Agent Skills: Memory Management & Knowledge Persistence

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Prefer the plan-hard skill for planning guidance in this Codex mirror.
• Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
• User-question prompts mean to ask the user directly in Codex.
• Ignore Claude-specific mode-switch instructions when they appear.
• Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
• Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
• Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
• For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
• If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex does not receive Claude hook-based doc injection. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

• docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
• docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
• docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Situation-based docs:

• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec/test-case planning or TC mapping: feature-docs-reference.md
• Integration test implementation/review: integration-test-reference.md
• E2E test implementation/review: e2e-test-reference.md
• Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

Quick Summary

Goal: Persist patterns, decisions, and task progress across sessions using two complementary memory systems.

Workflow:

1. File Checkpoints — Save task-specific context to plans/reports/checkpoint-*.md every 30-60 min
2. MCP Memory Graph — Store reusable knowledge (patterns, decisions, bug fixes) as typed entities with relations
3. Recovery — On context loss, find latest checkpoint via Glob, read it, resume from documented next steps

Key Rules:

• Use file checkpoints for task-specific progress; MCP memory for cross-session knowledge
• Create checkpoints before expected context compaction and at key milestones
• Always include Recovery Instructions in checkpoint files

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Memory Management & Knowledge Persistence

Build and maintain a knowledge graph of patterns, decisions, and learnings across sessions. Also provides external file-based checkpoints for long-running tasks.

Two Memory Systems

| System | Storage | Use Case | Persistence | | -------------------- | ------------------------ | ------------------------------ | --------------- | | MCP Memory Graph | In-memory graph database | Patterns, decisions, learnings | Cross-session | | File Checkpoints | plans/reports/*.md | Task progress, analysis | Permanent files |

Use MCP Memory for reusable knowledge. Use File Checkpoints for task-specific context.

Part 1: File-Based External Memory (Checkpoints)

When to Create File Checkpoints

• Starting complex multi-step tasks (investigation, planning, implementation)
• Every 30-60 minutes during long tasks
• At key milestones
• Before expected context compaction
• After completing significant analysis phases

Checkpoint File Location

Files saved to: plans/reports/checkpoint-{timestamp}-{slug}.md

CHECKPOINT_CREATE Protocol

Create a checkpoint file with this structure:

# Memory Checkpoint: {Task Description}

> Created: {ISO timestamp}
> Task Type: {investigation|planning|bugfix|feature|docs}
> Phase: {current phase number/name}

## Task Context

{What you're working on and why}

## Key Findings

{Critical discoveries and insights - be specific with file paths and line numbers}

## Files Analyzed

| File              | Purpose     | Status   |
| ----------------- | ----------- | -------- |
| path/file.cs:line | description | ✅/🔄/⏳ |

## Progress

- [x] Completed items
- [ ] In-progress items
- [ ] Remaining items

## Important Context

{Information that must be preserved - decisions, assumptions, rationale}

## Next Steps

1. {Immediate next action}
2. {Following action}

## Recovery Instructions

{Exact steps to resume: which file to read, which line to continue from}

CHECKPOINT_RECOVER Protocol

When recovering from a checkpoint:

1. Search for latest checkpoint: Glob("plans/reports/checkpoint-*.md")
2. Read the checkpoint file
3. Load any referenced analysis files
4. Review Progress section
5. Continue from documented Next Steps
6. Create new checkpoint after resuming

Auto-Checkpoint (PreCompact Hook)

The system automatically creates checkpoints before context compaction. These auto-checkpoints are minimal - for better context preservation, create manual checkpoints using $checkpoint.

