Turn a repeatable AI prompt or workflow into a structured, publish-ready skill for list.affitor.com. Use this skill when the user wants to create a new skill, write a SKILL.md, convert a prompt to a skill, publish a skill to the directory, or document an AI workflow. Also trigger for: "create a skill", "write a skill", "make this a skill", "turn this into a skill", "publish skill", "add skill to list", "write SKILL.md", "skill from prompt", "document this workflow", "package this as a skill".
Installation
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Turn a repeatable AI prompt or workflow into a structured, publish-ready skill for list.affitor.com. Use this skill when the user wants to create a new skill, write a SKILL.md, convert a prompt to a skill, publish a skill to the directory, or document an AI workflow. Also trigger for: "create a skill", "write a skill", "make this a skill", "turn this into a skill", "publish skill", "add skill to list", "write SKILL.md", "skill from prompt", "document this workflow", "package this as a skill".
Turn a repeatable AI prompt or workflow into a structured, publish-ready skill for
list.affitor.com. The output is a complete SKILL.md file
that works in any AI agent — plus the listing fields to publish it on LIST.
Stage
This skill belongs to Stage S8: Meta
When to Use
User has a prompt they keep reusing and wants to turn it into a shareable skill
User wants to create a new skill for the Affitor skills directory
User wants to write a SKILL.md file in the standard format
User says "make this a skill" or "write a skill for X"
User wants to package an AI workflow so others can replicate it
Input Schema
{
raw_prompt: string # (required) The prompt, workflow description, or detailed explanation of what the skill does
failure_modes: string # (optional) What goes wrong when the output is bad — helps write better Instructions and Error Handling
niche: string # (optional) Category hint, e.g., "content", "research", "seo"
examples: string # (optional) Example input/output pairs the user already has
}
Workflow
Step 1: Understand What the Prompt Actually Does
Before writing anything, analyze the user's raw prompt or workflow description:
Task type — Is this content creation, research, analysis, planning, automation, or something else?
Variable inputs — What changes each time? (product name, URL, audience, topic, etc.)
Fixed structure — What stays the same? (output format, sections, tone, constraints)
Quality differentiator — What makes a good output vs. a bad one?
Failure modes — Where does the AI tend to go wrong without explicit guidance?
If the user gave a vague description instead of an actual prompt, ask:
"What do you typically paste into ChatGPT/Claude for this?"
"What does the output look like when it works well?"
"What goes wrong when it doesn't?"
If the user says "just do it", infer from context and proceed.
Step 2: Determine Skill Metadata
Based on the analysis, determine:
Field
How to decide
name
Short, action-oriented. "Comparison Post Writer" not "A Skill for Writing Comparison Posts"
slug
kebab-case of name, e.g., comparison-post-writer
category
One of: research, content, seo, landing, distribution, analytics, automation, meta
What external tools the skill needs: web_search, web_fetch, code_execution, none
Step 3: Write the SKILL.md
Create a complete SKILL.md following this exact structure. Every section is required.
Frontmatter (YAML)
---name: [slug]
description:>
[2-3 lines. First line: what it does. Second line: trigger phrases.
This is used for skill discovery — be specific about use cases.]
license:MITversion:"1.0.0"tags: [relevanttags]
compatibility:"Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"metadata:author: [userhandleor"affitor"]
version:"1.0"stage: [S1-S8]
---
Title and Introduction
One paragraph. What the skill does and what makes the output reliable. No marketing speak.
When to Use
3-5 specific trigger scenarios. "Writing a blog post" is too vague. "You need to publish a comparison post for two competing SaaS tools this week" is useful.
Input Schema
Typed definition of every variable input. Mark required vs optional.
Workflow (numbered steps)
This is the core. Each step must be concrete enough that any AI model produces consistent output:
Action — what to do
Approach — how to do it specifically
Quality bar — what good looks like
Bad: "3. Write the pros and cons"
Good: "3. Write at least 3 pros and 2 cons. Each must reference a specific feature, not a vague category. 'Exports to 12 formats including PDF and DOCX' not 'Great export options'."
