48건 · 관련도순
Track work across sessions with persistent issues, dependencies, and progress notes. Use the bd command-line tool to find unblocked work, claim issues, record completion, and recover task context after conversation compaction.
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Persistent AI companion with long-term memory, project context awareness, and multi-session continuity across any LLM.
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여러 차례 인터뷰로 브랜드의 목적, 고객상, 개성과 말투를 정리합니다. 창업자들의 공통점과 인식 차이도 살펴 협업자에게 공유할 브랜드북으로 만듭니다.
대규모 기능 개발이나 마이그레이션 목표를 여러 차례 수행할 실행 계획으로 나눕니다. 단계별 배경, 의존 관계, 검증 방법을 정리해 다른 에이전트에게 작업을 인계할 수 있게 합니다.
여러 차례의 대화로 브랜드의 목적, 고객상, 말투를 구체화합니다. 답변과 진행 상황을 저장하고 브랜드북으로 정리해 창업자의 생각을 제작 담당자와 공유하도록 돕습니다.
Manage shared project tasks in Beads, from finding available work and claiming tasks to tracking dependencies, recording follow-up work, and closing completed items. Keep project status available across sessions and handoffs.
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Research an explicitly requested Marin question or coordinate a multi-session experiment; find prior work, assess evidence, and record results in the task's existing durable surface.
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Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary. Computes the version-jump path, inserting the 2.11.x stepping-stone and Python-transition step when needed. Chooses an approach by whether run history and the same environment (URL/ARN) must be kept: a new-environment upgrade (blue-green), a rehearsed in-place upgrade validated on a test copy, or a direct in-place upgrade. Runs Ruff scanning and deprecation-warning log scans for 3.x moves, plus Docker validation, batched deployment, and switchover. Saves a resumable upgrade plan for multi-session work. Triggers on: upgrade MWAA, upgrade Airflow, migrate to Airflow 3, MWAA Airflow 3, Airflow 2 to 3, preserve Airflow history, keep same MWAA environment, blue-green cutover, validation environment before cutover. Not for authoring new DAGs (authoring-mwaa-workflow), debugging unrelated DAG failures (debugging-mwaa-workflow), or MWAA Serverless YAML workflows (provisioned Python DAGs only).
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Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work that needs multiple sessions. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, or user says "just do it".
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Run a large, multi-session goal (e.g. shipping a whole side product) through the full V-model closed loop, one phase at a time, with cross-session state and a final north-star acceptance gate. Ultragoals never downgrade the lane: every phase runs CP-1→CP-6 with adversarial verification. Opt-in: invoke with /ultragoal or by calling something a long-running goal. Ordinary work does not run this.
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Context management engine for AI coding agents. Use when building agent memory systems, optimizing context windows, allocating token budgets, designing RAG pipelines for code, or managing persistent multi-session agent state.
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Review the week using accumulated session evidence — retrieval rates, hint depths, calibration accuracy, transfer and unassisted results. The learner identifies patterns and sets a strategy goal. Use weekly or after a multi-session period.
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Break a PRD into native GitHub sub-issues attached to the parent PRD. Project-local variant of /to-issues, adapted for this repo's PRD-as-parent + native sub-issues + agent:implement multi-session workflow. Argument is the parent PRD issue number.
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Teach a skill or concept at the user's current level, optionally preserving a lightweight multi-session learning record.
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Map a large, unclear, multi-session effort as linked decisions, then advance the map until the route to the destination is clear.
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Hierarchical memory organization for multi-session context retention. Wings (projects) > Rooms (domains) > Drawers (decisions). Semantic search across all memories with zero cloud dependency.
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Persistent planning system with 3 markdown files (PLAN.md, PROGRESS.md, CONTEXT.md). Use when starting large features, multi-session work, or complex refactoring. Auto-tracks commits in PROGRESS.md.
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Track complex, multi-session work with dependency graphs using bd (beads) issue tracker. Use when work spans multiple sessions, has complex dependencies, or requires persistent context across compaction cycles. For simple single-session linear tasks, TodoWrite remains appropriate.
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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface: 6 tools with --full (adds check_action); 13 tools with AR_EXTRAS=1 --full. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.
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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.
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Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project tracking.
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Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project tracking.
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Use when a learner or instructor asks for a report, summary, reflection, progress note, performance recap, activity metrics, instructor-shareable record of a MATLAB AI tutoring session, aggregate report across multiple MATLAB tutoring session reports, or instructor dashboard artifact with metric drilldowns. Supports optional start and end datetime arguments for multi-session report and dashboard ranges.
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Writes a handover document so another person, another agent, or a different tool can pick up multi-session work cold. Use when handing work to someone else, switching tools, or pausing work that will not resume in the same session.
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