prompt-engineering
Skill2 starsChanged 18 days agoMITView on GitHub
Expert prompt engineering guidance with proven techniques and anti-patterns. Invoke this skill whenever writing, editing, reviewing, or refining any prompt that will be interpreted by an AI model — system prompts, agent instructions, tool-use prompts, command templates, multi-step task prompts, or agentic workflows. Use it even when the task doesn't explicitly mention 'prompt engineering' — if the output is instructions for an AI, this skill makes them significantly better. Trigger for both user-requested prompt work and when autonomously generating prompts as part of a larger task (e.g. creating skills, writing agent instructions, drafting review templates).
85 other projects use a version of this.
Adopted by
Most recently taken up by
- ishandutta2007/Awesome-Claude-Skillstheir own version, first seen Nov 8, 2026
- kazemcodes/skill-treetheir own version, first seen Nov 8, 2026
- MMACABRE/SKILLStheir own version, first seen Nov 8, 2026
- Hoerire/Azurtheir own version, first seen Nov 8, 2026
- Bruno-Cunha-Souza/ValarMindSkillstheir own version, first seen Nov 8, 2026
- LuisSambrano/antigravity-configtheir own version, first seen Nov 8, 2026
Used with
Projects with it also use security-checklist, multi-agent-orchestration and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
atuljha23Skillsecurity-checklist
OWASP-style quick reference — authn/authz, injection, secrets, sessions, crypto, dependencies. Use during code review and before committing security-sensitive changes.
Also in 3 projects that use this
javalenciacaiSkillmulti-agent-orchestration
Design and coordinate multi-agent systems where specialized agents work together to solve complex problems. Covers agent communication, task delegation, workflow orchestration, and result aggregation. Use when building coordinated agent teams, complex workflows, or systems requiring specialized expertise across domains.
Also in 4 projects that use this
xfstudioSkillllm-evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Also in 3 projects that use this
GoogleSkillcoding-principles
Enforces engineering, architecture, public API, and code quality standards distilled from human reviewer feedback across A2UI pull requests. Use when authoring new code, refactoring, addressing review feedback, or reviewing PR diffs. Don't use for release publishing or spec blueprint generation without code or PR changes.
Also in 3 projects that use this
pproencaSkillrefactoring
Code refactoring best practices based on Martin Fowler's catalog and Clean Code principles. This skill should be used when refactoring existing code, improving code structure, reducing complexity, eliminating code smells, or reviewing code for maintainability. Triggers on tasks involving extract method, rename, decompose conditional, reduce coupling, or improve readability.
Also in 6 projects that use this
mjunaidcaSkilltool-design
Design tools that agents can use effectively. Use when creating new tools for agents, debugging tool-related failures, or optimizing existing tool sets.
Also in 3 projects that use this
Other versions85 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 85
- trycompai/comp★ 2.1ksame name
- niki914/zafiro★ 346same name
- CodeAlive-AI/ai-driven-development★ 169same name
- grandamenium/cortextos★ 106same name
- nahid-sparktales/agent-dispatcher★ 59same name
- geekatron/jerry★ 37same name
- Bbar0n234/learnflow-ai★ 35same name
- nimadorostkar/Claude-Skills-collection★ 33same name
- sabahattink/antigravity-fullstack-hq★ 30same name
- VoDaiLocz/kilo-kit-mcp★ 28same name
75 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository9 other files it gives its agents.
- .claude/CLAUDE.mdCLAUDE.md
- agent-harnessSkill
- grill-with-docsSkill
- improve-codebase-architectureSkill
- learnSkill
- plugin-browserSkill
- py-debugSkill
- tddSkill
- ts-debugSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 75 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 28
Use {{variables}} for dynamic content to make prompts reusable and testable
- Very specific: line 30
When adding examples (few-shot): use 3–5 diverse examples wrapped in <example> tags.
