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 zai-org/GLM-skills --skill glmv-prd-to-appgit clone --depth 1 https://github.com/zai-org/GLM-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/zai-org/glm-skills/glmv-prd-to-app)<a href="https://agentmods.dev/skills/zai-org/glm-skills/glmv-prd-to-app"><img src="https://agentmods.dev/badge/skills/zai-org/glm-skills/glmv-prd-to-app/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/zai-org/glm-skills/glmv-prd-to-app"><img src="https://agentmods.dev/badge/skills/zai-org/glm-skills/glmv-prd-to-app.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 | $0.00188 | $0.03541 |
| Opus 5.5 | $0.00075 | $0.01416 |
| Sonnet 5.5 | $0.00038 | $0.00708 |
| Haiku 4.5 | $0.00019 | $0.00354 |
Grade A, and why
glmv-prd-to-app scanned grade A with 1 finding 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
4. Test with curl or equivalent to verify response format How it starts
The opening of the file, as written. The whole thing — 510 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GLM-V PRD-to-App: Full-Stack Application Builder
Language: Respond in the same language the user uses. Code comments should match.
Build a complete, deployed web application from PRD + prototypes + resources.
The result must be fully reproducible via a single bash /workspace/start.sh.
Phase 0: Material Discovery & Analysis
Before anything else, understand what you're working with.
0a. Locate all inputs
/workspace/prd.md ← Product requirement document
/workspace/prototypes/*.jpg ← UI prototype images (the visual truth)
/workspace/resources/**/* ← Images, videos, icons, and other assets
If the materials are in a different location, adapt accordingly. Read the PRD fully.
0b. Deep prototype analysis
For every prototype image:
-
Read each image using the Read tool — you are multimodal, examine them directly.
-
For each image, document:
- Page identity: which page/view this represents
- Layout structure: header, sidebar, main content, footer, modals
- Component inventory: every button, form, card, table, list, nav element
- Content inventory: all visible text, numbers, labels, placeholder content
- Color extraction: primary, secondary, accent, background, text colors (hex values)
- Typography: font sizes, weights, hierarchy observed
- Interactive states: hover effects, active tabs, selected items, toggles
- Data patterns: what data populates lists/tables/cards — this drives seed data
-
Build a page map showing navigation flow between prototype pages.
0c. Resource inventory
List all files in /workspace/resources/ and map each to where it appears in the prototypes.
Every resource file must be used in the final application where relevant.
Phase 1: System Design Document
Produce a comprehensive design document at /workspace/docs/design.md.
1a. Data Model
For each entity, specify:
- Table/collection name
- All fields with types, constraints, defaults
- Relationships (foreign keys, many-to-many)
- Indexes needed for query patterns
- Content mapping: which prototype elements map to which fields
What ships with it
5 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.
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.
- 2mo ago First seen · 510 lines · 188 tokens per session scan A c3bfff80ea05
glmv-prd-to-app is a skill published in the GitHub repository zai-org/GLM-skills (473 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 188 tokens to every session and 3,541 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
vision-skills
Local vision CLIs: glance (describe/ask/OCR an image), ground (locate a target, pixel box), detect (element inventory), trace (image to SVG geometry), crop (cut a pixel box to a file), and scripts/htmlshot.py (HTML file to image). Use for any task involving an image — questions, text, splitting and transcribing long…
watch
Watch any video (URL, stream, or local path) via Watch Skill. Downloads, extracts scene-aware deduped frames, OCRs them, transcribes (captions first, then local Whisper — offline by default), indexes everything, and hands the result to the agent. Follow-up questions are answered from the persistent index without…
the-loop
The user built or changed something visual — a UI, an animation, a game, a generated video — and wants it verified, or asks "why does my UI look wrong", "check that the fix actually worked", "does the animation glitch". Use this to record the running thing, critique the recording against plain-language pass criteria…
asking-with-evidence
The user asks a question about a video that was already watched or indexed — "what did they say about X", "what error code appears", "what happens at 2:30", "does the video show Y". Use this to answer from the persistent index with timestamped evidence and a confidence score instead of re-watching or guessing.
configuring-vision
The user wants to connect an LLM or vision provider, already has an API key, asks "can I use OpenAI/Anthropic/Gemini/OpenRouter", wants local Ollama, or needs different cheap and strong models. Use this to configure provider-neutral visual understanding without tying Watch Skill to one agent or model vendor.
extracting-structure
The user wants structure pulled out of a watched video — "make chapters for this video", "where does the bug appear in this recording", "turn this screen recording into a bug report", "how strong is my intro/hook". Use this for deterministic extraction from the index — chapters with timestamps, a fileable bug report…