From fixing one sentence to designing an algorithm, AI becomes much more useful when you stop treating it as a single-purpose chatbot.
Instead, think of it as a collection of specialized working modes.
Need to debug?
/debug
Need to design an algorithm?
/algorithm
Need to plan research?
/researchplan
Need to challenge your own argument?
/critic
Need to turn a large project into manageable work?
/roadmap90
The underlying idea is simple:
Don’t just ask AI for an answer. Give it a mode of thinking.
From Prompt → Workflow
A normal interaction might look like:
User
↓
Question
↓
AI
↓
Answer
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A structured workflow looks different:
Goal
↓
Context
↓
Lens
↓
Analysis
↓
Output
↓
Review
↓
Iteration
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For example:
Project
↓
/researchplan
↓
Research questions
↓
/hypothesis
↓
Testable assumptions
↓
/experiment
↓
Evaluation
↓
/audit
↓
Final findings
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The shortcut is not magic.
It is a task-specific instruction layer.
1. Writing Lenses
The first group focuses on transforming existing text.
/rewrite
/improve
/polish
/proofread
/grammar
/copyedit
/expand
/shorten
/paraphrase
/simplifytext
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These commands represent different operations.
For example:
/rewrite
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should preserve the original meaning while changing the wording.
Whereas:
/improve
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can address:
- clarity
- structure
- flow
- word choice
- readability
And:
/shorten
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optimizes for concision.
This distinction matters because:
Editing and rewriting are not the same task.
2. Tone Is a Control Variable
The next group controls communication style:
/formal
/casual
/friendly
/professional
/persuasive
/convincing
/academic
/journalistic
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The same information can be communicated differently depending on the audience.
For example:
Technical explanation
↓
┌──────┼──────┐
↓ ↓ ↓
Student Developer Executive
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The underlying facts should remain stable.
The presentation changes.
That makes tone a communication parameter, not merely decoration.
3. Structured Writing
For longer outputs:
/story
/essay
/article
/report
/whitepaper
/casestudy
/proposal
/sop
/playbook
/manual
/guide
/faq
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These commands define the output structure.
For example:
Problem
↓
Context
↓
Analysis
↓
Evidence
↓
Recommendation
↓
Conclusion
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This is much more useful than simply saying:
“Write a detailed report.”
A structured request reduces ambiguity.
4. Information Compression
Large amounts of information often need to be compressed.
Useful lenses include:
/bulletpoints
/keypoints
/highlights
/notes
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Think of this as:
100 pages
↓
Information extraction
↓
Important concepts
↓
Key points
↓
Actionable notes
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The goal isn’t simply to make text shorter.
The goal is to preserve the information that matters.
5. Meetings and Collaboration
For collaborative work:
/minutes
/agenda
/meetingsummary
/todo
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A meeting can become:
Discussion
↓
Decisions
↓
Action Items
↓
Owners
↓
Deadlines
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That is a much more useful representation than a raw transcript.
6. Project Management
Large projects benefit from explicit planning.
The toolkit includes:
/kanban
/gantt
/okr
/kpi
/smartgoals
/roadmap90
/roadmapyear
/milestones
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A simple project decomposition might look like:
VISION
↓
OBJECTIVES
↓
MILESTONES
↓
TASKS
↓
DEPENDENCIES
↓
EXECUTION
↓
METRICS
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This turns a vague goal into an executable system.
7. Goals Need Measurement
A goal without a measurement strategy is difficult to evaluate.
That’s where:
/okr
/kpi
/smartgoals
/metrics
/dashboardmetrics
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become useful.
For example:
Goal:
Improve application performance
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can become:
Objective:
Reduce application response time
Key Results:
↓ p95 latency
↓ error rate
↑ throughput
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The important shift is:
“I want it better.”
↓
“How will we know it is better?”
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That question makes planning measurable.
8. Risk Analysis
Projects rarely fail because everything went according to plan.
Useful lenses:
/risks
/riskmatrix
/dependencies
/estimate
/budget
/forecast
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A basic risk model:
IMPACT
Low High
┌──────┬──────┐
Low │ │ │
├──────┼──────┤
High │ │ 🔴 │
└──────┴──────┘
LIKELIHOOD
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The goal isn’t to eliminate uncertainty.
