Agent skill

blog-drafter

Interview-driven blog post drafting for technical product audiences. Use when user wants to write a blog post, article, or essay and needs help developing their thesis, structure, and initial draft. Triggers on "write a blog post", "draft an article", "help me write about X", "blog drafter", or when user has a topic they want to turn into written content. Conducts structured interviews using AskUserQuestion to extract the user's unique insights before generating drafts.

Stars 3
Forks 1

Install this agent skill to your Project

npx add-skill https://github.com/petekp/agent-skills/tree/main/skills/blog-drafter

SKILL.md

Blog Drafter

Interview the user to extract their unique perspective, then produce a structured draft with thesis, outline, and research suggestions.

Process Overview

Phase 1: Discovery Interview → Structured Draft + Research
Phase 2: Prose Refinement Interview (after user approves draft)

Phase 1: Discovery Interview

Opening

Ask what topic they want to write about. If they've already stated it, acknowledge and move directly to the interview.

Interview Strategy

Use AskUserQuestion for structured choices. Use regular follow-up questions for open-ended exploration. Aim for 4-6 question rounds total.

Round 1: Core Thesis

AskUserQuestion:
  question: "What's the single most important thing you want readers to take away?"
  options:
    - "A specific insight or realization"
    - "A call to change behavior or practice"
    - "A framework or mental model"
    - "A contrarian or non-obvious take"

Then probe: "Can you state that in one sentence?"

Round 2: The "So What"

Ask directly: "Why should a PM, designer, or engineer care about this right now? What pain or opportunity does this address?"

Round 3: Evidence & Experience

AskUserQuestion:
  question: "What's your strongest evidence for this thesis?"
  options:
    - "Personal experience or case study"
    - "Data or research I've seen"
    - "Pattern I've observed across projects/companies"
    - "Logical argument from first principles"

Follow up: "Walk me through the specific example or evidence."

Round 4: Anticipated Resistance

Ask: "What's the strongest objection someone might raise? What would a skeptic say?"

Round 5: Unique Angle

AskUserQuestion:
  question: "What makes your perspective different from what's already written on this topic?"
  options:
    - "I have direct experience others don't"
    - "I'm connecting ideas that aren't usually connected"
    - "I disagree with conventional wisdom"
    - "I have a specific framework or process"

Round 6: Scope & Format

AskUserQuestion:
  question: "What length and depth feels right?"
  options:
    - "Short and punchy (800-1200 words)"
    - "Standard blog post (1500-2500 words)"
    - "Deep dive (3000+ words)"

Interview Principles

  • Listen for contradictions—they often reveal the real insight
  • When answers are abstract, ask for concrete examples
  • If the thesis sounds generic, push: "What would make someone disagree with this?"
  • Capture specific phrases and terminology the user employs

Phase 1 Output: Structured Draft

After the interview, produce:

1. Thesis Statement

One clear sentence stating the core argument.

2. Draft Structure

markdown
## [Working Title]

**Hook**: [Opening that creates tension or curiosity]

**Thesis**: [Core argument, stated directly]

### Section 1: [Setup/Context]
- Key point
- Key point

### Section 2: [Core Argument/Evidence]
- Key point with specific example from interview
- Key point

### Section 3: [Addressing Objections]
- Anticipated resistance
- Response

### Section 4: [Implications/Call to Action]
- What readers should do differently
- Why it matters

**Closing**: [Callback to hook or forward-looking statement]

3. Research Suggestions

Provide 3-5 specific suggestions:

  • Relevant studies, books, or articles to cite
  • Data points that would strengthen arguments
  • Examples from well-known companies/products that illustrate points
  • Experts or practitioners whose work relates to the thesis

Format as actionable items:

markdown
## Suggested Research

- [ ] Look for data on [specific metric/phenomenon] to support Section 2
- [ ] Reference [Author]'s work on [topic] for theoretical grounding
- [ ] Find a counter-example from [domain] to strengthen the objection response
- [ ] Check if [Company] has published anything on their approach to [topic]

4. Open Questions

Note 2-3 areas where more depth or clarity would strengthen the piece.


After presenting the draft, ask: "Does this structure capture what you want to say? Any sections that feel wrong or missing?"

