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 sun2443/designer-skills --skill guideline-generationgit clone --depth 1 https://github.com/sun2443/designer-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/sun2443/designer-skills/guideline-generation)<a href="https://agentmods.dev/skills/sun2443/designer-skills/guideline-generation"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/guideline-generation/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/sun2443/designer-skills/guideline-generation"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/guideline-generation.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 | $0.00117 | $0.01498 |
| Opus 5.5 | $0.00047 | $0.00599 |
| Sonnet 5.5 | $0.00023 | $0.00300 |
| Haiku 4.5 | $0.00012 | $0.00150 |
Grade A, and why
guideline-generation 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 13d 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.
This is a copy
100% identical to guideline-generation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guideline Generation
Generate comprehensive, LLM-ready brand voice guidelines from any combination of sources — brand documents, sales call transcripts, discovery reports, or direct user input. Transform raw materials into structured, enforceable guidelines with confidence scoring and open questions.
Inputs
Accept any combination of:
- Discovery report from the discover-brand skill (structured, pre-triaged)
- Brand documents uploaded or from connected platforms (PDF, PPTX, DOCX, MD, TXT)
- Conversation transcripts from Gong, Granola, manual uploads, or Notion meeting notes
- Direct user input about their brand voice and values
When a discovery report is provided, use it as the primary input — sources are already triaged and ranked. Supplement with additional analysis as needed.
Generation Workflow
1. Identify and Classify Sources
Determine what the user has provided. If no sources are available:
- Check if a discovery report exists from a previous
/brand-voice:discover-brandrun - Check
.claude/brand-voice.local.mdfor known brand material locations - Suggest running discovery first:
/brand-voice:discover-brand
2. Process Sources
For documents: Delegate to the document-analysis agent for heavy parsing. Extract voice attributes, messaging themes, terminology, tone guidance, and examples.
For transcripts: Delegate to the conversation-analysis agent for pattern recognition. Extract implicit voice attributes, successful language patterns, tone by context, and anti-patterns.
For discovery reports: Extract pre-triaged sources, conflicts, and gaps. Use the ranked sources directly.
3. Synthesize Into Guidelines
Merge all findings into a unified guideline document following the template in references/guideline-template.md. Key sections:
"We Are / We Are Not" Table — The core brand identity anchor:
| We Are | We Are Not |
|---|---|
| [Attribute — e.g., "Confident"] | [Counter — e.g., "Arrogant"] |
| [Attribute — e.g., "Approachable"] | [Counter — e.g., "Casual or sloppy"] |
What ships with it
2 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.
- 13d ago First seen · 142 lines · 117 tokens per session scan A 9f5fd0e3d9ce
guideline-generation is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 117 tokens to every session and 1,498 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to guideline-generation, differing in 0 lines, and is treated as a copy.
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