It’s to identify which uncertainties deserve attention first.
9. Root-Cause Analysis
When something goes wrong, the first explanation isn’t always the real explanation.
Useful analytical lenses:
/decisiontree
/fishbone
/pareto
/lean
/sixsigma
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For example:
Problem
↓
Why?
↓
Why?
↓
Why?
↓
Root Cause
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A Fishbone-style analysis can separate causes into categories such as:
People
Process
Technology
Environment
Data
Measurement
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This is much more useful than asking AI:
“Why did this fail?”
without providing a framework.
10. Productivity
Productivity lenses include:
/productivity
/timemanagement
/pomodoro
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But productivity shouldn’t simply mean:
“Do more tasks.”
A better model is:
Priorities
↓
Focus
↓
Execution
↓
Feedback
↓
Adjustment
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AI can help with planning and prioritization, but the actual constraints of your schedule and environment still matter.
11. Learning and Study
The educational group includes:
/studyplan
/revisionplan
/learningpath
/feynman
/memory
/mnemonics
/practice
/challengequestions
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A learning workflow could be:
Learn
↓
Explain
↓
Recall
↓
Practice
↓
Test
↓
Identify gaps
↓
Review
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The Feynman technique is particularly useful:
Learn concept
↓
Explain simply
↓
Find gaps
↓
Study gaps
↓
Explain again
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The important part is active retrieval and feedback—not simply generating longer notes.
12. Coding Lenses
Now we enter one of the most useful categories for developers:
/coding
/explaincode
/debug
/refactor
/optimizecode
/reviewcode
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These represent different software-engineering activities.
They should not be treated as interchangeable.
13. Writing Code vs Reviewing Code
For example:
/coding
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asks AI to produce an implementation.
While:
/reviewcode
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asks it to inspect an existing implementation.
The difference:
Generation
↓
“Create something.”
Review
↓
“Evaluate something.”
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That distinction is important because code generation and code evaluation have different failure modes.
14. Debugging
A useful debugging workflow is:
Bug
↓
Reproduce
↓
Observe
↓
Hypothesis
↓
Test
↓
Fix
↓
Regression Test
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The /debug lens should encourage this process.
Instead of:
“Fix this.”
a stronger debugging request provides:
Expected behavior
Actual behavior
Error message
Relevant code
Environment
Steps to reproduce
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Better context usually produces better debugging.
15. Refactoring
Refactoring is different from optimization.
/refactor
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focuses on:
- readability
- maintainability
- structure
- duplication
- separation of concerns
Whereas:
/optimizecode
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focuses on:
- runtime
- memory
- algorithmic complexity
- I/O
- resource usage
A clean implementation isn’t automatically the fastest implementation.
And the fastest implementation isn’t automatically the best design.
16. Algorithms and Data Structures
For algorithmic problems:
/pseudocode
/algorithm
/datastructure
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A useful workflow:
Problem
↓
Constraints
↓
Input / Output
↓
Candidate approaches
↓
Complexity analysis
↓
Data structure
↓
Algorithm
↓
Implementation
↓
Testing
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This is particularly important for technical interviews and competitive programming.
17. SQL and Data
The toolkit also includes:
/sql
/regex
/json
/yaml
/csv
/xml
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These are practical transformation and data-manipulation tasks.
For SQL, however, the database schema matters enormously.
A strong SQL request should include:
Tables
Columns
Relationships
Constraints
Sample data
Expected result
Database engine
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For example:
PostgreSQL
≠
SQL Server
≠
MySQL
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Even when the syntax looks similar.
18. API Design
The /api lens can help reason about:
Endpoints
HTTP methods
Request schemas
Response schemas
Authentication
Errors
Versioning
Pagination
Validation
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A simple API lifecycle:
Client
↓
Request
↓
Validation
↓
Authentication
↓
Business Logic
↓
Database / Service
↓
Response
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Thinking in this structure makes API design more systematic.
19. Research
Research requires a different mindset.