Phase 2: Prose Refinement

Trigger Phase 2 only after user approves the structure.

Refinement Interview

Round 1: Tone

AskUserQuestion:
  question: "What tone fits this piece?"
  options:
    - "Conversational and accessible"
    - "Authoritative and direct"
    - "Provocative and opinionated"
    - "Thoughtful and nuanced"

Round 2: Opening Style

AskUserQuestion:
  question: "How do you like to open posts?"
  options:
    - "Start with a story or anecdote"
    - "Lead with the controversial claim"
    - "Open with a question"
    - "Set up a problem or tension"

Round 3: Technical Depth

Ask: "How much should I explain? Are readers already familiar with [key concepts from interview], or do they need context?"

Round 4: Specific Preferences

Ask: "Any writing patterns you like or hate? (e.g., 'I never use bullet points' or 'I always include code examples')"

Refinement Output

Expand the structure into full prose, incorporating:

  • The chosen tone throughout
  • The selected opening style
  • Appropriate technical depth
  • User's stated preferences

Mark areas where user's voice is needed:

markdown
[VOICE: Add your personal take on why this matters to you]
[EXAMPLE: Insert specific story from your experience here]

Remind user: "This is a starting point for your voice. The final pass is yours."

Expand your agent's capabilities with these related and highly-rated skills.

petekp/agent-skills

multi-model-meta-analysis

Synthesize outputs from multiple AI models into a comprehensive, verified assessment. Use when: (1) User pastes feedback/analysis from multiple LLMs (Claude, GPT, Gemini, etc.) about code or a project, (2) User wants to consolidate model outputs into a single reliable document, (3) User needs conflicting model claims resolved against actual source code. This skill verifies model claims against the codebase, resolves contradictions with evidence, and produces a more reliable assessment than any single model.

3 1
Explore
petekp/agent-skills

capture-learning

Analyze recent conversation context and capture learnings to project knowledge files (for project-specific insights) or skills/commands/subagents (for cross-project patterns). Use when the user asks to "capture this learning", "update the docs with this", "remember this for next time", "document this issue", "add this to CLAUDE.md", "save this knowledge", or "update project knowledge". Also triggers after resolving build/setup issues, discovering non-obvious patterns, or completing debugging sessions with valuable insights.

3 1
Explore
petekp/agent-skills

optimize-agent-docs

Build a retrieval-optimized knowledge layer over agent documentation in dotfiles (.claude, .codex, .cursor, .aider). Use when asked to "optimize docs", "improve agent knowledge", "make docs more efficient", or when documentation has accumulated and retrieval feels inefficient. Generates a manifest mapping task-contexts to knowledge chunks, optimizes information density, and creates compiled artifacts for efficient agent consumption.

3 1
Explore
petekp/agent-skills

agent-changelog

Compile an agent-optimized changelog by cross-referencing git history with plans and documentation. Use when asked to "update changelog", "compile history", "document project evolution", or proactively after major milestones, architectural changes, or when stale/deprecated information is detected that could confuse coding agents.

3 1
Explore
petekp/agent-skills

literate-guide

Create a narrative guide to a codebase or feature in the style of Knuth's Literate Programming — code and prose interwoven as a single essay, ordered for human understanding rather than compiler needs. Use when the user asks to 'explain this codebase as a story', 'write a literate guide', 'create a narrative walkthrough', 'tell the story of this code', 'Knuth-style documentation', 'weave a guide for this feature', or when they want deep, readable documentation that treats the program as literature. Also trigger when someone wants a document that a thoughtful reader could follow from start to finish and come away understanding both WHAT the code does and WHY every design choice was made.

3 1
Explore
petekp/agent-skills

autonomous-agent-readiness

Assess a codebase's readiness for autonomous agent development and provide tailored recommendations. Use when asked to evaluate how well a project supports unattended agent execution, assess development practices for agent autonomy, audit infrastructure for agent reliability, or improve a codebase for autonomous agent workflows. Triggers on requests like "assess this project for agent readiness", "how autonomous-ready is this codebase", "evaluate agent infrastructure", or "improve development practices for agents".

3 1
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results