MCPInstall

self-learning

Use when starting work on a new or unfamiliar project, when encountering unexpected patterns, when user corrects your assumptions, or when explicitly invoked via /learn - auto-discovers and remembers project context through structured codebase analysis

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/pixel-process-ug/superkit-agents --skill self-learning
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads self-learning automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Overview

The self-learning skill automatically discovers, understands, and persists project context across sessions. It builds a mental model of the codebase, tech stack, conventions, and team preferences by scanning actual project artifacts. Without self-learning, every session starts from zero — with it, the agent accumulates institutional knowledge that improves accuracy and reduces errors over time.

Announce at start: "I'm using the self-learning skill to understand this project."


Phase 1: Trigger Identification

Determine which trigger activated this skill:

TriggerContextAction
New projectNo memory files existFull discovery (Phases 2-5)
Unfamiliar areaWorking in unknown part of codebaseTargeted discovery (Phase 2-3 for that area)
User correctionUser says "that's wrong" or corrects an assumptionCorrection protocol (Phase 6)
Explicit /learnUser invokes the commandFull discovery (Phases 2-5)
Session startMemory files exist but may be staleLoad and validate memory (Phase 7)

STOP: Identify your trigger before proceeding. Different triggers require different phases.


Phase 2: Project Structure Scan

Use Explore agents to examine these files in order of priority:

File/DirectoryWhat It RevealsPriority
package.json / pyproject.toml / go.mod / Cargo.toml / composer.jsonTech stack, dependencies, scriptsCritical
README.md / CLAUDE.md / AGENTS.mdProject purpose, conventions, rulesCritical
Directory structure (top 2 levels)Architecture pattern (monorepo, MVC, hexagonal, etc.)Critical
tsconfig.json / eslint.config.* / .prettierrc / phpstan.neonCoding standards, strictness levelHigh
.gitignoreWhat is excluded, deployment hintsHigh
docker-compose.yml / DockerfileInfrastructure, servicesMedium
.github/workflows/ / .gitlab-ci.ymlCI/CD setup, required checksMedium
.env.exampleEnvironment variables, external servicesMedium
specs/ / docs/Existing specifications and documentationMedium

Action: For each file found, extract key facts. Do NOT read every file — scan strategically.

STOP: Complete the structure scan before analyzing code patterns.


Phase 3: Code Pattern Analysis

Examine 3-5 representative files to identify patterns:

Pattern CategoryWhat to Look ForExample Indicators
Naming conventionsVariable/function/file namingcamelCase, snake_case, kebab-case, PascalCase
Import/exportModule organizationBarrel exports, relative vs absolute paths, path aliases
Error handlingHow failures are managedtry/catch, Result types, error boundaries, custom exceptions
Testing patternsTest framework and styleFile naming (*.test.ts vs *.spec.ts), structure (describe/it vs test)
State managementHow data flowsRedux, Zustand, Context, Vuex, Pinia, Livewire
API patternsCommunication styleREST, GraphQL, tRPC, RPC, WebSocket
Database accessData layer approachORM (Prisma, Eloquent, TypeORM), raw SQL, query builder
Component patternsUI structureAtomic design, feature folders, co-located styles

Action: Open 3-5 files from different areas of the codebase. Record observed patterns with specific examples.


Phase 4: Git History Analysis

git log --oneline -20          # Recent commits — development velocity, commit style
git shortlog -sn -20           # Active contributors
git branch -a                  # Branching strategy
git log --diff-filter=A --name-only --pretty=format: -10 | head -30  # Recently added files

Extract: Development velocity, commit message conventions, branching strategy, active areas.

STOP: Complete all discovery phases before persisting to memory.


Phase 5: Persist to Memory

Update the following memory files (create if they do not exist, append if they do):

memory/project-context.md

# Project Context
<!-- Updated by self-learning skill -->
<!-- Last updated: YYYY-MM-DD -->

## Purpose
[What this project does, who it is for]

## Tech Stack
- Language: [e.g., TypeScript 5.x]
- Framework: [e.g., Next.js 15]
- Database: [e.g., PostgreSQL via Prisma]
- Testing: [e.g., Vitest + Playwright]
- CI/CD: [e.g., GitHub Actions]

## Architecture
[e.g., Monorepo with apps/ and packages/]
[Key directories and their purposes]

## Key Dependencies
[Critical libraries and their roles]

memory/learned-patterns.md

# Learned Patterns
<!-- Updated by self-learning skill -->
<!-- Last updated: YYYY-MM-DD -->

## Naming Conventions
[What was observed with specific examples]

## Code Organization
[Import patterns, file structure, module boundaries]

## Error Handling
[How errors are handled in this project with examples]

## Testing Approach
[Framework, patterns, naming, coverage expectations]

memory/user-preferences.md

# User Preferences
<!-- Updated by self-learning skill -->
<!-- Last updated: YYYY-MM-DD -->

## Communication Style
[Terse? Detailed? Prefers code over explanation?]

## Workflow Preferences
[PR workflow? Branch naming? Commit style?]

## Review Preferences
[What they focus on in reviews?]

memory/decisions-log.md

# Decisions Log
<!-- Updated by self-learning and brainstorming skills -->

## YYYY-MM-DD: [Decision Title]
**Decision:** [What was decided]
**Context:** [Why this came up]
**Rationale:** [Why this was chosen over alternatives]
**Alternatives considered:** [What else was considered]

