# 102 Best Practices E1c528d6

> Agent Skills Authoring Best Practices

- Skill: `tools-only/102-best-practices-e1c528d6` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/102-best-practices-e1c528d6`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/102-best-practices-e1c528d6/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/102-best-practices-e1c528d6

---

# Agent Skills Authoring Best Practices

> Source: <https://platform.claude.com/docs/en/agents-and-tools/agent-skills/best-practices>

This document covers practical authoring guidance for writing effective skills that agents can discover and use successfully.

---

## Contents

- [Core principles](#core-principles)
- [Skill structure](#skill-structure)
- [Workflows and feedback loops](#workflows-and-feedback-loops)
- [Content guidelines](#content-guidelines)
- [Common patterns](#common-patterns)
- [Evaluation and iteration](#evaluation-and-iteration)
- [Advanced: executable code](#advanced-executable-code)
- [Checklist for effective skills](#checklist-for-effective-skills)

---

## Core Principles

### Concise Is Key

The context window is a public good. **Include only what the agent doesn't already know.** Prefer short examples over long explanations.

Apply these heuristics when drafting content:
- Include only skill-specific information — omit general concepts the agent already knows
- Each paragraph should justify its token cost with actionable guidance
- When in doubt, use a compact example instead of prose

**Good** (~50 tokens):

````markdown
## Extract PDF text

Use pdfplumber for text extraction:

```python
import pdfplumber
with pdfplumber.open("file.pdf") as pdf:
    text = pdf.pages[0].extract_text()
```
````

**Bad** (~150 tokens):

````markdown
## Extract PDF text

PDF (Portable Document Format) files are a common file format that contains
text, images, and other content. To extract text from a PDF, you'll need to
use a library. There are many libraries available...
````

### Set Appropriate Degrees of Freedom

Match specificity to the task's fragility:

**High freedom** (text-based instructions) — Multiple approaches valid, decisions depend on context:

```markdown
## Code review process
1. Analyze the code structure and organization
2. Check for potential bugs or edge cases
3. Suggest improvements for readability
4. Verify adherence to project conventions
```

**Medium freedom** (pseudocode/scripts with parameters) — Preferred pattern exists, some variation acceptable:

```python
def generate_report(data, format="markdown", include_charts=True):
    # Process data
    # Generate output in specified format
```

**Low freedom** (specific scripts, few parameters) — Operations fragile, consistency critical:

````markdown
## Database migration

Run exactly this script:
```bash
python scripts/migrate.py --verify --backup
```
Do not modify the command or add additional flags.
````

**Analogy:** Narrow bridge with cliffs = low freedom (exact instructions). Open field = high freedom (general direction).

### Test with All Target Models

- **Haiku** (fast): Does the skill provide enough guidance?
- **Sonnet** (balanced): Is the skill clear and efficient?
- **Opus** (powerful): Does the skill avoid over-explaining?

---

## Skill Structure

### Writing Effective Descriptions

The `description` field enables skill discovery. Include both what the skill does and when to use it.

**Write in third person.** The description is injected into the system prompt.

- Prefer: "Processes Excel files and generates reports"
- Prefer: "Extract text from PDFs. Use when the user mentions PDFs or document extraction"
- Instead of first/second person: use "Processes…" rather than "I can help you…" or "You can use this to…"

**Examples:**

```yaml
# PDF Processing
description: Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.

# Excel Analysis
description: Analyze Excel spreadsheets, create pivot tables, generate charts. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.

# Git Commit Helper
description: Generate descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.
```

### Progressive Disclosure Patterns

Keep SKILL.md body lean. Split content into separate files when the validator warns. Run `uv run plugins/plugin-creator/scripts/plugin_validator.py <skill-path>` after writing and follow its guidance on token-based sizing.

