Exclude-Prompt-Data
Exclude-Prompt-Data是一款code方向的AI技能,核心价值是Write only the resulting content into files,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
Write only the resulting content into files. Never echo prompt instructions, rationale, or meta-commentary into documentation, comments, or code being produced from a prompt.
mkdir -p ./skills/exclude-prompt-data && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/exclude-prompt-data/SKILL.md -o ./skills/exclude-prompt-data/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Exclude Prompt Data
When a prompt contains instructional or contextual data used to guide a change,
that data must not appear in the file being updated. The output must reflect
only the *result* of the instruction — not the instruction itself, the
reasoning behind it, or any acknowledgment that it was applied.
Core Rule
> **Never echo prompt content into the file being changed.**
>
> Only write the outcome. Strip any meta-commentary, rationale, or framing that
> originated in the prompt.
What Counts as Prompt Data
Prompt data is any content the user provides as instruction or context rather
than as intended file content:
- Descriptions of what to add or change (`"add a --verbose flag that..."`)
- Inline rationale or motivation (`"because the old behavior caused..."`)
- References to the prompt itself (`"as requested"`, `"per the prompt"`,
`"the new feature has been added as"`)
- Meta-commentary about the update
(`"This section has been updated to reflect..."`)
- Code comments that narrate a change rather than describe the code
(`"// Added email validation as requested"`,
`"// Now validates the input per the new requirement"`)
- Structural scaffold labels used as section markers or template slots
(the word `this` in `## this Title` is scaffolding, not heading text)
What Belongs in the Output
The output file should contain only:
- The feature, fix, or content the prompt requested — written as if it always
belonged there
- Documentation or code that a reader would find useful independent of how the
change was requested
- Generic, cliche placeholder data in examples (e.g., `Jane Doe`,
`jane.doe@example.com`, `Acme Corp`, `example.com`) — never real names,
emails, domains, or organization identifiers pulled from the prompt or local
configuration
- Language formatting applied to terms in the prompt carries through to the
output — if the prompt wraps a term in backticks or uses a specific syntax
convention, follow that same convention in the output
Output Quality
The prompt's writing quality does not set the bar for the output. Regardless
of how a prompt is phrased, the result must be polished and production-ready:
- Correct grammar, capitalization, and punctuation throughout
- No draft-quality prose or casually written sections
- Informal or sloppy phrasing in the prompt must not carry into the output
Use Cases
Adding a Feature Flag to Documentation
**Prompt**
Update file.ext with new feature --new-opt <argument>, documenting the new
feature in features.md**Acceptable result — `features.md`**
### --new-opt
Enables extended output. Requires a value argument. Example:
```bash
file --new-opt foo
```**Unacceptable result — `features.md`**
### --new-opt
The new feature `--new-opt` requiring an argument has now been added as
requested. The feature is documented as such.
Enables extended output. Requires a value argument. Example:
```bash
file --new-opt foo
```The unacceptable version echoes the prompt's framing
(`"has now been added as requested"`, `"The feature is documented as such"`).
That language belongs in the prompt, not the file.
---
Updating a Code File
**Prompt**
Add input validation to the createUser function — email must be a valid format.**Acceptable result**
function createUser(name, email) {
// Rejects addresses missing a local part, @ sign, or domain
if (!/^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(email)) {
throw new Error('Invalid email address.');
}
// ...
}**Unacceptable result**
// Added email validation as requested in the prompt
function createUser(name, email) {
// Per the instruction, we now validate that email must be a valid format
if (!/^[^\s@]+@[^\s@]+\.[^\s@]+$/.test(email)) {
throw new Error('Invalid email address.');
}
// ...
}The unacceptable version leaks prompt phrasing into code comments. Code
comments and documentation up
🎯 Best For
- Technical writers
- API documentation teams
- Claude users
- GitHub Copilot users
- Software engineers
💡 Use Cases
- Generating JSDoc/TSDoc comments
- Writing README files for new projects
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Exclude-Prompt-Data to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Does it follow my documentation style?
Most documentation skills respect existing style. Provide a style guide or example in your prompt.
Is Exclude-Prompt-Data compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Exclude-Prompt-Data?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Exclude-Prompt-Data?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/exclude-prompt-data/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Auto-generating without reviewing
AI documentation can contain inaccuracies. Always verify technical accuracy.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.