email-drafter
Draft professional emails based on context, tone, and recipient. Use for composing business emails.
git clone --depth 1 https://github.com/skrun-dev/skrun /tmp/email-drafter && cp -r /tmp/email-drafter/agents/email-drafter ~/.claude/skills/email-drafterSKILL.md
# Email Drafter Agent You are a professional email writing assistant. Draft emails that are clear, appropriate for the tone, and include a compelling call to action. ## Instructions 1. Read the context of the email (what it's about, why it's being sent) 2. Match the requested tone: formal, casual, or friendly 3. Adapt language to the recipient (colleague, client, executive, etc.) 4. Always include a clear call to action ## Output Format Return a JSON object with: - `subject`: A concise, descriptive subject line (< 60 chars) - `body`: The full email body (greeting, content, sign-off) - `call_to_action`: The specific action you want the recipient to take ## Tone Guidelines - **formal**: Professional language, no contractions, structured paragraphs - **casual**: Conversational, contractions OK, shorter sentences - **friendly**: Warm, personal, uses first names, emoji OK sparingly ## Examples Context: "Follow up on proposal sent last week" Tone: formal Recipient: "VP of Engineering at Acme Corp" → Subject: "Follow-Up: Technical Proposal for Acme Corp" → Body: Professional follow-up with reference to key benefits → CTA: "Would you be available for a 30-minute call this Thursday?"
Generate a numbered Architecture Decision Record (ADR) following the standard nygard/MADR convention. Reads the target ADR directory to compute the next number and to surface candidates for cross-linking. Use when asked to document an architectural decision, draft an ADR, or capture a technical choice with its rationale.
Generate a polished CHANGELOG.md and release-notes.md from a local git repository (or a captured `.git-log.txt` dump). Groups commits by Conventional Commit type, writes both artifacts to the run output directory. Use when asked to draft release notes, summarize commits between tags, or produce a human-readable changelog.
Review code for quality, bugs, security issues, and suggest improvements. Use when asked to review, audit, or improve code.
Turn a CSV of operational data (sales, usage, signups, support tickets) into a multi-page styled PDF executive report with narrative + matplotlib charts. The LLM analyzes the data, picks what's interesting, writes the prose, and emits a structured render request that becomes a polished PDF. Use when given a CSV and asked for a report, summary, or analysis.
Analyze structured data (CSV/JSON), find patterns, generate insights, and suggest visualizations. Use for data analysis tasks.
Turn a folder of Markdown notes (Obsidian vault, Notion export, plain repo docs) into a navigable static HTML knowledge base bundled as a single .zip file. Maintains a persistent concept graph across runs — concepts that appear in multiple runs gain prominence, and the index becomes denser over time. Use when given a Markdown vault and asked to publish, share, or render it as a browsable site.
Listen to a meeting recording and extract structured action items, decisions, and open questions. Maintains a persistent ledger across runs — previously-open actions are auto-resolved when mentioned as done in subsequent meetings. Outputs `actions.csv` (importable to Linear/Asana/Notion) + `recap.md` (paste into Slack). Use when given a meeting recording and asked for a recap or action items.
Read a PDF directly with vision and extract text, summarize, or analyze its structure. Use when the user passes a PDF file.