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 openviglet/turing-ce --skill brand-content-studiogit clone --depth 1 https://github.com/openviglet/turing-ceWrote 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/openviglet/turing-ce/brand-content-studio)<a href="https://agentmods.dev/skills/openviglet/turing-ce/brand-content-studio"><img src="https://agentmods.dev/badge/skills/openviglet/turing-ce/brand-content-studio/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/openviglet/turing-ce/brand-content-studio"><img src="https://agentmods.dev/badge/skills/openviglet/turing-ce/brand-content-studio.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 | $0.00127 | $0.01644 |
| Opus 5.5 | $0.00051 | $0.00658 |
| Sonnet 5.5 | $0.00025 | $0.00329 |
| Haiku 4.5 | $0.00013 | $0.00164 |
Grade A, and why
brand-content-studio 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 1mo 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Content Studio
Produce publication-ready, on-brand content for Acme Corp across four formats: article, video-script, newsletter, and one-pager. This file is the entry point; load the bundled references and run the bundled scripts only when the task actually needs them (progressive disclosure — keep context lean).
Folder layout
brand-content-studio/
├── SKILL.md ← you are here (always loaded when relevant)
├── references/
│ ├── brand-voice.md ← tone, beat model, do/don't vocabulary
│ ├── format-specs.md ← per-format section + length rules
│ └── legal-disclaimers.md ← mandatory disclaimers by content category
├── scripts/
│ ├── scaffold.py ← build a beat-structured markdown skeleton
│ ├── pick_quote.py ← extract the best pull quote from source text
│ ├── build_mindmap.py ← turn abstracts into a Mermaid mind-map
│ └── export.py ← render a draft to pdf / html / md
└── assets/
└── templates/
├── article.md
├── video-script.md
├── newsletter.md
└── one-pager.md
When to use / when NOT to use
Use when the request is to create or transform Acme narrative content, pick a quote, build a topic map, or export a draft.
Do NOT use when: the user wants code, spreadsheets/data analysis, content for a brand other than Acme, or anything that isn't prose/editorial. In those cases, say so and stop — do not force this skill.
Core workflow
Follow these steps in order. Skip a step only if the user already supplied its output.
-
Classify the format. Map the request to one of:
article,video-script,newsletter,one-pager. If ambiguous, ask one clarifying question, then proceed. -
Load the rules for that format. Read
references/format-specs.mdand find the section for the chosen format (required sections, target word count, tone overrides). Readreferences/brand-voice.mdonce per session for the global voice rules.
What ships with it
11 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.
- assets/templates/article.md 376 B
- assets/templates/newsletter.md 299 B
- assets/templates/one-pager.md 322 B
- assets/templates/video-script.md 393 B
- references/brand-voice.md 2.2 KB
- references/format-specs.md 1.7 KB
- references/legal-disclaimers.md 1.4 KB
- scripts/build_mindmap.py 2.0 KB runs code
- scripts/export.py 3.7 KB runs code
- scripts/pick_quote.py 1.8 KB runs code
- scripts/scaffold.py 1.7 KB runs code
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.
- 1mo ago First seen · 152 lines · 127 tokens per session scan A 1d1f128a5420
brand-content-studio is a skill published in the GitHub repository openviglet/turing-ce (22 stars, last pushed 4d ago), licensed Apache-2.0. It adds 127 tokens to every session and 1,644 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
sentence-transformers
Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production…
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
lilbee-mcp
Search and manage the user's local lilbee knowledge base over MCP. Use whenever the user has indexed code, docs, PDFs, or web pages into lilbee and you need cited answers, or whenever they ask you to ingest content, swap models, or tune retrieval against their library. Every fact returned cites file and line.…
lilbee-mcp-wiki
Wiki layer for lilbee. Use only when the user explicitly asks about wiki / concept / entity / synthesis pages, or when lilbeestatus shows a built wiki. Requires the lilbee-mcp skill to be active for the underlying MCP connection.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
apple-dev-docs
Answer Apple platform questions from Apple's official developer documentation and WWDC session transcripts instead of guessing or web-searching. Use when the user asks about iOS, iPadOS, macOS, watchOS, tvOS, or visionOS; Swift, SwiftUI, UIKit, AppKit, StoreKit, SwiftData, RealityKit, or any Apple framework; an API's…