// Claude for Office – Skills
Master
Office Skills
9 modules to understand, use and create Skills in the Claude for Excel and Claude for PowerPoint plugins. Less repetition, more efficiency.
Beginner
Understand what Skills are and how to invoke them in Excel and PowerPoint.
3 modules · ~1h
Intermediate
Discover available Skills, create your own and combine them with prompts.
3 modules · ~1h30
Advanced
Write robust Skills, share them with your team and integrate them into your workflow.
3 modules · ~1h30
Anthropic Documentation
A synthesis of Anthropic's published best practices — concepts that complement this training.
Official documentation · Anthropic
Skill authoring best practices
Complete guide to writing effective Skills: structure, naming, testing, iterative evaluation, and anti-patterns to avoid.
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What this training already covers
Modules 6, 7 and 8 address writing robust prompts, team sharing, and workflow integration — topics also covered in the Anthropic documentation. The concepts below are complementary and not included in the modules.
Complementary concepts to know
1
Conciseness and token budget
Context is a shared resource. Anthropic recommends keeping the
SKILL.md body under 500 lines and moving long content (APIs, examples, reference data) into separate files loaded on demand. Claude only reads them when they become relevant — zero token cost until then.2
Degrees of freedom based on task fragility
The documentation defines three levels based on error risk:
High freedom → general text instructions (multiple valid approaches)
Medium freedom → pseudocode or parameterised scripts (preferred pattern, some variation allowed)
Low freedom → exact script to run as-is (fragile operation, critical sequence)
Analogy: narrow bridge with cliffs = precise instructions; open field = general direction.
High freedom → general text instructions (multiple valid approaches)
Medium freedom → pseudocode or parameterised scripts (preferred pattern, some variation allowed)
Low freedom → exact script to run as-is (fragile operation, critical sequence)
Analogy: narrow bridge with cliffs = precise instructions; open field = general direction.
3
Test with all target models
A Skill that works well on Claude Opus may be insufficient on Haiku (which needs more context) and over-explained for Opus (which already knows). Systematically test with each model intended for production before deploying.
4
Skill naming conventions
Anthropic recommends the gerund form for Skill names:
Technical rules: lowercase, digits and hyphens only · max 64 characters · no reserved words (
Avoid vague names:
analyzing-spreadsheets, processing-pdfs, generating-reports.Technical rules: lowercase, digits and hyphens only · max 64 characters · no reserved words (
anthropic, claude) · no XML tags.Avoid vague names:
helper, utils, data.
5
Description in third person (discovery mechanism)
The description is injected into Claude's system prompt. It must describe what the Skill does and when to use it, always in the third person.
✓ "Analyzes Excel spreadsheets and generates pivot reports. Use when working with .xlsx files."
✗ "I can help you analyze your Excel files."
A vague description ("Helps with documents") prevents Claude from selecting the right Skill among 100+ available.
✓ "Analyzes Excel spreadsheets and generates pivot reports. Use when working with .xlsx files."
✗ "I can help you analyze your Excel files."
A vague description ("Helps with documents") prevents Claude from selecting the right Skill among 100+ available.
6
Validation feedback loops
For critical or batch operations (form filling, data migration, bulk edits), plan a do → validate → fix → repeat cycle. Example: generate a JSON plan → validate with a script → apply changes → verify the output. This pattern catches errors before they propagate.
7
Iterative development with Claude A/B
The documentation recommends a dual-role approach:
Claude A (expert) → helps draft and refine the Skill
Claude B (agent) → fresh instance that uses the Skill on real tasks
Observing how Claude B navigates the Skill reveals blind spots the author can't see. Repeat the observe → refine → test cycle until stable.
Claude A (expert) → helps draft and refine the Skill
Claude B (agent) → fresh instance that uses the Skill on real tasks
Observing how Claude B navigates the Skill reveals blind spots the author can't see. Repeat the observe → refine → test cycle until stable.
Official checklist before sharing a Skill
Content quality
- Description specifies both what the Skill does AND when to invoke it
- SKILL.md body is under 500 lines
- Long details are in separate files, referenced no more than one level deep
- No time-sensitive information (dates, versions) in main instructions
- Consistent terminology throughout (one term per concept)
- Examples are concrete, not abstract
Testing
- At least 3 test scenarios written before iterating on the Skill
- Tested on Haiku, Sonnet and Opus if those models are used in production
- Tested on real files, not just the file used during design
- Feedback from at least one colleague incorporated