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10 changes: 5 additions & 5 deletions tutorials/README.md
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Expand Up @@ -17,26 +17,26 @@ Use this guide to navigate all tutorial tracks, understand structure rules, and
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| Tutorial directories | 191 |
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| Tutorial directories | 191 |
| Tutorial markdown files | 1732 |
| Tutorial markdown lines | 1,004,205 |
| Tutorial markdown lines | 1,048,791 |
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| Tutorial directories | 191 |
| Tutorial markdown files | 1732 |
| Tutorial markdown lines | 1,004,205 |
| Tutorial markdown lines | 1,048,791 |
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| Tutorial directories | 191 |
| Tutorial markdown files | 1732 |
| Tutorial markdown lines | 1,004,205 |
| Tutorial markdown lines | 1,048,791 |

## Source Verification Snapshot

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title: "Anthropic Skills Tutorial"
nav_order: 91
has_children: true
format_version: v2
---

# Anthropic Skills Tutorial: Reusable AI Agent Capabilities
Expand All @@ -13,6 +14,16 @@ has_children: true
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Spec](https://img.shields.io/badge/Spec-agentskills.io-blue)](https://agentskills.io/specification)

## Why This Track Matters

Anthropic Skills let you package reusable, reliable behaviors for Claude agents once and deploy them across every integration point — Claude Code, Claude.ai, and the API — without re-engineering each time.

This track focuses on:
- designing skills with clear invocation boundaries and deterministic outputs
- packaging repeatable workflows using scripts, references, and asset files
- publishing versioned skills for team or public reuse
- operating a skills catalog with ownership and lifecycle controls

## What are Anthropic Skills?

Anthropic Skills are packaged instructions and supporting files that Claude can load for specific jobs. A skill can be lightweight (one `SKILL.md`) or operationally rich (scripts, templates, and domain references).
Expand All @@ -35,7 +46,7 @@ The official `anthropics/skills` repository demonstrates real patterns used for:
| `references/` | Source material Claude can load on demand for better answers |
| `assets/` | Non-text files required by the workflow |

## Tutorial Structure
## Chapter Guide

| Chapter | Topic | What You Will Learn |
|:--------|:------|:--------------------|
Expand Down Expand Up @@ -110,11 +121,24 @@ Ready to begin? Start with [Chapter 1: Getting Started](01-getting-started.md).
7. [Chapter 7: Publishing and Sharing](07-publishing-sharing.md)
8. [Chapter 8: Real-World Examples](08-real-world-examples.md)

## Current Snapshot (auto-updated)

- repository: [anthropics/skills](https://github.com/anthropics/skills)
- stars: about **1.2K**
- project positioning: official reference implementation for the Agent Skills format specification

## What You Will Learn

- how to design and structure a SKILL.md file with frontmatter and behavioral contracts
- how to compose multi-file skills with scripts, references, and asset directories
- how to integrate skills across Claude Code, Claude.ai, and the Claude API
- how to version, publish, and maintain skills catalogs for team-wide reuse

## Source References

- [anthropics/skills repository](https://github.com/anthropics/skills)

## Concept Flow
## Mental Model

```mermaid
flowchart TD
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Expand Up @@ -493,3 +493,97 @@ Suggested trace strategy:
- [Next Chapter: Chapter 2: Datascript Deep Dive](02-datascript-database.md)
- [Main Catalog](../../README.md#-tutorial-catalog)
- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)

## Depth Expansion Playbook

<!-- depth-expansion-v2 -->

This chapter is expanded to v1-style depth for production-grade learning and implementation quality.

### Strategic Context

- tutorial: **Athens Research: Deep Dive Tutorial**
- tutorial slug: **athens-research-knowledge-graph**
- chapter focus: **Chapter 1: System Overview**
- system context: **Athens Research Knowledge Graph**
- objective: move from surface-level usage to repeatable engineering operation

### Architecture Decomposition

1. Define the runtime boundary for `Chapter 1: System Overview`.
2. Separate control-plane decisions from data-plane execution.
3. Capture input contracts, transformation points, and output contracts.
4. Trace state transitions across request lifecycle stages.
5. Identify extension hooks and policy interception points.
6. Map ownership boundaries for team and automation workflows.
7. Specify rollback and recovery paths for unsafe changes.
8. Track observability signals for correctness, latency, and cost.

### Operator Decision Matrix

| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
|:--------------|:--------------|:------------------|:---------|
| Runtime mode | managed defaults | explicit policy config | speed vs control |
| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
| Rollout method | manual change | staged + canary rollout | effort vs safety |
| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |

### Failure Modes and Countermeasures

| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
|:-------------|:-------------|:-------------------|:---------------|
| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |

### Implementation Runbook

1. Establish a reproducible baseline environment.
2. Capture chapter-specific success criteria before changes.
3. Implement minimal viable path with explicit interfaces.
4. Add observability before expanding feature scope.
5. Run deterministic tests for happy-path behavior.
6. Inject failure scenarios for negative-path validation.
7. Compare output quality against baseline snapshots.
8. Promote through staged environments with rollback gates.
9. Record operational lessons in release notes.

### Quality Gate Checklist

- [ ] chapter-level assumptions are explicit and testable
- [ ] API/tool boundaries are documented with input/output examples
- [ ] failure handling includes retry, timeout, and fallback policy
- [ ] security controls include auth scopes and secret rotation plans
- [ ] observability includes logs, metrics, traces, and alert thresholds
- [ ] deployment guidance includes canary and rollback paths
- [ ] docs include links to upstream sources and related tracks
- [ ] post-release verification confirms expected behavior under load

### Source Alignment

- [Athens Research](https://github.com/athensresearch/athens)
- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)

### Cross-Tutorial Connection Map

- Related tutorials are listed in this tutorial index.

### Advanced Practice Exercises

1. Build a minimal end-to-end implementation for `Chapter 1: System Overview`.
2. Add instrumentation and measure baseline latency and error rate.
3. Introduce one controlled failure and confirm graceful recovery.
4. Add policy constraints and verify they are enforced consistently.
5. Run a staged rollout and document rollback decision criteria.

### Review Questions

1. Which execution boundary matters most for this chapter and why?
2. What signal detects regressions earliest in your environment?
3. What tradeoff did you make between delivery speed and governance?
4. How would you recover from the highest-impact failure mode?
5. What must be automated before scaling to team-wide adoption?
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