KS
Killer-Skills

aif-improve — how to use aif-improve how to use aif-improve, aif-improve alternative, aif-improve setup guide, what is aif-improve, aif-improve vs plan refinement tools, aif-improve install, plan refinement with aif-improve, aif-improve for developers, aif-improve for project managers

v1.0.0
GitHub

About this Skill

Perfect for Code Review Agents needing advanced plan refinement and codebase analysis capabilities. aif-improve is a skill that refines existing plans through deeper codebase analysis and optional user feedback to enhance task quality and correct dependencies.

Features

Re-analyzes existing plans against the codebase to find gaps and missing tasks
Enhances task quality through deeper codebase analysis and user feedback
Corrects wrong dependencies and assumptions in existing plans
Locates the active plan file using a prioritized search
Supports optional user feedback for more accurate plan refinement

# Core Topics

drsapaev drsapaev
[1]
[0]
Updated: 2/23/2026

Quality Score

Top 5%
57
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add drsapaev/final/aif-improve

Agent Capability Analysis

The aif-improve MCP Server by drsapaev is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use aif-improve, aif-improve alternative, aif-improve setup guide.

Ideal Agent Persona

Perfect for Code Review Agents needing advanced plan refinement and codebase analysis capabilities.

Core Value

Empowers agents to re-analyze existing plans against codebases, finding gaps and enhancing task quality through deeper analysis and optional user feedback, utilizing dependencies and task management protocols.

Capabilities Granted for aif-improve MCP Server

Refining project plans for improved task quality
Identifying missing edge cases in codebase dependencies
Enhancing project management through gap analysis and corrective measures

! Prerequisites & Limits

  • Requires access to the codebase and existing plan files
  • May require user feedback for optimal results
Project
SKILL.md
9.4 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

[No tags]
SKILL.md
Readonly

Improve - Plan Refinement (Second Iteration)

Refine an existing plan by re-analyzing it against the codebase. Finds gaps, missing tasks, wrong dependencies, and enhances task quality.

Core Idea

existing plan + deeper codebase analysis + user feedback (optional)
    ↓
find gaps, missing edge cases, wrong assumptions
    ↓
enhanced plan with better tasks, correct dependencies, more detail

Workflow

Step 0: Find the Plan

Locate the active plan file using this priority:

1. .ai-factory/PLAN.md exists? → Use it (from $2 fast)
2. No .ai-factory/PLAN.md → Check current git branch:
   git branch --show-current
   → Convert branch name to filename: replace "/" with "-", add ".md"
   → Look for .ai-factory/plans/<branch-name>.md
   Example: feature/user-auth → .ai-factory/plans/feature-user-auth.md

If NO plan file found at either location:

No active plan found.

To create a plan first, use:
- $2 full <description>  — for a new feature (creates branch + plan)
- $2 fast <description>  — for a quick task plan

STOP here. Do not proceed without a plan file.

If plan file found → read it and continue to Step 1.

Step 1: Load Context

1.1: Read the plan file

Read the found plan file completely. Understand:

  • Feature scope and goals
  • Current tasks (subjects, descriptions, dependencies)
  • Settings (testing, logging preferences)
  • Commit checkpoints
  • Which tasks are already completed (checkboxes - [x])

1.2: Read project context

Read .ai-factory/DESCRIPTION.md if it exists:

  • Tech stack
  • Architecture
  • Conventions
  • Non-functional requirements

1.3: Read patches (past mistakes)

Glob: .ai-factory/patches/*.md

If patches exist, read them to understand:

  • What mistakes were made before
  • What patterns to avoid
  • What the plan should account for

1.4: Load current task list

TaskList → Get all tasks with statuses

Understand what's already been created, what's in progress, what's completed.

Step 2: Deep Codebase Analysis

Now do a deeper codebase exploration than what $2 did initially:

2.1: Trace through existing code paths

For each task in the plan, find the relevant files:

Glob + Grep: Find files mentioned in tasks
Read: Understand current implementation

Look for:

  • Existing patterns the plan should follow
  • Code that already partially implements what a task describes
  • Hidden dependencies the plan missed
  • Shared utilities or services the plan should use instead of creating new ones

2.2: Check for integration points

Look for things the plan might have missed:

  • API routes that need updating
  • Database migrations needed
  • Config files that need changes
  • Import/export updates
  • Middleware or guards that apply
  • Existing validation patterns

2.3: Check for edge cases

Based on the tech stack and codebase:

  • Error handling patterns used in the project
  • Null/undefined safety patterns
  • Authentication/authorization checks needed
  • Rate limiting, caching considerations
  • Data validation at boundaries

Step 3: Identify Improvements

Compare the plan against what you found. Categorize issues:

3.1: Missing tasks

  • Tasks that should exist but don't (e.g., migration, config update, index creation)
  • Tasks for edge cases not covered

3.2: Task quality issues

  • Descriptions too vague (no file paths, no specific implementation details)
  • Missing logging requirements
  • Missing error handling details
  • Incorrect file paths

3.3: Dependency issues

  • Wrong task order (task A depends on B but B comes after A)
  • Missing dependencies (task C needs task A's output but isn't blocked by it)
  • Unnecessary dependencies (tasks could run in parallel)

3.4: Redundant or duplicate tasks

  • Two tasks doing the same thing
  • Task that's unnecessary because the code already exists
  • Task that duplicates existing functionality

3.5: Scope issues

  • Tasks too large (should be split)
  • Tasks too small (should be merged)
  • Tasks outside the feature scope (gold-plating)

3.6: User-prompted improvements (if $ARGUMENTS provided)

If the user provided specific improvement instructions in $ARGUMENTS:

