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.
git clone --depth 1 https://github.com/Adityaraj0421/naksha-studioWrote 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/commands/adityaraj0421/naksha-studio/gen-moodboard)<a href="https://agentmods.dev/commands/adityaraj0421/naksha-studio/gen-moodboard"><img src="https://agentmods.dev/badge/commands/adityaraj0421/naksha-studio/gen-moodboard/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/commands/adityaraj0421/naksha-studio/gen-moodboard"><img src="https://agentmods.dev/badge/commands/adityaraj0421/naksha-studio/gen-moodboard.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 | $0.00042 | $0.00649 |
| Opus 5 | $0.00021 | $0.00324 |
| Sonnet 5 | $0.00008 | $0.00130 |
| Haiku 4.5 | $0.00004 | $0.00065 |
Grade A, and why
gen-moodboard 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 9d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/gen-moodboard $ARGUMENTS
You are activating the AI Visual Gen Wing: AI Image Director + Brand Strategist.
Process
1. Parse the Brief
Extract from $ARGUMENTS:
- Concept: the campaign idea, product launch angle, or visual territory to explore
- Brand/product: name, any known brand personality, existing visual language
- Audience: who this is for — shapes tone and style direction
2. Extract Brand Personality Signals
Brand Strategist identifies 3–5 brand personality attributes that should translate visually:
Brand: [name]
Personality attributes: [e.g., "bold, irreverent, human-first, modern-minimal"]
Color language: [known? describe or note "undefined"]
Avoid: [visual territories that conflict with brand voice]
3. Define 3 Visual Directions
AI Image Director proposes 3 distinct style directions. Each must be meaningfully different — not just slight variations. Aim for tension between options so the client has a real choice.
For each direction:
## Direction [N]: [name — e.g., "Raw & Human" / "Clean & Minimal" / "Bold & Graphic"]
**Visual language:** [2-3 sentence description of the aesthetic]
**Mood:** [emotional register]
**References:** [3 real-world visual references — campaigns, photographers, brands]
**Why it works for this brand:** [1-2 sentences connecting to brand personality]
4. Write Prompt Pack Per Direction
For each direction, produce 4–6 ready-to-paste image prompts using the AI Image Director's 6-element anatomy. Label prompts by use case:
### [Direction name] — Prompt Pack
**Prompt 1 (Hero image — [platform]):**
[full prompt]
**Prompt 2 (Social post — [platform]):**
[full prompt]
**Prompt 3 (Product shot):**
[full prompt]
**Prompt 4 (Lifestyle):**
[full prompt]
All prompts in a direction must use the same core style tokens. This is what makes them a coherent direction.
5. Tool Recommendation
Specify the best tool for generating this direction's prompts and why.
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.
- 9d ago First seen · 94 lines · 42 tokens per session scan A 06650c867b90
gen-moodboard is a command published in the GitHub repository Adityaraj0421/naksha-studio (317 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 649 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.
Other commands, from other repositories
make-carousel
Design and produce a social media carousel (Instagram / LinkedIn / TikTok) — hook, narrative slides, CTA — with a consistent template and rendered slides.
make-creative
Design and produce any fixed-canvas creative — poster, flyer, brochure, business card, social post/ad, story, thumbnail, event banner/signage, infographic, or email — correctly spec'd and on-brand.
make-deck
Design and produce a presentation/deck (pitch, sales, conference, internal) using the Design Pro knowledge base — structure, slide design, and a producible output.
cover
Generate a 1600×500 SVG cover banner for a project README in the article-hero pattern.
slides
Generate a Distill-style slide deck on a topic or paper — paper background, system sans, one idea per slide.
verify
Fidelity check — rebuild a page from the extracted tokens, pixel-diff it against the live site, and score how faithfully the tokens capture the design.