Part 2: MCP Memory Graph (Knowledge Persistence)

Memory Entity Types

| Entity Type | Purpose | Examples | | ----------------- | -------------------------------------- | ------------------------------ | | Pattern | Recurring code patterns | CQRS, Validation, Repository | | Decision | Architectural/design decisions | Why we chose X over Y | | BugFix | Bug solutions for future reference | Race condition fixes | | ServiceBoundary | Service ownership and responsibilities | Growth owns Employees | | SessionSummary | End-of-session progress snapshots | Task progress, next steps | | Dependency | Cross-service dependencies | Growth depends on Accounts | | AntiPattern | Patterns to avoid | Don't call side effects in cmd |

Memory Operations

Create New Entity

mcp__memory__create_entities([
    {
        name: 'EmployeeValidationPattern',
        entityType: 'Pattern',
        observations: [
            'Use project validation fluent API (see docs/project-reference/backend-patterns-reference.md)',
            'Chain with .And() and .AndAsync()',
            "Return validation result, don't throw",
            'Location: {Service}.Application/UseCaseCommands/'
        ]
    }
]);

Create Relationships

mcp__memory__create_relations([
    {
        from: 'ServiceA',
        to: 'ServiceB',
        relationType: 'depends_on'
    },
    {
        from: 'EmployeeEntity',
        to: 'UserEntity',
        relationType: 'syncs_from'
    }
]);

Add Observations

mcp__memory__add_observations([
    {
        entityName: 'EmployeeValidationPattern',
        contents: [
            'Also supports .AndNot() for negative validation',
            'Use .Of<ICqrsRequest>() for type conversion (see docs/project-reference/backend-patterns-reference.md)'
        ]
    }
]);

Search Knowledge

// Search by query
mcp__memory__search_nodes({ query: 'validation pattern' });

// Open specific entities
mcp__memory__open_nodes({
    names: ['EmployeeValidationPattern', 'ServiceAModule']
});

// Read entire graph
mcp__memory__read_graph();

Delete Outdated Knowledge

// Delete entities
mcp__memory__delete_entities({ entityNames: ['OutdatedPattern'] });

// Delete specific observations
mcp__memory__delete_observations([
    {
        entityName: 'EmployeeValidationPattern',
        observations: ['Outdated observation text']
    }
]);

// Delete relations
mcp__memory__delete_relations([
    {
        from: 'OldService',
        to: 'NewService',
        relationType: 'depends_on'
    }
]);

When to Save to Memory

Always Save

1. Discovered Patterns: New code patterns not in documentation
2. Bug Solutions: Complex bugs with non-obvious solutions
3. Service Boundaries: Which service owns what
4. Architectural Decisions: Why a particular approach was chosen
5. Anti-Patterns: Mistakes to avoid

Save at Session End

// Session summary template
mcp__memory__create_entities([
    {
        name: `Session_${taskName}_${date}`,
        entityType: 'SessionSummary',
        observations: [
            `Task: ${taskDescription}`,
            `Completed: ${completedItems.join(', ')}`,
            `Remaining: ${remainingItems.join(', ')}`,
            `Key Files: ${keyFiles.join(', ')}`,
            `Discoveries: ${discoveries.join(', ')}`,
            `Next Steps: ${nextSteps.join(', ')}`
        ]
    }
]);

Memory Retrieval Patterns

Session Start Protocol

// 1. Search for related context
const results = mcp__memory__search_nodes({
    query: 'current feature or task keywords'
});

// 2. Load relevant entities
mcp__memory__open_nodes({
    names: results.entities.map(e => e.name)
});

// 3. Check for incomplete sessions
mcp__memory__search_nodes({ query: 'SessionSummary Remaining' });

Before Implementation

// Check for existing patterns
mcp__memory__search_nodes({ query: 'CQRS command pattern' });

// Check for anti-patterns
mcp__memory__search_nodes({ query: 'AntiPattern command' });

// Check for related decisions
mcp__memory__search_nodes({ query: 'Decision validation' });

After Bug Fix

// Save the fix
mcp__memory__create_entities([
    {
        name: `BugFix_${bugName}`,
        entityType: 'BugFix',
        observations: [
            `Symptom: ${symptomDescription}`,
            `Root Cause: ${rootCause}`,
            `Solution: ${solution}`,
            `Files: ${affectedFiles.join(', ')}`,
            `Prevention: ${preventionTip}`
        ]
    }
]);