Phase 6: Correction Protocol

When the user corrects an assumption:

  1. Acknowledge the correction immediately — do not defend the wrong assumption
  2. Identify which memory file should be updated
  3. Update the memory file with the correction, including date and context
  4. Apply the correction to current work immediately
  5. Propagate — check if the correction invalidates other assumptions in memory
Correction TypeMemory File to UpdateExample
Naming convention wronglearned-patterns.md"We use snake_case, not camelCase"
Tech stack wrongproject-context.md"We use Vitest, not Jest"
Workflow preferenceuser-preferences.md"Always create PRs, never push to main"
Architecture misunderstandingproject-context.md"That is a microservice, not a monolith"
Decision contextdecisions-log.md"We chose X because of Y, not Z"

Do NOT skip the memory update. Corrections that are not persisted will be repeated.


Phase 7: Session Start — Memory Validation

When memory files already exist:

  1. Load all memory files from memory/ directory
  2. Check Last updated dates — flag anything older than 30 days
  3. Spot-check 2-3 facts against current codebase (e.g., does package.json still list the same framework?)
  4. If discrepancies found, run a targeted re-scan of the changed area
  5. Update stale entries with current information

Decision Table: Discovery Depth

SituationDiscovery DepthTime Budget
Brand new project, no memory filesFull (all phases)3-5 minutes
New area of known projectTargeted (Phase 2-3 for that area only)1-2 minutes
User correctionCorrection protocol only (Phase 6)30 seconds
Session start with existing memoryValidation only (Phase 7)1 minute
Major refactor detected (many changed files)Full re-scan3-5 minutes

Anti-Patterns / Common Mistakes

What NOT to DoWhy It FailsWhat to Do Instead
Read every file in the projectWastes context window, slowScan strategically: config files first, then 3-5 representative code files
Assume conventions from one fileOne file may be an outlierVerify patterns across 2+ files before persisting
Overwrite memory files completelyLoses historical context and user correctionsAppend or update specific sections, preserve history
Skip git historyMisses development velocity and team patternsAlways check recent commits and contributors
Persist guesses as factsPoisons future sessions with wrong contextOnly persist observations backed by evidence
Ignore user correctionsRepeats the same mistakesCorrections override observations immediately
Deep-dive into implementation detailsLoses the forest for the treesFocus on patterns and conventions, not specific logic
Skip memory validation on session startUses stale contextAlways spot-check memory against current codebase

Anti-Rationalization Guards

ThoughtReality
"I already know this framework"You do not know THIS project's conventions. Scan.
"The memory files are recent enough"Spot-check anyway. Code changes fast.
"This correction is minor"Minor corrections prevent major errors. Persist it.
"I will remember this without writing it down"You will not. Sessions are independent. Persist to memory.
"Scanning will take too long"Not scanning leads to wrong assumptions that take longer to fix.

Do NOT skip memory persistence. If you discovered it, write it down.


Integration Points

SkillRelationship
using-toolkitTriggers self-learning at session start
brainstormingLoads project context before idea generation
planningUses learned patterns to propose consistent approaches
code-reviewChecks code against learned conventions
auto-improvementRecords discovery effectiveness metrics
resilient-executionFailure patterns inform future approach selection

Concrete Examples

Discovery Command Sequence

# Step 1: Identify tech stack
cat package.json | head -50
# or
cat composer.json | head -50
# or
cat pyproject.toml

# Step 2: Check project structure
ls -la
ls -la src/ || ls -la app/ || ls -la lib/

# Step 3: Examine coding standards
cat tsconfig.json 2>/dev/null || cat phpstan.neon 2>/dev/null
cat .eslintrc* 2>/dev/null || cat .prettierrc* 2>/dev/null

# Step 4: Git activity
git log --oneline -20
git shortlog -sn -20

Memory Update After Correction

## 2026-03-15: Naming Convention Correction
**Previous assumption:** Project uses camelCase for database columns
**Correction:** Project uses snake_case for database columns (user corrected)
**Evidence:** Checked `migrations/` directory — all columns use snake_case
**Updated:** learned-patterns.md, Naming Conventions section

Key Principles

  • Observe, do not assume — base learnings on evidence from the codebase
  • Incremental updates — append to memory files, do not overwrite
  • Verify before persisting — double-check observations with 2+ examples
  • Respect corrections — user corrections override observations immediately
  • Stay current — re-scan when significant changes occur

Skill Type

RIGID — Discovery phases must be followed in order. Memory persistence is mandatory. Corrections must be recorded immediately. Do not skip phases or rationalize away the need to scan.

FAQ

What does the self-learning skill do?

Use when starting work on a new or unfamiliar project, when encountering unexpected patterns, when user corrects your assumptions, or when explicitly invoked via /learn - auto-discovers and remembers project context through structured codebase analysis

How do I install self-learning?

Run npx skills add https://github.com/pixel-process-ug/superkit-agents --skill self-learning in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/self-learning/ (personal) or .claude/skills/self-learning/ (project).

Which AI agents can use self-learning?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is self-learning safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains self-learning?

It is published by pixel-process-ug in pixel-process-ug/superkit-agents. It has 80 recorded installs.

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