Generate optimized instructions for Claude (Project instructions, Skills, or standalone prompts). Use when users request creating project setups, writing effective prompts, building Skills, or need guidance on instruction types for Claude.ai.
npx skills add https://github.com/oaustegard/claude-skills --skill crafting-instructions
Generate technically optimized instructions for Claude.ai across three formats: Project instructions, Skills, and standalone prompts.
Ask these questions to determine the right format:
Signals: "for this project", "all conversations about X", "team workspace", "project-specific"
Signals: "every time I", "whenever", "reusable", "across projects", "teach Claude how to"
Signals: "for this task", "right now", "just this once", "can you"
Project + Skill:
Skill + Prompt:
These apply to ALL instruction formats:
Frame as direct action commands, not suggestions:
State WHAT to do, not what NOT to do:
WHY: Negative instructions force inference. Positive instructions state desired behavior directly.
Explain WHY requirements exist:
WHY: Context helps Claude make better autonomous decisions in edge cases.
Provide goals and decision frameworks, not step-by-step procedures:
Model-aware calibration:
Claude's system prompt already covers:
ONLY specify project/domain-specific deviations.
See: references/project-instructions.md
Key points:
See: references/creating-skills.md
Key points:
See: references/standalone-prompts.md
Key points:
Read: references/skill-vs-project.md for detailed comparison
Quick reference:
Project = "Here's what you need to know"
Skill = "Here's how to do things"
Example:
Use both together for powerful combinations.
CRITICAL for Claude 4.x: Examples teach ALL patterns, including unintended ones.
When including examples:
Model-aware calibration:
Default to clear organization:
Use structured markup (XML/JSON) only when:
Extended thinking is UI toggle, not phrase-controlled.
In instructions, you CAN:
Pattern:
For tasks involving [specific complexity], suggest enabling Extended
thinking, explaining briefly why it would help for THIS task.
Match instruction complexity to task needs:
Simple task → Simple prompt or brief instructions
Medium task → Structured guidance with decision frameworks
Complex task → Comprehensive instructions + suggest extended thinking
Before adding complexity: Could simpler formulation work equally well?
When crafting instructions, consider which model will execute them:
For Sonnet-executed instructions:
For Opus-executed instructions:
Instruction density heuristic:
When uncertain: Instructions optimized for Opus will still work with Sonnet (just less perfectly). Instructions over-optimized for Sonnet may constrain Opus unnecessarily.
Before delivering instructions:
Strategic:
Technical:
Execution:
❌ System prompt duplication - "Use web_search for current info, cite sources"
✅ Omit unless project has SPECIFIC deviations
❌ Negative framing - "Don't use lists, never be verbose"
✅ "Present in natural prose paragraphs"
❌ Fake thinking triggers - "Use 'think carefully' for deep thinking"
✅ "Suggest Extended thinking toggle for [specific complexity]"
❌ Procedural micromanagement - "Step 1: X, Step 2: Y..."
✅ "Goal: X. Quality standard: Y. Approach: Z."
❌ Contextless requirements - "Always use formal tone"
✅ "Use formal tone for professional docs because recipients expect authoritative voice"
❌ Imperfect examples - Example uses bullets when you want prose
✅ Either create perfect examples or omit entirely
Use when creating new skills, editing existing skills, or verifying skills work before deployment
Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.
Convert abstract edge concepts into strategy draft variants and optional exportable ticket YAMLs for edge-candidate-agent export/validation.
INVOKE THIS SKILL when writing ANY LangGraph code. Covers StateGraph, state schemas, nodes, edges, Command, Send, invoke, streaming, and error handling.
Analyze the protocol layer between agent harness and LLM model. Use when (1) understanding message wire formats and API contracts, (2) examining tool call encoding/decoding mechanisms, (3) evaluating streaming protocols and partial response handling, (4) identifying agentic chat primitives (system prompts, scratchpads, interrupts), (5) comparing multi-provider abstraction strategies, or (6) understanding how frameworks translate between native LLM APIs and internal representations.
Translate SKILL.md and README.md files into multiple languages for sharing skills internationally
| Shared workflow for editing Langfuse's repo-owned agent setup under `.agents/`. Use when changing AGENTS files, shared skills, `.agents/config.json`, generated shim behavior, provider discovery paths, or install-time agent sync.
>- Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).
Take oaustegard/crafting-instructions from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.