  • Apply the user's feedback to the plan
  • Look for tasks that need modification based on the prompt
  • Add new tasks if the user's prompt requires them

Step 4: Present Improvements

Show the user what you found in a clear format:

## Plan Refinement Report

Plan: [plan file path]
Tasks analyzed: N

### Findings

#### 🆕 Missing Tasks (N found)
1. **[New task subject]**
   Why: [reason this task is needed]
   After: Task #X (dependency)

2. **[New task subject]**
   Why: [reason]

#### 📝 Task Improvements (N found)
1. **Task #X: [subject]**
   Issue: [what's wrong]
   Fix: [what should change]

2. **Task #Y: [subject]**
   Issue: [what's wrong]
   Fix: [what should change]

#### 🔗 Dependency Fixes (N found)
1. Task #X should depend on Task #Y
   Reason: [why]

#### 🗑️ Removals (N found)
1. **Task #X: [subject]**
   Reason: [why it's redundant/unnecessary]

#### 📋 Summary
- Missing tasks: N
- Tasks to improve: N
- Dependencies to fix: N
- Tasks to remove: N

Apply these improvements?
- [ ] Yes, apply all
- [ ] Let me pick which ones
- [ ] No, keep the plan as is

If no improvements found:

## Plan Review Complete

The plan looks solid! No significant gaps or issues found.

Plan: [plan file path]
Tasks: N

Ready to implement:
$2

Step 5: Apply Approved Improvements

Based on user's choice:

5.1: Apply task improvements

For existing tasks that need better descriptions:

TaskGet(taskId) → read current
TaskUpdate(taskId, description: "improved description", subject: "improved subject")

5.2: Add missing tasks

For new tasks:

TaskCreate(subject, description, activeForm)
TaskUpdate(taskId, addBlockedBy: [...]) → set dependencies

5.3: Fix dependencies

TaskUpdate(taskId, addBlockedBy: [...])

5.4: Remove redundant tasks

TaskUpdate(taskId, status: "deleted")

5.5: Update the plan file

CRITICAL: After all changes, update the plan file to reflect the new state:

  • Add new tasks to the correct phase with - [ ] checkboxes
  • Update task descriptions if they changed
  • Fix task ordering if dependencies changed
  • Remove deleted tasks
  • Update commit checkpoints if task count changed significantly
  • Preserve any - [x] checkboxes for already completed tasks

Use Edit to make surgical changes to the plan file, or Write to regenerate it if changes are extensive.

5.6: Confirm completion

## Plan Refined

Changes applied:
- Added N new tasks
- Improved N task descriptions
- Fixed N dependencies
- Removed N redundant tasks

Updated plan: [plan file path]
Total tasks: N

Ready to implement:
$2

Context Cleanup

Context is heavy after deep codebase analysis. Plan is updated in file — suggest freeing space:

AskUserQuestion: Free up context before continuing?

Options:
1. /clear — Full reset (recommended)
2. /compact — Compress history
3. Continue as is

Important Rules

  1. Don't rewrite from scratch — improve the existing plan, don't replace it
  2. Preserve completed work — never modify or remove - [x] completed tasks
  3. Traceable improvements — every change must be justified by codebase analysis or user input
  4. Respect settings — if testing is "no", don't add test tasks. If logging is "minimal", don't add verbose logging tasks
  5. No gold-plating — don't add tasks outside the feature scope unless critical
  6. Minimal viable improvements — suggest only what matters, not every possible enhancement
  7. User approves first — never apply changes without user confirmation
  8. Keep plan file in sync — the plan file MUST match the task list after improvements

Examples

Example 1: Auto-review (no arguments)

User: $2

→ Found plan: .ai-factory/plans/feature-user-auth.md
→ 6 tasks in plan
→ Deep codebase analysis...
→ Found: project uses middleware pattern for auth, plan misses middleware task
→ Found: Task #3 description doesn't mention existing UserService
→ Found: Task #5 depends on Task #3 but no dependency set

Report:
- 1 missing task (auth middleware)
- 1 task to improve (reference UserService)
- 1 dependency to fix

Apply? → Yes → Changes applied

Example 2: With user prompt

User: $2 добавь обработку ошибок и валидацию входных данных

→ Found plan: .ai-factory/PLAN.md
→ 4 tasks in plan
→ User wants: error handling + input validation
→ Analyzing each task for missing error handling...
→ Found: none of the tasks mention input validation
→ Found: error handling is inconsistent

Report:
- 2 tasks improved (added validation details to descriptions)
- 1 new task (create shared validation utils)
- Updated task descriptions with error handling patterns from codebase

Apply? → Yes → Changes applied

Example 3: No plan found

User: $2

→ No .ai-factory/PLAN.md found
→ Branch: main (no feature branch)
→ No plan file found

"No active plan found. Create one first:
- $2 full <description>
- $2 fast <description>"

Example 4: Plan already looks good

User: $2

→ Found plan: .ai-factory/plans/feature-product-search.md
→ 5 tasks in plan
→ Deep analysis... all tasks well-defined, dependencies correct
→ No significant improvements found

"Plan looks solid! Ready to implement:
$2"

Related Skills

Looking for an alternative to aif-improve or building a Categories.community AI Agent? Explore these related open-source MCP Servers.

View All

widget-generator

Logo of f
f

widget-generator is an open-source AI agent skill for creating widget plugins that are injected into prompt feeds on prompts.chat. It supports two rendering modes: standard prompt widgets using default PromptCard styling and custom render widgets built as full React components.

149.6k
0
Design

chat-sdk

Logo of lobehub
lobehub

chat-sdk is a unified TypeScript SDK for building chat bots across multiple platforms, providing a single interface for deploying bot logic.

73.0k
0
Communication

zustand

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
Communication

data-fetching

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
Communication