Knowledge Graph Structure

┌─────────────────────────────────────────────────────────────┐
│                     Project Knowledge                       │
├─────────────────────────────────────────────────────────────┤
│  Services                                                   │
│  ├── ServiceA ──depends_on──> AccountsService               │
│  ├── ServiceB ──depends_on──> AccountsService               │
│  └── ServiceC ──depends_on──> AccountsService               │
│                                                             │
│  Patterns                                                   │
│  ├── CQRSCommandPattern                                     │
│  ├── CQRSQueryPattern                                       │
│  ├── EntityEventPattern                                     │
│  └── ValidationPattern                                      │
│                                                             │
│  Entities                                                   │
│  ├── Employee ──syncs_from──> User                          │
│  ├── Company ──syncs_from──> Organization                   │
│  └── LeaveRequest ──owned_by──> ServiceA                     │
│                                                             │
│  Sessions                                                   │
│  ├── Session_LeaveRequest_2025-01-15                        │
│  └── Session_EmployeeImport_2025-01-14                      │
└─────────────────────────────────────────────────────────────┘

Importance Scoring

When saving observations, prioritize:

| Score | Criteria | | ----- | ------------------------------------------- | | 10 | Critical bug fixes, security issues | | 8-9 | Architectural decisions, service boundaries | | 6-7 | Code patterns, best practices | | 4-5 | Session summaries, progress notes | | 1-3 | Temporary notes, exploration results |

Memory Maintenance

Weekly Cleanup

// Find old session summaries (> 30 days)
mcp__memory__search_nodes({ query: 'SessionSummary' });

// Delete outdated sessions
mcp__memory__delete_entities({
    entityNames: ['Session_OldTask_2024-12-01']
});

Consolidation

When multiple observations cover same topic:

// 1. Read existing entity
mcp__memory__open_nodes({ names: ['PatternName'] });

// 2. Delete fragmented observations
mcp__memory__delete_observations([
    {
        entityName: 'PatternName',
        observations: ['Fragment 1', 'Fragment 2']
    }
]);

// 3. Add consolidated observation
mcp__memory__add_observations([
    {
        entityName: 'PatternName',
        contents: ['Consolidated comprehensive observation']
    }
]);

Quick Reference

Create: mcp__memory__create_entities / mcp__memory__create_relations Read: mcp__memory__read_graph / mcp__memory__open_nodes / mcp__memory__search_nodes Update: mcp__memory__add_observations Delete: mcp__memory__delete_entities / mcp__memory__delete_observations / mcp__memory__delete_relations

Part 3: Integration with Workflows

Long-Running Task Memory Pattern

All long-running workflows should follow this pattern:

┌─────────────────────────────────────────────────────────┐
│ TASK START                                               │
│   └── Create initial checkpoint with task context        │
│   └── Initialize todo list                               │
│                                                          │
│ EVERY 20-30 OPERATIONS                                   │
│   └── Update checkpoint with progress                    │
│   └── Update todo list status                            │
│                                                          │
│ MILESTONE REACHED                                         │
│   └── Create detailed checkpoint                         │
│   └── Save key findings to MCP memory (if reusable)      │
│                                                          │
│ BEFORE COMPACTION (auto via PreCompact hook)             │
│   └── Auto-checkpoint created by system                  │
│                                                          │
│ AFTER COMPACTION / SESSION RESUME                        │
│   └── Read latest checkpoint                             │
│   └── Search MCP memory for relevant context             │
│   └── Continue from documented Next Steps                │
│                                                          │
│ TASK COMPLETE                                             │
│   └── Final checkpoint with summary                      │
│   └── Save reusable patterns to MCP memory               │
│   └── Clean up temporary checkpoints                     │
└─────────────────────────────────────────────────────────┘

Checkpoint Naming Convention

| Type | Format | Example | | ----------------- | ------------------------------------------- | -------------------------------------- | | Manual checkpoint | checkpoint-{YYMMDD}-{HHMM}-{slug}.md | checkpoint-250106-1430-user-auth.md | | Auto checkpoint | memory-checkpoint-{timestamp}.md | memory-checkpoint-20250106-143000.md | | Analysis notes | {type}-{date}-{slug}.md | analysis-250106-payment-flow.md | | Task notes | .ai/workspace/analysis/{slug}.analysis.md | Used by feature-implementation |

