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Education content brief builder

Builds a production-ready content brief for any customer education asset — article, video script, or course module — with defined learning objectives, audience-and-lifecycle framing, an outline, success metrics, and…

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Purpose

Turn a rough content request into a complete, hand-off-ready brief — with learning objectives, audience and lifecycle framing, a structured outline, format specs, and success metrics — so writers, video producers, and instructional designers can execute without a second planning meeting. Eliminates the vague "we need an article about X" request that produces off-target content.

When to Use

Invoke this skill when the user asks to:

  • Plan or scope a help article, knowledge base doc, or how-to guide
  • Brief a tutorial video, product walkthrough, or webinar segment
  • Outline an onboarding lesson or an LMS/academy course module
  • Turn a feature launch into supporting education content
  • Hand a content request to a writer, SME, or instructional designer
  • Standardize how education content requests get scoped across the team

Inputs Required

Before starting, collect:

  • Asset type: Article / video script / course module / interactive walkthrough / other
  • Feature or topic: The specific product feature, workflow, or concept the asset teaches
  • Audience segment: Role, technical level, and company type (e.g., admin vs. end user; technical vs. non-technical)
  • Lifecycle stage: Onboarding / adoption / expansion / retention / renewal
  • Desired outcome: What the learner should be able to do after consuming this
  • Distribution channel: Where it lives (knowledge base, in-app, academy, email, YouTube)
  • Constraints: Length, format requirements, brand/tone rules, or existing content to reference

If lifecycle stage or audience technical level is missing, infer from the topic and note the assumption explicitly at the top of the brief.

Instructions

Step 1: Frame the Learner Context

Establish who this is for and where they are before defining what to teach.

  • Audience profile: One-line description combining role + technical level + likely emotional state (confused, evaluating, blocked, curious)
  • Lifecycle mapping: Tie the asset to a stage and name the job-to-be-done at that stage:
  • Onboarding → "get to first value fast" — bias toward guided, step-by-step
  • Adoption → "use it correctly and often" — bias toward best practices and use cases
  • Expansion → "discover what else is possible" — bias toward advanced/adjacent features
  • Retention/Renewal → "prove ongoing value" — bias toward outcomes and ROI framing
  • Prior knowledge assumed: What the learner already knows (so the brief doesn't over- or under-explain)

Step 2: Write Learning Objectives

Define 2–4 objectives using measurable, behavior-based verbs (not "understand" or "learn about").

  • Format each as: "After this [asset], the learner will be able to [action verb] [specific task/outcome]."
  • Use verbs like configure, complete, troubleshoot, choose between, set up, interpret, apply
  • Rank objectives primary → secondary so the writer knows what to protect if length gets cut

Step 3: Build the Content Outline

Produce a section-by-section skeleton matched to the asset type:

  • Article: Hook/problem → prerequisites → numbered steps or concept sections → common pitfalls → next steps
  • Video script: Cold open/hook (first 5 sec) → what you'll learn → demonstration beats → recap → CTA
  • Course module: Intro + objectives → concept explanation → guided walkthrough → practice/knowledge check → summary For each section, include a one-line note on what it must cover and its approximate weight (% of total length or runtime).

Step 4: Specify Format and Production Notes

Give the producer everything needed to execute:

  • Visual/media requirements: Screenshots, GIFs, diagrams, screen recording, or annotations needed
  • Tone and reading level: Match to audience (e.g., "plain-language, 8th-grade reading level, no jargon" or "technical, admin-appropriate")
  • Internal links / related content: What to cross-link to
  • CTA: The single next action the learner should take

Step 5: Define Success Metrics

Name how this asset will be judged post-publish:

  • Primary metric: Tied to lifecycle stage (e.g., onboarding → time-to-first-value; retention → deflection rate / feature adoption lift)
  • Engagement signals: Views, completion rate, video retention curve, or article dwell time
  • Feedback loop: Helpful/not-helpful votes, support ticket deflection, or in-product survey State a realistic target where a benchmark exists; otherwise mark as "establish baseline."

Step 6: Add SME and Reviewer Notes

Close gaps before writing starts:

  • Open questions for SME: Specific product/technical points needing expert confirmation
  • Reviewers required: Who must sign off (product, support, legal, brand)
  • Known risks: Edge cases, deprecated flows, or features likely to change soon

Output Format

  • Structure: Single structured brief document with clear labeled sections; outline presented as a nested list; metrics and reviewer notes as scannable bullets
  • Length: 500–800 words
  • Format: Structured document (headers + bullets), not prose paragraphs
  • Sections: Brief header (asset type, topic, owner, assumptions) → Learner Context → Learning Objectives → Content Outline → Format & Production Notes → Success Metrics → SME & Reviewer Notes
  • Tone: Operational and directive — written for a busy writer or producer who wants clarity, not narrative

Quality Checklist

Before delivering output, verify:

  • [ ] Every learning objective uses a measurable behavior verb — zero instances of "understand" or "learn about"
  • [ ] The outline is matched to the specific asset type (article/video/module) — not a generic structure
  • [ ] Lifecycle stage is named and the job-to-be-done for that stage is reflected in the outline's bias
  • [ ] At least one primary success metric is tied to the lifecycle stage, not just generic views
  • [ ] SME open questions are specific to the feature — not placeholder prompts
  • [ ] Any inferred inputs (audience level, lifecycle stage) are flagged as assumptions at the top

Notes

  • The single most common failure is objectives written as topics ("configuring SSO") instead of behaviors ("the learner will be able to configure SSO for a new identity provider") — enforce the verb rule strictly.
  • Match content depth to lifecycle stage: onboarding assets should ruthlessly cut everything not needed to reach first value; expansion assets can afford more context.
  • If the same feature needs coverage across multiple lifecycle stages, produce one brief per stage rather than one asset trying to serve everyone — a common scoping mistake.
  • The brief header's "assumptions" line is what protects the writer when inputs were incomplete — never skip it.

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

  • ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
  • <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills