A 9be747cd3425
neural-reality-capture is a skill published in the GitHub repository calesthio/generative-media-skills (170 stars, last pushed 2mo ago), licensed MIT. It adds 128 tokens to every session and 6,998 once invoked, about $0.0006 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.
Other skills, from other repositories
cliptalk-cover-director
Produces evidence-backed cover candidates and reviewable cover variants for a ClipTalk video. Use when the user asks for a cover, poster frame, thumbnail, or multiple cover directions; do not use for timeline editing or social-video reframing.
cliptalk-smart-reframe
Creates a subject-aware, time-varying crop track and a review-only social-format preview from an accepted ClipTalk cut. Use for automatic vertical, square, or portrait reframing; do not use for a fixed manual crop or before content editing is accepted.
cliptalk-content-extractor
Locates and assembles source passages matching a semantic request. Use for extracting explanations, topics, quotes, demonstrations, or other specifically described content.
cliptalk-interview-editor
Produces a coherent interview edit by combining speaker discovery, topic selection, dialogue context, cleanup, subtitles, and preview. Use for interviews, podcasts, testimonials, or question-and-answer recordings.
cliptalk-shortform-hook-director
Finds and assembles a reviewable short-form cut with a strong opening hook. Use for Shorts, Reels, social clips, talking-head cutdowns, or requests for a punchier opening.
cliptalk-social-reframe-exporter
Creates a review-only 9:16, 4:5, 1:1, or 16:9 version from an existing accepted ClipTalk cut, then checks the rendered preview. Use only when a cut already exists and the user asks to adapt it for Shorts, Reels, Douyin, Xiaohongshu, WeChat Channels, or square feeds; do not use when the user still needs content found…