prompt-engineering
Skill3 starsChanged 7 months agoMITView on GitHub
Create reusable skills and agent configurations through structured interview and template generation. Covers SKILL.md authoring, agent persona design, and structured output contracts. Use when creating a new skill, designing an agent, capturing a repeatable process, or when asked to "skillify" a workflow.
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 repository14 other files it gives its agents.
- architectural-planningSkill
- browser-testingSkill
- codebase-explorationSkill
- code-reviewSkill
- code-verificationSkill
- context-summarizationSkill
- disciplined-codingSkill
- effective-tool-useSkill
- knowledge-managementSkill
- safe-autonomous-modeSkill
- security-reviewSkill
- session-handoffSkill
- systematic-debuggingSkill
- task-orchestrationSkill
The file
Show its rules and text
We split this skill into the rules it gives an agent
14 rules. 0 are common, 12 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 75 edited versions we read keep these.
12
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
- Distinctive: line 16
Before asking questions, analyze what you already know:
- Distinctive: line 26
Use structured questions. Don't over-ask for simple processes.
- Distinctive: line 35
Ask: inline (current conversation) or forked (sub-agent with own context)?
- Distinctive: line 38
Ask where to save:
- Distinctive: line 46
Should the user confirm before proceeding?
- Distinctive: line 52
Ask for gotchas or edge cases.
- Distinctive: line 93
Human checkpoint: When to pause and ask before proceeding (irreversible actions, error judgment)
- Distinctive: line 100
Keep simple skills simple — a 2-step skill doesn't need annotations on every step
- Distinctive: line 118
When designing a standalone agent (not a skill):
- Distinctive: line 124
Create an identity with domain knowledge relevant to the task.
- Distinctive: line 140
Avoid generic terms like "helper" or "assistant"
- Distinctive: line 153
When a skill or agent needs structured output, define the contract explicitly:
- Very specific: line 157
XML tags: Use <analysis>, <summary>, <result> for machine-parseable sections
- Very specific: line 171
If user-config.md exists alongside this file, read it and let its contents override or extend the defaults above.
“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
CRITICAL: Check existing project conventions before inventing new ones. Read CLAUDE.md, .claude/rules/, and existing skills to align with what's already established.
Skill Creation Workflow
Step 1: Analyze the Session/Request
Before asking questions, analyze what you already know:
- What repeatable process was performed or requested
- What the inputs/parameters are
- The distinct steps in order
- Success criteria for each step
- Where the user steered or corrected during the session
- What tools and permissions are needed
Step 2: Interview (4 Rounds)
Use structured questions. Don't over-ask for simple processes.
Round 1 — High-Level Confirmation
- Suggest a name and description. Ask to confirm or rename.
- Suggest goal(s) and specific success criteria.
Round 2 — Details
- Present the steps as a numbered list.
- Suggest arguments based on observed inputs.
- Ask: inline (current conversation) or forked (sub-agent with own context)?
- Fork for self-contained tasks without mid-process user input
- Inline when the user wants to steer mid-process
- Ask where to save:
- This repo (
.claude/skills/<name>/SKILL.md) — project-specific workflows - Personal (
~/.claude/skills/<name>/SKILL.md) — follows you across repos
- This repo (
Round 3 — Per-Step Breakdown For each major step, if not obvious:
- What does this step produce that later steps need?
- What proves this step succeeded?
- Should the user confirm before proceeding? (especially irreversible actions)
- Are any steps independent and could run in parallel?
- What are the hard constraints?
Round 4 — Final
- Confirm when the skill should be invoked. Suggest trigger phrases.
- Ask for gotchas or edge cases.