Related Commands & Skills

| Command/Skill | Purpose | | ------------------------ | ----------------------------------- | | $checkpoint | Create manual memory checkpoint | | $context | Load project context | | $compact | Manually trigger context compaction | | $watzup | Generate progress summary | | feature-implementation | Uses task analysis notes pattern | | debug-investigate | Uses investigation logs | | feature-investigation | Uses analysis report pattern |

Memory Decision Matrix

| Context Type | Storage | Why | | ----------------------- | --------------- | ------------------------ | | Task progress | File checkpoint | Specific to current task | | Code patterns | MCP memory | Reusable across sessions | | Bug solutions | MCP memory | Helps future debugging | | Service boundaries | MCP memory | Architectural knowledge | | Investigation findings | File checkpoint | Task-specific analysis | | Architectural decisions | MCP memory | Long-term knowledge |

Related

• learn
• context-optimization

[IMPORTANT] Use task tracking to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Closing Reminders

• MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using task tracking BEFORE starting
• MANDATORY IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
• MANDATORY IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
• MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using task tracking.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/hooks/lib/prompt-injections.cjs + .claude/.ck.json

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

1. DETECT: Match prompt against workflow catalog
2. ANALYZE: Find best-match workflow AND evaluate if a custom step combination would fit better
3. ASK (REQUIRED FORMAT): Use a direct user question with this structure:
   • Question: "Which workflow do you want to activate?"
   • Option 1: "Activate [BestMatch Workflow] (Recommended)"
   • Option 2: "Activate custom workflow: [step1 → step2 → ...]" (include one-line rationale)
4. ACTIVATE (if confirmed): Call $workflow-start <workflowId> for standard; sequence custom steps manually
5. CREATE TASKS: task tracking for ALL workflow steps
6. EXECUTE: Follow each step in sequence [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows.

Learned Lessons

Lessons Learned

[CRITICAL] Hard-won project debugging/architecture rules. MUST ATTENTION apply BEFORE forming hypothesis or writing code.

Quick Summary

Goal: Prevent recurrence of known failure patterns — debugging, architecture, naming, AI orchestration, environment.

Top Rules (apply always):

• MUST ATTENTION verify ALL preconditions (config, env, DB names, DI regs) BEFORE code-layer hypothesis
• MUST ATTENTION fix responsible layer — NEVER patch symptom sites with caller-specific defensive code
• MUST ATTENTION use ExecuteInjectScopedAsync for parallel async + repo/UoW — NEVER ExecuteUowTask
• MUST ATTENTION name by PURPOSE not CONTENT — adding member forces rename = abstraction broken
• MUST ATTENTION persist sub-agent findings incrementally after each file — NEVER batch at end
• MUST ATTENTION Windows bash: verify Python alias (where python/where py) — NEVER assume python/python3 resolves

Debugging & Root Cause Reasoning

• [2026-04-11] Holistic-first: verify environment before code. Failure → list ALL preconditions (config, env vars, DB names, endpoints, DI regs, credentials, permissions, data prerequisites) → verify each via evidence (grep/cat/query) BEFORE code-layer hypothesis. Worst rabbit holes: diving nearest layer while bug sits elsewhere — e.g., hours debugging "sync timeout", real cause: test appsettings pointing wrong DB. ALWAYS cheapest check first.
• [2026-04-01] Ask "whose responsibility?" before fixing. Trace: bug caller (wrong data) or callee (wrong handling)? Fix responsible layer — NEVER patch symptom site masking real issue.
• [2026-04-01] Trace data lifecycle, not error site. Follow data: creation → transformation → consumption. Bug usually where data created wrong, not consumed.
• [2026-04-01] Code caller-agnostic. Functions/handlers/consumers don't know who invokes them. Comments/guards/messages describe business intent — NEVER reference specific callers (tests, seeders, scripts).

