Enforces complete, unabridged output by banning truncation patterns and placeholder code.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfull-output-enforcementExecute the skills CLI command in your project's root directory to begin installation:
Fetches full-output-enforcement from leonxlnx/taste-skill and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate full-output-enforcement. Access via /full-output-enforcement in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
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.
When a response approaches the token limit:
[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.
Before finalizing any response, verify:
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
leonxlnx/taste-skill
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
full-output-enforcement has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in full-output-enforcement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: full-output-enforcement is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added full-output-enforcement from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
full-output-enforcement is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
full-output-enforcement fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for full-output-enforcement matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in full-output-enforcement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
full-output-enforcement reduced setup friction for our internal harness; good balance of opinion and flexibility.
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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