Useful lenses:
/researchplan
/literaturereview
/hypothesis
/experiment
/peerreview
/critic
/audit
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A structured research workflow:
Research Question
↓
Literature
↓
Gap
↓
Hypothesis
↓
Method
↓
Experiment
↓
Evidence
↓
Analysis
↓
Conclusion
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AI can help organize this process, but generated references, claims, statistics, and citations still need verification.
20. Hypothesis vs Opinion
A hypothesis should be testable.
For example:
Opinion:
“This model seems better.”
Hypothesis:
“Model A will achieve higher F1-score than Model B
on dataset X under the same evaluation protocol.”
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The second statement can actually be tested.
That’s a major difference.
21. Experiment Design
The /experiment lens can help structure:
Independent variable
Dependent variable
Controls
Dataset
Procedure
Evaluation metric
Expected outcome
Threats to validity
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A simplified structure:
EXPERIMENT
│
┌───────┼────────┐
↓ ↓ ↓
INPUT METHOD CONTROL
│ │ │
└───────┼────────┘
↓
OUTPUT
↓
METRICS
↓
INTERPRETATION
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This is much stronger than simply asking AI:
“Design an experiment.”
22. Peer Review and Criticism
Two particularly useful lenses are:
/peerreview
/critic
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Their purpose should not be:
“Find everything wrong.”
Instead:
Claim
↓
Evidence
↓
Reasoning
↓
Assumptions
↓
Limitations
↓
Alternative explanations
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A strong critique should distinguish:
Fact
Inference
Assumption
Opinion
Uncertainty
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This is one of the most useful habits when working with AI-generated material.
23. The /audit Lens
An audit is broader than a review.
For example:
Technical Audit
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could inspect:
Architecture
Security
Performance
Maintainability
Testing
Dependencies
Documentation
Deployment
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A research audit could inspect:
Sources
Methodology
Evidence
Statistics
Claims
Limitations
Reproducibility
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The same underlying lens can therefore be adapted to different domains.
24. The Meta-Lens: /framework
The final shortcut is:
/framework
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This is arguably one of the most powerful concepts in the collection.
Instead of asking:
“Which framework should I use?”
you can ask AI to first determine:
Problem
↓
Characteristics
↓
Candidate frameworks
↓
Selection criteria
↓
Most suitable framework
↓
Application
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For example:
Root cause?
→ Fishbone
Prioritization?
→ Pareto
Project execution?
→ Kanban / Scrum
Strategic analysis?
→ SWOT / PESTLE
Experiment?
→ Experimental design
Decision?
→ Decision tree / decision matrix
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The framework should match the problem.
The Complete 100-Lens Map
# Shortcut Purpose 301/rewrite
Rewrite while preserving meaning
302
/improve
Improve clarity and quality
303
/polish
Make writing smoother
304
/proofread
Correct grammar and spelling
305
/grammar
Fix grammar only
306
/copyedit
Professional copy editing
307
/expand
Add useful detail
308
/shorten
Condense content
309
/paraphrase
Reword naturally
310
/simplifytext
Use simpler language
311
/formal
Formal tone
312
/casual
Casual conversational tone
313
/friendly
Warm, friendly tone
314
/professional
Professional business tone
315
/persuasive
Strengthen persuasion
316
/convincing
Strengthen arguments
317
/academic
Academic style
318
/journalistic
News-style writing
319
/story
Story format
320
/essay
Essay format
321
/article
Article format
322
/report
Professional report
323
/whitepaper
White-paper structure
324
/casestudy
Case-study format
325
/proposal
Business proposal
326
/sop
Standard operating procedure
327
/playbook
Reusable playbook
328
/manual
User manual
329
/guide
Step-by-step guide
330
/faq
Frequently asked questions
331
/checklist
Checklist format
332
/template
Reusable template
333
/outline
Structured outline
334
/bulletpoints
Bullet summary
335
/keypoints