Architecture Invariants

• [2026-05-09] User name materialization MUST ATTENTION go through User.UpdateName(firstName, middleName, lastName). Domain method (src/Services/bravoTALENTS/Employee.Domain/AggregatesModel/User.cs:202-209) recomputes FullName as single source of truth. Three sites still manually patch user.FullName = user.GetFullName() after assigning name fields — src/Services/bravoTALENTS/Employee.Application/Factories/UserFactory.cs:50, src/Services/bravoSURVEYS/LearningPlatform.Application/ApplyPlatform/MessageBus/Consumers/AccountUserDeletedEventBusConsumer.cs:102, src/Services/bravoINSIGHTS/Analyze/Analyze.Application/MessageBus/Consumers/AccountUserDeletedEventBusConsumer.cs:66. Next time touching any: replace manual patch with user.UpdateName(...) to maintain invariant.
• [2026-03-31] ParallelAsync + repo/UoW MUST ATTENTION use ExecuteInjectScopedAsync, NEVER ExecuteUowTask. ExecuteUowTask creates new UoW but reuses outer DI scope (same DbContext) — parallel iterations sharing non-thread-safe DbContext silently corrupt data. ExecuteInjectScopedAsync creates new UoW + new DI scope (fresh repo per iteration).
• [2026-03-31] Bus message naming MUST ATTENTION include service name prefix — core services NEVER consume feature events. Prefix declares schema ownership (AccountUserEntityEventBusMessage = Accounts owns). Core services (Accounts, Communication) leaders. Feature services (Growth, Talents) sending to core MUST ATTENTION use {CoreServiceName}...RequestBusMessage — NEVER define own event for core to consume.

Naming & Abstraction

• [2026-04-12] Name PURPOSE not CONTENT — "OrXxx" anti-pattern. HrManagerOrHrOrPayrollHrOperationsPolicy names set members, not what guards. Add role → rename = broken abstraction. Rule: names express DOES/GUARDS, not CONTAINS. Test: adding/removing member forces rename? YES = content-driven = bad → rename to purpose (e.g., HrOperationsAccessPolicy). Nuance: "Or" fine behavioral idioms (FirstOrDefault, SuccessOrThrow) — expresses HAPPENS, not membership.

Environment & Tooling

• [2026-04-20] Windows bash: NEVER assume python/python3 resolves — verify alias first. Python may not be bash PATH under those names. Check: where python / where py. ALWAYS prefer py (Windows Python Launcher) one-liners, node if JS alternative exists.

Test-specific lessons → docs/project-reference/integration-test-reference.md Lessons Learned section. Production-code anti-patterns → docs/project-reference/backend-patterns-reference.md Anti-Patterns section. Generic debugging/refactoring reminders → System Lessons .claude/hooks/lib/prompt-injections.cjs.

Closing Reminders

• IMPORTANT MUST ATTENTION holistic-first: verify ALL preconditions (config, env, DB names, endpoints, DI regs) BEFORE code-layer hypothesis — cheapest check first
• IMPORTANT MUST ATTENTION fix responsible layer — NEVER patch symptom site; trace caller (wrong data) vs callee (wrong handling), fix root owner
• IMPORTANT MUST ATTENTION parallel async + repo/UoW → ALWAYS ExecuteInjectScopedAsync, NEVER ExecuteUowTask (shared DbContext = silent data corruption)
• IMPORTANT MUST ATTENTION bus message prefix = schema ownership; feature services NEVER define events for core services — use {CoreServiceName}...RequestBusMessage
• IMPORTANT MUST ATTENTION name by PURPOSE — adding/removing member forces rename = broken abstraction
• IMPORTANT MUST ATTENTION sub-agents MUST write findings after each file/section — NEVER batch all findings into one final write
• IMPORTANT MUST ATTENTION Windows bash: NEVER assume python/python3 resolves — run where python/where py first, use py launcher or node
• IMPORTANT MUST ATTENTION every claim needs file:line evidence — confidence >80% to act, NEVER speculate

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
6. Auto-fix gate: "Could $code-review/$code-simplifier/$security/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.