full-output-enforcement▌
leonxlnx/taste-skill · updated May 16, 2026
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Enforces complete, unabridged output by banning truncation patterns and placeholder code.
- ›Eliminates common shortcuts like // ... , // TODO , // rest of code , and prose phrases that defer work (\"let me know if you want more\")
- ›Treats every task as production-critical: full files, all components, no skeletons or partial implementations
- ›Handles token-limit splits cleanly by pausing at logical breakpoints (end of function, end of file) with a resumption marker, then continuing without
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
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★53 reviews- ★★★★★Chaitanya Patil· Dec 24, 2024
full-output-enforcement has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sakura Sanchez· Dec 8, 2024
Useful defaults in full-output-enforcement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakura Flores· Dec 8, 2024
Keeps context tight: full-output-enforcement is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Sakura Thomas· Dec 4, 2024
We added full-output-enforcement from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ren Choi· Nov 27, 2024
full-output-enforcement is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Maya Srinivasan· Nov 23, 2024
full-output-enforcement fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hiroshi Wang· Nov 23, 2024
Registry listing for full-output-enforcement matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Arya Wang· Nov 23, 2024
Useful defaults in full-output-enforcement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Piyush G· Nov 15, 2024
full-output-enforcement reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Isabella Shah· Oct 18, 2024
Solid pick for teams standardizing on skills: full-output-enforcement is focused, and the summary matches what you get after install.
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