- Distinctive: line 34
Run through this when reviewing or debugging a prompt:
- Distinctive: line 57
Use structured formats (JSON) for status tracking, freeform text for progress notes
“Common” is measured: kept by at least 6 in 10 of the 75 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Prompt Engineering
Start here: what are you doing?
| Task | Go to |
| Writing a prompt from scratch | Crafting prompts |
| Reviewing or debugging an existing prompt | Prompt review checklist |
| Building an agentic system or multi-step workflow | Agentic prompt patterns |
| Need deeper detail on a specific technique | Going deeper — pick a source doc and WebFetch it |
Crafting prompts
The one principle that matters most: explain why, not just what. Claude reasons better with context than rigid rules. Instead of NEVER use ellipses, write Your response will be read aloud by a text-to-speech engine, so never use ellipses since the TTS engine can't pronounce them. Claude generalizes from the reasoning.
Structure for any non-trivial prompt:
- Role/context — who is Claude, what's the situation
- Input — wrapped in descriptive XML tags (
<code>,<document>,<query>) - Task — what to do, with success criteria
- Output format — show one concrete example rather than describing the format in prose
- Use
{{variables}}for dynamic content to make prompts reusable and testable
When adding examples (few-shot): use 3–5 diverse examples wrapped in <example> tags. Cover edge cases. Ask yourself: would a pattern-matcher pick up unintended shortcuts from these examples?
Prompt review checklist
Run through this when reviewing or debugging a prompt:
- Over-constraining? — Too many MUST/NEVER/ALWAYS makes prompts brittle. If you see a wall of rigid rules, rewrite them as reasoned guidance. One explained rationale outperforms five shouted commands.
- Kitchen-sink? — If the prompt handles discovery, analysis, and output in one block, break it into a chain. Each stage should have a clear input and output.
- Missing examples? — One concrete input/output example beats a paragraph of format description. If the prompt describes a format without showing it, add an example.
- Telling what NOT to do? — Reframe as positive instructions. "Don't use markdown" → "Write in flowing prose paragraphs." Claude steers better toward a destination than away from one.
- Leftover aggressive language from older models? — Phrases like "CRITICAL: You MUST use this tool" or "If in doubt, use [tool]" cause overtriggering on current Claude models. Dial back to normal phrasing: "Use this tool when..."
- Prefills on the last assistant turn? — Deprecated in Claude 4.6. Use direct instructions, structured outputs, or XML output tags instead. See migration patterns in the prompting best-practices doc listed under Going deeper.
- Vague action language? — "Can you suggest changes?" makes Claude suggest rather than act. "Change this function to..." makes it act. Be explicit about whether you want analysis or action.
Agentic prompt patterns
These are the non-obvious patterns from the official docs — things Claude wouldn't do by default without guidance.
Autonomy/safety balance — Without guidance, agents may take hard-to-reverse actions (deleting files, force-pushing, posting externally). Add a principle like: "Take local, reversible actions freely. For destructive or externally-visible actions, confirm first." List specific examples of what warrants confirmation.
Subagent orchestration — Current Claude models proactively spawn subagents. Watch for overuse: the model may spawn a subagent for a task that a single grep could handle. Add guidance: "Use subagents for parallel, independent workstreams. For simple tasks, sequential operations, or single-file edits, work directly."
Overthinking and over-exploration — Current models do significantly more upfront exploration than predecessors. If your prompt previously encouraged thoroughness ("always explore before acting", "when in doubt, research more"), dial it back. Replace blanket defaults ("Default to using [tool]") with targeted ones ("Use [tool] when it would enhance your understanding").
Long-horizon state tracking — For tasks spanning multiple context windows:
- First window: set up framework (tests, scripts, structured state files)
- Subsequent windows: iterate on a todo-list, discover state from filesystem and git
- Use structured formats (JSON) for status tracking, freeform text for progress notes
- Encourage git usage for state checkpoints
Overeagerness / overengineering — Current models tend to create extra files, add unnecessary abstractions, or build in unrequested flexibility. Counter with: "Only make changes that are directly requested or clearly necessary. The right amount of complexity is the minimum needed for the current task."
Minimizing hallucinations in agentic coding — Add: "Never speculate about code you haven't read. If a file is referenced, read it before answering."
Going deeper
For technique-specific detail, consult references/reference.md — it lists the source URLs to WebFetch, with a one-line description of what each doc covers. Pick the doc that matches the topic (general principles and migration, when-to-prompt-engineer vs. switch models, or Console tooling) and fetch only that one.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