**Pattern 1 — High-level guide with references:**

```markdown
# PDF Processing

## Quick start
[core example]

## Advanced features
- **Form filling**: See [FORMS.md](FORMS.md)
- **API reference**: See [REFERENCE.md](REFERENCE.md)
```

**Pattern 2 — Domain-specific organization:**

```
bigquery-skill/
├── SKILL.md (overview + navigation)
└── reference/
    ├── finance.md
    ├── sales.md
    └── product.md
```

**Pattern 3 — Conditional details:**

```markdown
For simple edits, modify XML directly.
**For tracked changes**: See [REDLINING.md](REDLINING.md)
```

### Keep References One Level Deep

Link each reference file directly from SKILL.md so the agent can discover content without following long chains.

**Prefer** (one level):

```
SKILL.md → advanced.md
SKILL.md → reference.md
SKILL.md → examples.md
```

**Instead of** (too deep):

```
SKILL.md → advanced.md → details.md → actual info
```

### Structure Longer Reference Files

For files >100 lines, include a table of contents at the top so the agent can see the full scope when previewing.

---

## Workflows and Feedback Loops

### Use Workflows for Complex Tasks

Break complex operations into clear, sequential steps. Provide a checklist the agent can track:

```markdown
## PDF form filling workflow

Task Progress:
- [ ] Step 1: Analyze the form (run analyze_form.py)
- [ ] Step 2: Create field mapping (edit fields.json)
- [ ] Step 3: Validate mapping (run validate_fields.py)
- [ ] Step 4: Fill the form (run fill_form.py)
- [ ] Step 5: Verify output (run verify_output.py)
```

### Implement Feedback Loops

Common pattern: Run validator, fix errors, repeat.

```markdown
## Document editing process

1. Make edits to `word/document.xml`
2. **Validate immediately**: `python scripts/validate.py unpacked_dir/`
3. If validation fails:
   - Review the error message
   - Fix the issues
   - Run validation again
4. **Only proceed when validation passes**
5. Rebuild: `python scripts/pack.py unpacked_dir/ output.docx`
```

---

## Content Guidelines

### Use Current Patterns; Preserve Legacy in Labeled Sections

Use current methods as the default. When documenting deprecated approaches, keep them in a clearly labeled legacy section so agents know not to prefer them:

```markdown
## Current method
Use the v2 API endpoint: `api.example.com/v2/messages`

## Old patterns
<details>
<summary>Legacy v1 API (deprecated 2025-08)</summary>
The v1 API used: `api.example.com/v1/messages`
</details>
```

### Use Consistent Terminology

Choose one term and use it throughout:

- Good: Always "API endpoint", always "field", always "extract"
- Bad: Mix "API endpoint" / "URL" / "API route" / "path"

---

## Common Patterns

### Template Pattern

**Strict requirements:**

```markdown
## Report structure

ALWAYS use this exact template:

# [Analysis Title]
## Executive summary
[One-paragraph overview]
## Key findings
- Finding 1 with data
## Recommendations
1. Specific recommendation
```

**Flexible guidance:**

```markdown
## Report structure

Sensible default format — adapt as needed:

# [Analysis Title]
## Executive summary
[Overview]
## Key findings
[Adapt sections based on discovery]
```

### Examples Pattern

Provide input/output pairs:

```markdown
## Commit message format

**Example 1:**
Input: Added user authentication with JWT tokens
Output:
feat(auth): implement JWT-based authentication
Add login endpoint and token validation middleware

**Example 2:**
Input: Fixed bug where dates displayed incorrectly
Output:
fix(reports): correct date formatting in timezone conversion
Use UTC timestamps consistently
```

### Conditional Workflow Pattern

```markdown
## Document modification

1. Determine type:
   **Creating new?** → Follow "Creation workflow"
   **Editing existing?** → Follow "Editing workflow"

2. Creation workflow:
   - Use docx-js library
   - Build from scratch
   - Export to .docx

3. Editing workflow:
   - Unpack existing document
   - Modify XML directly
   - Validate after each change
   - Repack when complete
```

---

## Evaluation and Iteration

### Build Evaluations First

Create evaluations BEFORE writing extensive documentation:

1. **Identify gaps**: Run agent on tasks without a skill. Document failures.
2. **Create evaluations**: Build three scenarios testing those gaps.
3. **Establish baseline**: Measure performance without the skill.
4. **Write minimal instructions**: Just enough to address gaps and pass evaluations.
5. **Iterate**: Execute evaluations, compare against baseline, refine.

