full-output-enforcement▌
Leonxlnx/taste-skill · updated May 28, 2026
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Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
| name | full-output-enforcement |
| description | Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output. |
Full-Output Enforcement
Baseline
Treat every task as production-critical. A partial output is a broken output. Do not optimize for brevity — optimize for completeness. If the user asks for a full file, deliver the full file. If the user asks for 5 components, deliver 5 components. No exceptions.
Banned Output Patterns
The following patterns are hard failures. Never produce them:
In code blocks: // ..., // rest of code, // implement here, // TODO, /* ... */, // similar to above, // continue pattern, // add more as needed, bare ... standing in for omitted code
In prose: "Let me know if you want me to continue", "I can provide more details if needed", "for brevity", "the rest follows the same pattern", "similarly for the remaining", "and so on" (when replacing actual content), "I'll leave that as an exercise"
Structural shortcuts: Outputting a skeleton when the request was for a full implementation. Showing the first and last section while skipping the middle. Replacing repeated logic with one example and a description. Describing what code should do instead of writing it.
Execution Process
- Scope — Read the full request. Count how many distinct deliverables are expected (files, functions, sections, answers). Lock that number.
- Build — Generate every deliverable completely. No partial drafts, no "you can extend this later."
- Cross-check — Before output, re-read the original request. Compare your deliverable count against the scope count. If anything is missing, add it before responding.
Handling Long Outputs
When a response approaches the token limit:
- Do not compress remaining sections to squeeze them in.
- Do not skip ahead to a conclusion.
- Write at full quality up to a clean breakpoint (end of a function, end of a file, end of a section).
- End with:
[PAUSED — X of Y complete. Send "continue" to resume from: next section name]
On "continue", pick up exactly where you stopped. No recap, no repetition.
Quick Check
Before finalizing any response, verify:
- No banned patterns from the list above appear anywhere in the output
- Every item the user requested is present and finished
- Code blocks contain actual runnable code, not descriptions of what code would do
- Nothing was shortened to save space
How to use full-output-enforcement on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add full-output-enforcement
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches full-output-enforcement from GitHub repository Leonxlnx/taste-skill and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate full-output-enforcement. Access the skill through slash commands (e.g., /full-output-enforcement) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★49 reviews- ★★★★★Layla Farah· Dec 16, 2024
Registry listing for full-output-enforcement matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ava Johnson· Dec 8, 2024
Useful defaults in full-output-enforcement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Amelia Verma· Dec 4, 2024
Solid pick for teams standardizing on skills: full-output-enforcement is focused, and the summary matches what you get after install.
- ★★★★★Layla Huang· Dec 4, 2024
I recommend full-output-enforcement for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Liam Kim· Nov 23, 2024
I recommend full-output-enforcement for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aisha Torres· Nov 23, 2024
Solid pick for teams standardizing on skills: full-output-enforcement is focused, and the summary matches what you get after install.
- ★★★★★William Huang· Nov 15, 2024
Registry listing for full-output-enforcement matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Evelyn Ramirez· Oct 14, 2024
Keeps context tight: full-output-enforcement is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Layla Singh· Oct 14, 2024
full-output-enforcement is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ava Smith· Oct 6, 2024
Useful defaults in full-output-enforcement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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