smithery/rohunvora

improve-flow

This skill should be used when users drop off mid-task, motivation fades, or experiences feel flat. Applies Peak-End Rule, Goal-Gradient Effect, and Zeigarnik Effect.

Installation

$ npx skills add smithery/rohunvora --skill improve-flow

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from smithery/rohunvora · top by installs.

npx skills add smithery/rohunvora

Browse all from smithery/rohunvora

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,608 B
  • docs SUMMARY.md 186 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Improve Flow & Experience

Applies these laws from lawsofux.com:

  • Peak-End Rule: Experiences judged by peak moments and endings
  • Goal-Gradient Effect: Motivation increases as goal approaches
  • Zeigarnik Effect: Incomplete tasks are remembered better

When to Use

  • Diagnose user drop-off patterns in mid-flow
  • Identify flat or forgettable onboarding experiences
  • Address lack of visible progress indicators
  • Improve retention when users don't return
  • Reduce checkout or form abandonment rates

How to Use

  1. Analyze the current flow - Map out the user journey from start to finish
  2. Run diagnosis - Apply the three diagnostic frameworks below to identify gaps
  3. Identify improvements - Use the law-specific techniques to address each issue
  4. Apply fixes - Implement changes following the output format structure
  5. Validate with checklist - Ensure all flow stages are optimized per the design checklist

The Laws

Peak-End Rule

People judge an experience based on how they felt at its most intense point (peak) and at its end, not on the average.

Application:

DESIGN FOR:
1. One memorable PEAK moment (delight, success, wow)
2. A strong positive ENDING

The middle can be mundane—peaks and ends are remembered.

Techniques:

  • Celebration on completion (confetti, success screen)
  • Delightful micro-interactions at key moments
  • End with accomplishment, not error or limbo
  • Recovery from errors matters more than preventing them

Goal-Gradient Effect

Motivation increases as progress approaches the goal.

Application:

SHOW PROGRESS VISIBLY:
- Progress bars accelerate motivation
- "2 of 5 steps" creates momentum
- Artificial starting progress works (start at 20%)
- Breaking into smaller goals = more motivation hits

Techniques:

  • Progress indicators on multi-step flows
  • "Almost there" messaging near completion
  • Chunk long tasks into visible milestones
  • LinkedIn-style profile completion meters

Zeigarnik Effect

People remember uncompleted tasks better than completed ones.

Application:

USE INCOMPLETENESS STRATEGICALLY:
- Unfinished profiles pull users back
- Draft states create return triggers
- "You're 80% there" is more compelling than "Complete your profile"
- Cliffhangers in onboarding

Techniques:

  • Save partial progress visibly
  • Show what's incomplete on dashboard
  • Use incompleteness for retention, not frustration
  • Don't lose user's work (increases abandonment anxiety)

Diagnosis

Apply these diagnostic frameworks to identify flow issues:

  1. Assess Peak-End Rule - Locate the peak moment in the experience and evaluate the ending quality
  2. Evaluate Goal-Gradient Effect - Check if progress is visible and if momentum accelerates toward completion
  3. Examine Zeigarnik Effect - Determine whether incompleteness creates productive return triggers or frustration
  4. Map flow stages - Identify which stages lack appropriate law applications
  5. Document current state - Record findings using the output format below

Output Format

FLOW DIAGNOSIS

Peak-End Rule:
Current peak: [moment] or [none]
Current ending: [experience]
FIX: [add peak at X / improve ending Y]

Goal-Gradient Effect:
Progress visibility: [visible/hidden]
Steps shown: [Yes/No]
Artificial progress: [used/not used]
FIX: [add progress bar / show steps / start at 20%]

Zeigarnik Effect:
Incomplete states: [how handled]
Return triggers: [exist/missing]
FIX: [save drafts / show incomplete / add reminders]

Flow Design Checklist

Stage Law Technique
Start Goal-Gradient Show progress from step 1
Start Zeigarnik Let them begin before account creation
Middle Goal-Gradient Break into visible milestones
Middle Zeigarnik Auto-save, show "draft saved"
Peak Peak-End Add delight at key success moment
End Peak-End Celebrate completion, clear next step
After Zeigarnik Show what else is incomplete

Examples

Onboarding:

Bad: 10 required fields, then "Account created"
Good: Start with win (choose avatar),
      show "3 of 5 steps",
      end with "Welcome! Here's what you can do"

Checkout:

Bad: Long form, then confirmation email
Good: Progress bar, "Almost done!",
      celebration confetti,
      immediate order summary