**Evaluation structure:**

```json
{
  "skills": ["pdf-processing"],
  "query": "Extract all text from this PDF file and save it to output.txt",
  "files": ["test-files/document.pdf"],
  "expected_behavior": [
    "Successfully reads the PDF file",
    "Extracts text from all pages",
    "Saves extracted text to output.txt"
  ]
}
```

### Iterative Development with Two Instances

1. Work through a task with **Claude A** (the skill author)
2. Identify reusable patterns from the session
3. Ask Claude A to create a skill capturing those patterns
4. Test with **Claude B** (fresh instance with skill loaded)
5. Observe Claude B's behavior — note struggles or missed context
6. Return to Claude A with specifics to refine
7. Repeat the observe-refine-test cycle

---

## Advanced: Executable Code

### Solve, Don't Punt

Handle errors explicitly in scripts:

```python
def process_file(path):
    try:
        with open(path) as f:
            return f.read()
    except FileNotFoundError:
        print(f"File {path} not found, creating default")
        with open(path, "w") as f:
            f.write("")
        return ""
```

### Document Constants

```python
# HTTP requests typically complete within 30 seconds
REQUEST_TIMEOUT = 30

# Three retries balances reliability vs speed
MAX_RETRIES = 3
```

### Provide Utility Scripts

Pre-made scripts are more reliable, save tokens, save time, and ensure consistency. Make execution intent clear:

- "Run `analyze_form.py` to extract fields" (execute)
- "See `analyze_form.py` for the algorithm" (read as reference)

### Create Verifiable Intermediate Outputs

For complex tasks, use plan-validate-execute:

1. Analyze input
2. **Create plan file** (e.g., `changes.json`)
3. **Validate plan** with a script
4. Execute only if validation passes
5. Verify output

### Package Dependencies

List required packages in SKILL.md. Verify availability in the target environment.

### MCP Tool References

Use fully qualified names: `ServerName:tool_name`

```markdown
Use the BigQuery:bigquery_schema tool to retrieve table schemas.
```

---

## Checklist for Effective Skills

### Core Quality

- [ ] Description is specific and includes key terms
- [ ] Description includes both what the skill does and when to use it
- [ ] Description written in third person
- [ ] SKILL.md body passes token-count validation (run `uv run plugins/plugin-creator/scripts/plugin_validator.py <skill-path>` and follow its sizing guidance)
- [ ] Additional details in separate files (if needed)
- [ ] Current patterns as default; legacy in clearly labeled section
- [ ] Consistent terminology throughout
- [ ] Examples are concrete, not abstract
- [ ] File references one level deep
- [ ] Progressive disclosure used appropriately
- [ ] Workflows have clear steps

### Code and Scripts

- [ ] Scripts solve problems rather than punt to agent
- [ ] Error handling is explicit and helpful
- [ ] No magic constants (all values justified)
- [ ] Required packages listed and verified
- [ ] Forward slashes for paths (e.g., `scripts/helper.py`)
- [ ] Validation/verification steps for critical operations
- [ ] Feedback loops for quality-critical tasks

### Testing

- [ ] At least three evaluations created
- [ ] Tested with Haiku, Sonnet, and Opus
- [ ] Tested with real usage scenarios
- [ ] Team feedback incorporated (if applicable)

### Preferred Alternatives

| Instead of | Use |
|------------|-----|
| Windows-style paths (`scripts\helper.py`) | Forward slashes: `scripts/helper.py` (portable) |
| Too many options without a default | One preferred approach with optional alternatives |
| Assuming tools are installed | List required packages in SKILL.md; verify availability |
| Vague descriptions ("Helps with documents") | Specific descriptions with triggers: "Extracts text from PDFs. Use when…" |
| Deeply nested references (SKILL → A → B → C) | Flat structure: SKILL links directly to each reference |
| Conditional logic on dates/versions | Current method as default; legacy in labeled section |
| Mixed terminology | One term per concept throughout |