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 KirKruglov/claude-skills-kit --skill research-to-content-briefgit clone --depth 1 https://github.com/KirKruglov/claude-skills-kitWrote 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/kirkruglov/claude-skills-kit/research-to-content-brief)<a href="https://agentmods.dev/skills/kirkruglov/claude-skills-kit/research-to-content-brief"><img src="https://agentmods.dev/badge/skills/kirkruglov/claude-skills-kit/research-to-content-brief/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/kirkruglov/claude-skills-kit/research-to-content-brief"><img src="https://agentmods.dev/badge/skills/kirkruglov/claude-skills-kit/research-to-content-brief.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 | $0.00064 | $0.01576 |
| Opus 5 | $0.00032 | $0.00788 |
| Sonnet 5 | $0.00013 | $0.00315 |
| Haiku 4.5 | $0.00006 | $0.00158 |
Grade A, and why
research-to-content-brief 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research to Content Brief
This skill reads a folder of raw research notes (audience observations, competitor signals, trend snippets) and synthesizes them into a structured content brief. Unlike dialogue-based brief generators, it extracts insights directly from your files — no interview needed.
Input:
- Folder path containing
.mdand/or.txtresearch files, OR direct paste of research notes with type labels - Optional: target content format hint (blog post, landing page, social, email)
Output:
content-brief.md— structured markdown brief with six sections: Audience, Core Message, Content Angles, Competitive Differentiation, Trend Hooks, Recommended Formats
Language Detection
Detect the user's language from their message:
- If Russian (or contains Cyrillic): respond in Russian
- If English (or other Latin-script language): respond in English
- If ambiguous: respond in the language of the trigger phrase used
Instructions
Step 1: Read and Categorize Research Files
-
Read all
.mdand.txtfiles from the provided folder path (or accept pasted content)- If no files found and no content pasted: stop immediately. Report: "No research files found. Provide a folder path with .md or .txt files, or paste research notes directly."
- If files exist but contain no readable text (images, binary, placeholder text): Report: "Files found but contain no extractable research content. Ensure files are .md or .txt with actual text."
-
Categorize each file into one of four types:
- Audience notes — pain points, goals, jobs-to-be-done, user profiles, quotes
- Competitor signals — what competitors do/say, feature lists, positioning language
- Trend snippets — industry trends, emerging topics, market shifts, date-stamped observations
- Other — anything that doesn't fit the above (include in extraction as context)
-
If files lack explicit category labels, auto-categorize by keyword heuristics:
- Competitor names, brand names, "they offer", "pricing", "compared to" → competitor signals
- "pain", "problem", "frustrated", "goal", "wants to", "needs to", "job" → audience notes
- "trend", "growing", "2025", "2026", "new", "emerging", "market shift" → trend snippets
- Note classification rationale in the output source coverage section
What ships with it
4 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.
- 12d ago First seen · 158 lines · 64 tokens per session scan A c3d93c0aa947
research-to-content-brief is a skill published in the GitHub repository KirKruglov/claude-skills-kit (18 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 1,576 once invoked, about $0.0003 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.
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