Output Schema
Typed fields that other skills can consume via conversation context. Include output_schema_version: "1.0.0".
Output Format
A markdown code block showing the exact template with [placeholder] brackets. This is the single most important section for consistency.
Error Handling
3-5 named failure modes with specific recovery behavior. What happens when input is missing, ambiguous, or the task can't be completed?
Examples
2-3 concrete examples showing:
User input
Key decisions made during the workflow
What the output looks like (excerpt, not full)
Flywheel Connections
Feeds Into: which skills consume this skill's output
Fed By: which skills produce input for this skill
Feedback Loop: how community engagement improves the skill
chain_metadata YAML block with skill_slug, stage, timestamp, suggested_next
Quality Gate
5-7 numbered checklist items that must all pass before the output is delivered. These are the self-validation checks the AI runs silently.
References
Links to supplementary reference files if applicable.
Step 4: Write the LIST Description
Separately from the SKILL.md, write a community-facing description for the listing on list.affitor.com. This is what people see in the feed — it sells the skill, not documents it.
Structure:
Opening (2 sentences) — what the skill does, who it's for
When to Use (3 bullets) — specific scenarios
What Makes It Different (brief) — why this skill vs. just prompting
Instructions summary — condensed version of the workflow
Input Required — what the user needs to provide
Output Format — what the skill produces (show template)
Example — one concrete input/output
Tips (3-5) — practical advice for getting the best results
This is NOT the SKILL.md content — it's a human-friendly summary for discovery.
Step 5: Assemble Output
Present two clearly separated outputs:
SKILL.md — the full file, ready to save to skills/{stage}/{slug}/SKILL.md
LIST Submission — listing fields + description for list.affitor.com
Step 6: Self-Validation
Before presenting output, verify:
SKILL.md has all required sections (frontmatter, intro, when-to-use, input schema, workflow, output schema, output format, error handling, examples, flywheel, quality gate)
Every workflow step has action + approach + quality bar
Output Format uses a code block with [placeholder] brackets
At least 2 examples with concrete input/output
Error handling covers realistic failure modes, not hypothetical ones
Quality gate items are testable (not "make sure it's good")
Description is specific enough that someone knows if it's relevant before clicking
Frontmatter name matches the slug exactly
Output Schema
Other skills consume these fields from conversation context:
---
name: [slug]
description: >
[2-3 lines describing the skill and trigger phrases]
license: MIT
version: "1.0.0"
tags: [[tags]]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
author: [author]
version: "1.0"
stage: [S1-S8 stage]
---
# [Skill Name]
[1 paragraph intro]
## Stage
This skill belongs to Stage [S1-S8]: [Stage Name]
## When to Use
- [Scenario 1]
- [Scenario 2]
- [Scenario 3]
## Input Schema
[typed input definition]
## Workflow
### Step 1: [Action]
[Instructions with approach and quality bar]
### Step 2: [Action]
[Instructions]
...
## Output Schema
[typed output definition with output_schema_version]
## Output Format
[code block template with [placeholders]]
## Error Handling
- **[Failure mode 1]:** [Recovery behavior]
- **[Failure mode 2]:** [Recovery behavior]
## Examples
**Example 1: [Scenario]**
[Input, decisions, output excerpt]
**Example 2: [Scenario]**
[Input, decisions, output excerpt]
## Flywheel Connections
### Feeds Into
- [skill] ([stage]) — [how]
### Fed By
- [skill] ([stage]) — [how]
### Feedback Loop
[How community engagement improves this skill]
chain_metadata YAML block
## Quality Gate
1. [Testable check]
2. [Testable check]
...
## References
- [reference files if applicable]
Output 2: LIST Submission Fields
## Listing Fields (for list.affitor.com)
| Field | Value |
|-------|-------|
| Name | [Skill Name] |
| Slug | [slug] |
| Category | [category] |
| Level | [level] |
| Tags | [tag1, tag2, tag3] |
---
## Description (paste into the description field on LIST)
[Community-facing description — see Step 4]
Error Handling
User gives a vague description instead of a prompt: Ask for the actual prompt they paste into AI, or a concrete example of input and expected output. If they say "just figure it out", do your best but flag that the skill may need iteration.