Key takeaways
336
/highlights
Important ideas
337
/notes
Study notes
338
/minutes
Meeting minutes
339
/agenda
Meeting agenda
340
/meetingsummary
Meeting summary
341
/todo
Task list
342
/kanban
Kanban task board
343
/gantt
Gantt-style plan
344
/okr
Objectives and key results
345
/kpi
Key performance indicators
346
/smartgoals
SMART goals
347
/roadmap90
90-day roadmap
348
/roadmapyear
Annual roadmap
349
/milestones
Project milestones
350
/risks
Risk assessment
351
/riskmatrix
Likelihood × impact
352
/dependencies
Task dependencies
353
/estimate
Effort/time estimation
354
/budget
Budget planning
355
/forecast
Forecasting
356
/metrics
Useful metrics
357
/dashboardmetrics
Dashboard KPI ideas
358
/decisiontree
Decision-tree analysis
359
/fishbone
Root-cause analysis
360
/pareto
80/20 analysis
361
/lean
Lean methodology
362
/sixsigma
Six Sigma approach
363
/agile
Agile methodology
364
/scrum
Scrum framework
365
/kanbanflow
Kanban workflow
366
/productivity
Productivity optimization
367
/timemanagement
Time management
368
/pomodoro
Pomodoro scheduling
369
/studyplan
Study plan
370
/revisionplan
Revision timetable
371
/learningpath
Progressive learning path
372
/feynman
Feynman technique
373
/memory
Memory techniques
374
/mnemonics
Mnemonic creation
375
/practice
Practice exercises
376
/challengequestions
Difficult questions
377
/coding
Write code
378
/explaincode
Explain code
379
/debug
Debug code
380
/refactor
Refactor code
381
/optimizecode
Optimize performance
382
/reviewcode
Code review
383
/pseudocode
Generate pseudocode
384
/algorithm
Design algorithms
385
/datastructure
Choose data structures
386
/sql
Generate SQL
387
/regex
Generate regular expressions
388
/api
Design/explain APIs
389
/json
Work with JSON
390
/yaml
Generate YAML
391
/csv
Generate CSV
392
/xml
Generate XML
393
/researchplan
Plan research
394
/literaturereview
Review literature
395
/hypothesis
Generate testable hypotheses
396
/experiment
Design experiments
397
/peerreview
Critical peer review
398
/critic
Constructive criticism
399
/audit
Comprehensive audit
400
/framework
Select an appropriate analytical framework
The Bigger Picture
The interesting thing about these 100 lenses is that they can be connected.
For example, building a software project:
IDEA
↓
/researchplan
↓
/proposal
↓
/roadmap90
↓
/milestones
↓
/dependencies
↓
/coding
↓
/reviewcode
↓
/debug
↓
/optimizecode
↓
/audit
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Writing a research paper:
QUESTION
↓
/researchplan
↓
/literaturereview
↓
/hypothesis
↓
/experiment
↓
/analysis
↓
/critic
↓
/peerreview
↓
/article
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Preparing for an exam:
SYLLABUS
↓
/learningpath
↓
/studyplan
↓
/notes
↓
/feynman
↓
/practice
↓
/challengequestions
↓
/revisionplan
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That’s where these shortcuts become more interesting.
They stop being isolated commands and become workflow components.
The Core Principle
There is a temptation to think:
“The more detailed the prompt, the better.”
I don’t think that’s always true.
A better principle is:
Give the model the right context, the right task, the right constraints, and the right evaluation criteria.
A useful conceptual model is:
Better Result
=
Context
+
Task
+
Constraints
+
Relevant Lens
+
Evaluation
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Not:
Better Result
=
More Words
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Final Thought
The most interesting future for AI assistants may not be about having one enormous prompt.
It may be about having many small, composable reasoning modes.
Instead of:
“AI, do everything.”
We move toward:
AI
├── Writer
├── Researcher
├── Programmer
├── Reviewer
├── Planner
├── Analyst
├── Teacher
└── Auditor
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And the user chooses the appropriate lens for the current problem.
That makes AI interaction feel less like asking a chatbot a question—
and more like operating a general-purpose cognitive workbench.
What would you add?
If you could add one more shortcut to this 301–400 collection, what would it be?
Maybe:
/securityaudit
/testcode
/factcheck
/architecture
/benchmark
/citationcheck
or something completely different?
I’m especially interested in shortcuts that can turn AI from a content generator into a verification and reasoning tool.