Expert guide for automated and manual Web Accessibility (a11y) testing — axe-core, Pa11y, Playwright a11y, screen reader testing, and WCAG 2.2 Level AA/AAA compliance / Panduan ahli pengujian aksesibilitas web.
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Manages the entire session lifecycle: from context loading (PRD, Tech Stack) at startup, to maintaining project memory during active sessions, and creating ultra-compact checkpoints for handoff. / Mengelola siklus hidup sesi: dari pemuatan konteks awal, pemeliharaan memori proyek, hingga pembuatan checkpoint ringkas untuk handoff.
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Connects and orchestrates with relevant domain skills like brainstorming and zero-to-prod-orchestrator to ensure cohesive execution across the entire session lifecycle.
This skill acts as the agent's memory controller. It handles loading project context at session startup, dynamically updating the context map and learning graph during the session, and seamlessly generating ultra-compact handoff checkpoints at session end.
Activate this phase IMMEDIATELY on a new session, or when the user says "start session" or "load context". Do not answer primary requests until complete.
Workflow:
.docs/ using list_dir, grep_search, or view_file for: CONTEXT_MAP.md, LEARNING_GRAPH.md, PRD.md, BLUEPRINT.md (or ARCHITECTURE.md), and CHECKPOINT.md. Also read tech stack identifiers (TECH_STACK.md, package.json, go.mod, etc.).Maintain project memory actively to prevent context exhaustion and hallucination.
Maintain Context Map (CONTEXT_MAP.md)
grep_search before modifying. Append new major components/routes immediately upon creation.Maintain Learning Graph (LEARNING_GRAPH.md)
<appDataDir>/knowledge). Inject this historical context proactively in future tasks.Triggers: Session is very long (>30 messages or >70% context used), major milestone reached, or user says "save checkpoint" / "handoff".
Action: Generate an ultra-compact YAML checkpoint block (<200 tokens).
Checkpoint Format:
# CHECKPOINT — [Project Name] — [Date]
project: "[Name]"
goal: "[One-line goal]"
status: "[e.g., 60% — backend API done]"
stack: "[Tech Stack]"
last_completed:
- "[Most recent task]"
next_tasks:
- "[Immediate next task]"
blockers: "[Blockers or 'none']"
key_files:
- "[path/to/critical.ts]"
env_needed: "[List of vars]"
notes: "[Critical context]"
Cross-Model Handoff Prompt: Use this universal template when moving across models (Gemini, Claude, GPT):
## RESUME CONTEXT — [Project Name]
**Goal**: [One-sentence goal]
**Current Status**: [Status]
**Tech Stack**: [Stack]
**Completed**: [Done items]
**Next Tasks**: 1. [Task 1] 2. [Task 2]
**Critical Files to Read First**: [File paths & reasons]
**Known Constraints**: [Constraints]
**Do NOT**: [Anti-patterns to avoid]
Start by reading the critical files, then confirm understanding before proceeding.
Checkpoint Storage Hierarchy:
CHECKPOINT.md in project root (Primary)..agents/CHECKPOINT.md (Alternative).<a name="bahasa-indonesia"></a>
Terhubung dan mengorkestrasi skill domain seperti brainstorming dan zero-to-prod-orchestrator untuk memastikan eksekusi kohesif selama siklus hidup sesi.
Skill ini mengatur memori agen: memuat konteks di awal sesi, memperbarui peta proyek dan grafik pembelajaran selama sesi berjalan, dan membuat checkpoint ringkas untuk perpindahan sesi (handoff).
Aktifkan SEGERA pada sesi baru, atau saat diminta "mulai sesi" / "muat konteks".
Alur Kerja:
.docs/: CONTEXT_MAP.md, LEARNING_GRAPH.md, PRD.md, BLUEPRINT.md, CHECKPOINT.md, dan file identifikasi tech stack (package.json, dsb).Pertahankan memori proyek agar tidak terjadi halusinasi dan hemat token.
Pelihara Peta Konteks (CONTEXT_MAP.md)
grep_search sebelum mengedit. Tambahkan entri baru saat membuat komponen/rute besar.Pelihara Grafik Pembelajaran (LEARNING_GRAPH.md)
<appDataDir>/knowledge). Gunakan pengetahuan masa lalu secara proaktif dengan menyebutkannya.Pemicu: Pesan > 30, jendela konteks > 70% terpakai, milestone tercapai, atau pengguna berkata "simpan checkpoint".
Tindakan: Hasilkan blok YAML super ringkas (<200 token) berisi: project, goal, status, stack, last_completed, next_tasks, blockers, key_files, env_needed, dan notes.
Prompt Handoff Lintas-Model: Gunakan template universal saat berpindah model: Berikan konteks proyek (Tujuan, Status, Stack, Tugas Selesai, Tugas Berikutnya), daftar file kritis untuk dibaca pertama kali, batasan, dan apa yang JANGAN dilakukan. Minta konfirmasi sebelum lanjut.
Hierarki Penyimpanan Checkpoint:
CHECKPOINT.md di root proyek (Utama)..agents/CHECKPOINT.md (Alternatif).아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Expert guide for automated and manual Web Accessibility (a11y) testing — axe-core, Pa11y, Playwright a11y, screen reader testing, and WCAG 2.2 Level AA/AAA compliance / Panduan ahli pengujian aksesibilitas web.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert guide for intelligent model cascading and routing — complexity-scored task routing from Flash/Haiku to Sonnet/Opus/Astra, dynamic escalation with quality gates, 40-60% token cost reduction while maintaining output quality / Panduan ahli untuk kaskade dan routing model cerdas — routing tugas berbasis skor kompleksitas dari Flash/Haiku ke Sonnet/Opus/Astra, eskalasi dinamis dengan gerbang kualitas, pengurangan biaya token 40-60% dengan kualitas output terjaga.
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Expert guide for Affective Computing, emotional AI, and real-time sentiment analysis through native multimodal tokens (voice intonation and facial micro-expressions) / Panduan ahli komputasi afektif, AI emosional, dan analisis sentimen real-time melalui token multimodal native.
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Expert guide for AI-assisted coding workflows — agentic code generation, multi-agent code swarms, self-healing CI/CD, automated PR review, spec-to-code pipelines, codebase knowledge graphs, and human-in-the-loop approval gates / Panduan ahli untuk workflow pengkodean berbasis AI — generasi kode agentic, code swarm multi-agen, CI/CD self-healing, review PR otomatis, pipeline spec-to-code, knowledge graph codebase, dan gate persetujuan human-in-the-loop.
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Expert guide for autonomous Machine-to-Machine (M2M) agent settlement, dual-rail fiat & stablecoin micro-clearing, dynamic budget envelopes, and cryptographic agent invoicing / Panduan ahli settlement otonom antar-agen (M2M), kliring mikro dual-rail fiat & stablecoin, pagu anggaran dinamis, dan penagihan agen kriptografis.
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Expert guide for long-term episodic memory integration (Mem0 v2, Letta/MemGPT, Zep v2), memory tier architecture, pgvector HNSW storage, and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang (Mem0 v2, Letta/MemGPT, Zep v2), arsitektur tier memori, penyimpanan pgvector HNSW, dan manajemen konteks terpadu untuk agen AI otonom.
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