TLDR Prompt

TLDR Prompt is an AI skill that condenses lengthy prompts, documents, and instructions into concise, effective summaries that retain essential meaning and actionable content. It covers summarization strategies, key information extraction, context preservation techniques, and output formatting that delivers maximum value in minimum text.

What Is This?

Overview

TLDR Prompt provides intelligent condensation of verbose content into focused summaries. It identifies and preserves core instructions while removing redundancy, extracts key decisions and action items from lengthy discussions, maintains technical accuracy when compressing detailed specifications, adapts summary length to the content's complexity, and formats output for quick scanning with clear structure.

Who Should Use This

This skill serves developers who need to quickly understand long technical documents, project managers summarizing meeting notes and decision logs, teams that share condensed versions of lengthy specifications, and anyone who processes large volumes of text and needs efficient extraction of key points.

Why Use It?

Problems It Solves

Long documents, conversation threads, and specifications consume excessive time to read. Important details get buried in verbose explanations. Teams waste time re-reading lengthy content to find specific decisions or action items. Manual summarization is subjective and often misses critical technical details.

Core Highlights

The skill preserves technical precision when condensing content, ensuring that code references, API names, and configuration values remain intact. Priority-based extraction focuses on actionable items over background context. Format-aware summarization adapts to the input type whether it is a conversation, specification, or code review. Length targets can be specified to match the reader's available time.

How to Use It?

Basic Usage

Input: 2,000-word API specification document

TLDR Output:
API: User Management Service v2.1
Base URL: /api/v2/users
Auth: Bearer token (OAuth 2.0)

Endpoints:
- POST /users: Create user (requires email, name)
- GET /users/:id: Retrieve user profile
- PATCH /users/:id: Update fields (partial update)
- DELETE /users/:id: Soft delete (30-day recovery)

Key constraints:
- Rate limit: 100 req/min per API key
- Max payload: 1MB
- Email uniqueness enforced at database level
- Passwords: bcrypt, min 12 characters

Breaking changes from v1:
- User ID format changed from integer to UUID
- /users/list replaced by GET /users with pagination

Real-World Examples

Input: 45-message Slack thread about deployment issue

TLDR Output:
Issue: Production API latency spike (3x normal) after deploy v4.2.1
Root cause: N+1 query in new user activity feed endpoint
Timeline:
- 9:15 AM: Deploy v4.2.1 rolled out
- 9:45 AM: Monitoring alerts triggered (p99 > 2s)
- 10:10 AM: Root cause identified by Sarah (DB team)
- 10:30 AM: Hotfix merged (added eager loading)
- 10:45 AM: v4.2.2 deployed, latency normal

Action items:
1. Add query count assertions to CI (assigned: Mike)
2. Review other endpoints for N+1 patterns (assigned: Sarah)
3. Update deployment checklist with DB perf check (assigned: Alex)

Advanced Tips

Specify the audience and purpose when requesting summaries, as a summary for executives differs from one for engineers. Chain TLDR operations for very long documents by first summarizing sections individually and then creating a master summary. Preserve exact names, numbers, and technical identifiers rather than paraphrasing them.

When to Use It?

Use Cases

Use TLDR Prompt when onboarding to a project with extensive documentation, when sharing key takeaways from lengthy meetings or discussions, when creating executive summaries of technical specifications, or when processing large volumes of support tickets or feedback for pattern analysis.

Related Topics

Text summarization techniques, information extraction, document processing pipelines, meeting notes automation, and knowledge management systems all complement the TLDR workflow.

Important Notes

Requirements

The source text in a readable format such as plain text, Markdown, or conversation logs. Clarity about the target audience and purpose helps produce more relevant summaries. Desired output length or detail level guides the compression ratio.

Usage Recommendations

Do: verify that critical details like dates, names, and technical values are preserved accurately in the summary. Specify what aspects of the content matter most for your use case. Use structured formats like bullet points for scannable output.

Don't: use TLDR for content where nuance and context are critical to correct interpretation such as legal agreements. Assume the summary captures every detail from the original, as some context is necessarily lost. Apply the same summarization approach to all content types without considering their structure.

Limitations

Summarization inevitably loses some nuance and context from the original content. Technical documents with dense interdependencies may not condense well without losing important relationships between concepts. The skill works best with well-structured input and may produce less coherent summaries from disorganized source material.