Prompt is too simple for a skill: If the prompt is a single sentence with no variable inputs (e.g., "write me a joke"), tell the user this doesn't need to be a skill — it's already a prompt. Skills add value when there are variable inputs, structured outputs, and quality concerns.
Prompt does too many things: If the workflow has 10+ distinct steps covering different domains, suggest splitting into 2-3 focused skills that chain together via flywheel connections.
No clear output format: If the user can't describe what good output looks like, ask for 1-2 examples. Build the Output Format section from those examples.
User wants to copy an existing skill: Check if a similar skill already exists on list.affitor.com or in the affiliate-skills repo. If so, suggest improving the existing one rather than creating a duplicate.
Examples
Example 1: Converting a reused ChatGPT prompt
User: "I always ask ChatGPT to write me a comparison blog post about two SaaS tools for my affiliate site. Sometimes it's great, sometimes it writes a generic listicle."
Analysis:
Task: content creation (comparison post)
Variable inputs: product A, product B, niche
Fixed structure: comparison table, pros/cons, recommendation with affiliate links
Failure mode: generic listicle instead of specific comparison
Stage: S3-Blog, Category: seo
Output: SKILL.md with comparison-post-writer slug, detailed workflow for researching both products, building comparison tables, writing specific pros/cons, and a quality gate that checks for concrete feature comparisons (not generic praise).
Example 2: Packaging a workflow that spans multiple tools
User: "I have a workflow where I research a product on G2, check their pricing page, then write a review post with SEO keywords. Can you make this a skill?"
Analysis:
Task: research + content creation (review post)
Variable inputs: product name, target keywords
Tools needed: web_search, web_fetch
Fixed structure: G2 data extraction, pricing analysis, SEO-optimized review
Stage: S3-Blog, Category: seo, Level: intermediate
Output: SKILL.md with product-review-writer slug, workflow that explicitly separates research (Steps 1-3) from writing (Steps 4-6), output schema that passes review data to downstream skills, and error handling for when G2 listings don't exist.
Example 3: Skill that's too simple
User: "Make a skill that writes Twitter threads"
Response: Ask clarifying questions — "What kind of Twitter threads? About a product you're promoting? Summarizing a blog post? Sharing tips in a niche? The skill needs a specific use case to produce consistent output. 'Write a Twitter thread' is too broad — the AI will produce different things every time."
Revenue & Action Plan
Expected Outcomes
Revenue potential: Published skills build your authority as an affiliate marketing expert. Each skill on list.affitor.com acts as a lead magnet — users discover your skill, star it, and follow your profile. Profile visibility → traffic to your affiliate content. Top skill creators report 500-2,000 monthly profile views from popular skills
Benchmark: Skills with 10+ stars get featured in recommendations and search results, driving compounding traffic. The first 5 stars are the hardest — after that, momentum takes over
Key metric to track: Stars + installs on list.affitor.com. Secondary: profile views and click-through to your other content
Do This Right Now (15 min)
Submit the skill to list.affitor.com — use the LIST Submission fields from the output
Share the skill on 2-3 platforms (X, LinkedIn, Reddit r/ChatGPT) with a brief demo of what it does
Add the skill to your bio link page — it's a portfolio piece that builds authority
Star your own skill and ask 2-3 colleagues to try it and leave feedback
Track Your Results
After 7 days: how many stars? After 30 days: how many installs? If stars are growing, create more skills in related areas to build a skill portfolio. Your skills become your brand — people trust affiliates who create useful tools.
Next step — copy-paste this prompt:
"Find ways to improve my skill based on usage feedback" → runs self-improver
Flywheel Connections
Feeds Into
skill-finder (S8) — newly created skills appear in the directory for discovery
Community requests on list.affitor.com — "I wish there was a skill for X"
Feedback Loop
Skills with high star counts on list.affitor.com reveal which formats and structures
resonate most. Low-engagement skills get revised. The Quality Gate evolves based on
which checks correlate with high-quality, high-engagement skill output.
