Agent Skills: Context Optimization & Management

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: Manage context window efficiently to maintain productivity in long Claude Code sessions.

Workflow:

1. Write — Save critical findings to persistent memory entities
2. Select — Retrieve relevant memories at session/task start
3. Compress — Create context anchors every 10 operations summarizing progress
4. Isolate — Delegate exploration tasks to sub-agents to reduce context usage

Key Rules:

• Write context anchor every 10 operations (re-read task, verify alignment, summarize)
• Use offset/limit and grep before reading large files
• Combine search patterns with OR instead of sequential searches
• At 100K tokens: required compression; at 150K: critical save and summarize

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

Context Optimization & Management

Manage context window efficiently to maintain productivity in long sessions.

Context Architecture

┌─────────────────────────────────────────────────────────────┐
│                     Context Window (~200K tokens)           │
├─────────────────────────────────────────────────────────────┤
│ System Prompt (CLAUDE.md excerpts)          ~2,000 tokens   │
│ ─────────────────────────────────────────────────────────── │
│ Working Memory (current task state)         ~10,000 tokens  │
│ ─────────────────────────────────────────────────────────── │
│ Retrieved Context (RAG from codebase)       ~20,000 tokens  │
│ ─────────────────────────────────────────────────────────── │
│ Episodic Memory (past session learnings)    ~5,000 tokens   │
│ ─────────────────────────────────────────────────────────── │
│ Tool Descriptions (relevant tools only)     ~3,000 tokens   │
└─────────────────────────────────────────────────────────────┘

Four Context Strategies

1. Writing (Save Important Context)

Save critical findings to persistent memory:

// After discovering important patterns or decisions
mcp__memory__create_entities([
    {
        name: 'EmployeeValidation',
        entityType: 'Pattern',
        observations: ['Uses validation framework fluent API', 'Async validation via ValidateRequestAsync', 'Found in Application/UseCaseCommands/']
    }
]);

When to Write:

• Discovered architectural patterns
• Important business rules
• Cross-service dependencies
• Solution decisions

2. Selecting (Retrieve Relevant Context)

Load relevant memories at session start:

// Search for relevant patterns
mcp__memory__search_nodes({ query: 'Employee validation pattern' });

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

When to Select:

• Starting a related task
• Continuing previous work
• Cross-referencing patterns

3. Compressing (Summarize Long Trajectories)

Create context anchors every 10 operations:

=== CONTEXT ANCHOR ===
Current Task: Implement employee leave request feature
Completed:

- Created LeaveRequest entity with validation
- Added SaveLeaveRequestCommand with handler
- Implemented entity event handler for notifications

Remaining:

- Create GetLeaveRequestListQuery
- Add controller endpoint
- Write unit tests

Key Findings:

- Leave requests use service-specific repository
- Notifications via entity event handlers, not direct calls
- Validation uses validation framework fluent .AndAsync()

# Next Action: Create query handler with GetQueryBuilder pattern

4. Isolating (Use Sub-Agents)

Delegate specialized tasks to sub-agents:

// Explore codebase (reduced context)
Task({ agent_type: 'Explore', prompt: 'Find all entity event handlers in the target service' });

// Plan implementation (focused context)
Task({ agent_type: 'Plan', prompt: 'Plan leave request approval workflow' });

When to Isolate:

• Broad codebase exploration
• Independent research tasks
• Parallel investigations

Context Anchor Protocol

Every 10 operations, write a context anchor:

1. Re-read original task from todo list or initial prompt
2. Verify alignment with current work
3. Write anchor summarizing progress
4. Save to memory if discovering important patterns

=== CONTEXT ANCHOR [10] ===
Task: [Original task description]
Phase: [Current phase number]
Progress: [What's been completed]
Findings: [Key discoveries]
Next: [Specific next step]
Confidence: [High/Medium/Low]
===========================

Token-Efficient Patterns

File Reading

// ❌ Reading entire files
Read({ file_path: 'large-file.cs' });

// ✅ Read specific sections
Read({ file_path: 'large-file.cs', offset: 100, limit: 50 });

// ✅ Use grep to find specific content first
Grep({ pattern: 'class SaveEmployeeCommand', path: 'src/' });

Search Optimization

// ❌ Multiple sequential searches
Grep({ pattern: 'CreateAsync' });
Grep({ pattern: 'UpdateAsync' });
Grep({ pattern: 'DeleteAsync' });

// ✅ Combined pattern
Grep({ pattern: 'CreateAsync|UpdateAsync|DeleteAsync', output_mode: 'files_with_matches' });

Parallel Operations

// ✅ Parallel reads for independent files
[Read({ file_path: 'file1.cs' }), Read({ file_path: 'file2.cs' }), Read({ file_path: 'file3.cs' })];

Memory Management Commands

Save Session Summary

// Before ending session or hitting limits
const summary = {
    task: 'Implementing employee leave request feature',
    completed: ['Entity', 'Command', 'Handler'],
    remaining: ['Query', 'Controller', 'Tests'],
    discoveries: ['Use entity events for notifications'],
    files: ['LeaveRequest.cs', 'SaveLeaveRequestCommand.cs']
};

// Save to memory
mcp__memory__create_entities([
    {
        name: `Session_${new Date().toISOString().split('T')[0]}`,
        entityType: 'SessionSummary',
        observations: [JSON.stringify(summary)]
    }
]);

Load Previous Session

// At session start
mcp__memory__search_nodes({ query: 'Session leave request' });

Anti-Patterns

| Anti-Pattern | Better Approach | | -------------------------- | ------------------------------ | | Reading entire large files | Use offset/limit or grep first | | Sequential searches | Combine with OR patterns | | Repeating same searches | Cache results in memory | | No context anchors | Write anchor every 10 ops | | Not using sub-agents | Isolate exploration tasks | | Forgetting discoveries | Save to memory entities |

Quick Reference

Token Estimation:

• 1 line of code ≈ 10-15 tokens
• 1 page of text ≈ 500 tokens
• Average file ≈ 1,000-3,000 tokens

Context Thresholds:

• 50K tokens: Consider compression
• 100K tokens: Required compression
• 150K tokens: Critical - save and summarize

Memory Commands:

• mcp__memory__create_entities - Save new knowledge
• mcp__memory__search_nodes - Find relevant context
• mcp__memory__add_observations - Update existing entities

Related

• memory-management

[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.