full-output-enforcement

leonxlnx/taste-skill · updated May 16, 2026

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$npx skills add https://github.com/leonxlnx/taste-skill --skill full-output-enforcement
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summary

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
skill.md

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

  1. Scope — Read the full request. Count how many distinct deliverables are expected (files, functions, sections, answers). Lock that number.
  2. Build — Generate every deliverable completely. No partial drafts, no "you can extend this later."
  3. 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

How to use full-output-enforcement on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/leonxlnx/taste-skill --skill full-output-enforcement

The skills CLI fetches full-output-enforcement from GitHub repository leonxlnx/taste-skill and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/full-output-enforcement

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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.653 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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