Step 3: Write the SKILL.md
Use this template:
---
name: {{skill-name}}
description: {{one-line description}}
allowed-tools:
{{list of tool permission patterns}}
when_to_use: {{detailed invocation triggers with example phrases}}
argument-hint: "{{hint showing argument placeholders}}"
arguments:
{{list of argument names}}
context: {{inline or fork — omit for inline}}
---
# {{Skill Title}}
Description of skill
## Inputs
- `$arg_name`: Description of this input
## Goal
Clearly stated goal with defined artifacts or completion criteria.
## Steps
### 1. Step Name
What to do. Be specific and actionable. Include commands when appropriate.
**Success criteria**: What proves this step is done.Per-Step Annotations
- Success criteria (REQUIRED on every step): What proves the step is done
- Execution:
Direct(default),Task agent(subagent), or[human](user does it) - Artifacts: Data this step produces that later steps need
- Human checkpoint: When to pause and ask before proceeding (irreversible actions, error judgment)
- Security constraints: For steps handling user input, external APIs, or data storage, include explicit requirements (input sanitization, output encoding, auth checks). Reference
security-reviewpatterns for checklists. - Rules: Hard constraints. User corrections from the reference session are especially useful here.
Step Structure Tips
- Steps that can run concurrently use sub-numbers: 3a, 3b
- Steps requiring the user to act get
[human]in the title - Keep simple skills simple — a 2-step skill doesn't need annotations on every step
Frontmatter Rules
allowed-tools: Minimum permissions needed — least privilege. Use patterns likeBash(gh:*), not blanketBash. Grant only what the skill actually requires.context: Only setcontext: forkfor self-contained skillswhen_to_use: CRITICAL — this tells the model when to auto-invoke. Start with "Use when..." and include trigger phrasesuser-invocable: truefor skills the user triggers explicitlydisable-model-invocation: truefor sensitive skills that shouldn't auto-trigger
Step 4: Review and Save
Show the complete SKILL.md to the user for review before writing. After saving, tell them:
- Where the skill was saved
- How to invoke it:
/{{skill-name}} [arguments] - That they can edit the SKILL.md directly to refine it
Agent Creation Workflow
When designing a standalone agent (not a skill):
1. Extract Core Intent
Identify the fundamental purpose, responsibilities, and success criteria. Check CLAUDE.md for project-specific context the agent should align with.
2. Design Expert Persona
Create an identity with domain knowledge relevant to the task. Written in second person ("You are...", "You will...").
3. Architect Instructions
The system prompt should:
- Establish clear behavioral boundaries
- Provide specific methodologies for task execution
- Anticipate edge cases
- Include concrete examples
- Define output format expectations
- Build in self-verification steps
4. Create Identifier
- Lowercase letters, numbers, and hyphens only
- 2-4 words joined by hyphens
- Clearly indicates the agent's function
- Memorable and easy to type
- Avoid generic terms like "helper" or "assistant"
Agent Output Format
{
"identifier": "descriptive-agent-name",
"whenToUse": "Use this agent when... (include example triggers)",
"systemPrompt": "Complete operational manual for the agent"
}Structured Output Contracts
When a skill or agent needs structured output, define the contract explicitly:
- JSON: Include the exact schema with field descriptions and required fields
- Markdown: Define section headers and what goes in each
- XML tags: Use
<analysis>,<summary>,<result>for machine-parseable sections
Forced structured output (guaranteed schema with tool_choice or JSON mode) eliminates format drift at zero ongoing prompt cost.
CRITICAL REMINDER: Check existing conventions first. Interview before authoring. Every skill needs when_to_use and per-step success criteria. Show the result for review before saving.
Related Skills
- Skills created here often invoke
codebase-explorationandcode-verificationas sub-workflows. task-orchestrationpatterns inform how multi-step skills coordinate work.safe-autonomous-moderisk classification informsallowed-toolsscoping.
Project Customization
If user-config.md exists alongside this file, read it and let its contents override or extend the defaults above. Common customizations:
- Team conventions for skill naming and placement
- Required frontmatter fields for the project
- Default allowed-tools patterns
- Standard step annotations required by the team
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.

