'elf' 검색 결과

Check opencode commands, HTTP server behavior, lifecycle events, and terminal startup with purpose-built scripts. Find stored sessions by ID, title, or scoped message searches, and export conversations as JSON for further inspection.

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• Check CLI commands and HTTP server health
• Observe lifecycle events associated with plugin hooks
• Check terminal startup and keyboard input

Store team conventions, troubleshooting notes, and project knowledge on a self-hosted Hindsight server. Retrieve relevant context before new tasks and draw on recorded experiences, including successful procedures, failed attempts, and individual preferences attributed to specific people.

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• Share project conventions across the team
• Record fixes and troubleshooting procedures
• Recall past decisions before starting work

Review open commitments in acc and record success or failure with supporting notes. Distinguish self-assessment from owner validation or observed real-world results, and keep commitments open when they are still waiting on an outcome.

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• Reviewing open commitments in acc
• Closing commitments with verified outcomes
• Distinguishing self-assessment from external validation

Assess your Claude Code experience with a quick checklist or a deeper review across 10 feature areas. Receive a skill profile, identified gaps, and a personalized learning path with priorities, estimated study time, and practice exercises.

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• Find a starting point for learning Claude Code
• Review experience across feature areas
• Prioritize topics to learn next

Add social-emotional learning—practicing emotional awareness, self-management, empathy, and collaboration—to subject lessons in OpenMAIC. It connects these goals to academic concepts through discussion, choices, feedback, retries, and reflection.

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• Adding emotional check-ins to the start of a subject lesson
• Practicing empathy through discussion of different viewpoints
• Building feedback and retry paths into learning activities

Turn work history into clear accomplishment statements for performance reviews, promotion cases, and weekly updates. Use Copilot CLI sessions, git commits, and GitHub pull requests to connect each action with its result and supporting evidence.

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• Recovering work accomplishments missing from a personal log
• Preparing evidence for a performance review
• Organizing achievements for a promotion case

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

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• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Set up encrypted connections between devices with Tailscale and control access by user or device. The skill covers network routing, device name resolution, and self-hosting the coordination server with Headscale.

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• Replacing a traditional VPN with Tailscale
• Restricting server access by team or device
• Connecting remotely to internal networks

Connect NanoClaw to an existing Signal account through signal-cli, a command-line tool for Signal. Use personal chats, Note to Self, or group conversations to exchange messages with an AI assistant without registering a new number.

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• Contact an assistant through Signal's Note to Self
• Use an assistant in a small Signal group
• Link an existing Signal number without registering a new one

Prepare a graded Claude Managed Agent for recurring schedules, event triggers, or on-demand use. Validate timing, build deployment request data, optionally include self-evaluation for each run, and document the next steps in a roadmap.

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• Schedule an agent to run every morning or weekly
• Prepare a curl trigger for an external event
• Add self-evaluation to each recurring run

Use an English working contract in Trae to address repeated failures or unverified completion. It separates execution, self-review, and acceptance, guiding agents through small checks and delivering results with evidence, residual risks, and confirmation needs.

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• Restarting a stalled task in Trae with a different approach
• Requesting command output to support a completion claim
• Reviewing changes with evidence and remaining risks

Execute development subtasks under a supervising agent, starting with an implementation plan and impact analysis. Deliver code and a three-question self-review in a defined completion format for the supervisor to assess.

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• Delegating an implementation subtask to an agent
• Planning changes and assessing their impact before coding
• Handing over code with a structured self-review

Turn completed research, analysis, reports, or plans from the current session into a self-contained HTML artifact. A renderer controls layout, typography, and color, using structured content and a choice of two visual styles.

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• Turn research findings into an HTML artifact
• Format a cost analysis with tables and callouts
• Present investigation findings as a designed document

Turn Matomo analytics into reports on organic traffic, landing pages, devices, countries, referrers, and search keywords. Use data from a self-hosted instance or Matomo Cloud to support reporting alongside GA4 or as an alternative.

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• Review organic traffic trends in Matomo
• Identify popular landing pages
• Compare visits by device, country, and referrer

Design how an AI agent edits its own working conversation history, choosing what to retain, rewrite, or remove. Covers safeguards, capacity monitoring, recovery after overflow, and the computing cost of changing earlier context.

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• Designing agents that edit their own conversation history
• Preserving exact values during long tasks
• Recovering from context overflow

Track machine learning experiments with SwanLab by recording training settings, metrics, and media such as images and audio. Learn to compare runs, inspect training charts, and connect existing training code to local, cloud, or self-hosted dashboards.

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• Comparing experiments with different training settings
• Viewing training metrics in a local dashboard
• Logging sample images and audio alongside metrics

Helps decide whether to handle a task directly, create a subagent, or route it to an agent already familiar with the work. The choice depends on required background knowledge and whether plausible but incorrect results can be checked.

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• Choosing who should handle a task
• Delegating self-contained research with verifiable sources
• Routing context-heavy reviews to an informed agent

Assess your Claude Code knowledge through multiple-choice questions with immediate feedback. Choose a quick check or a comprehensive assessment to identify gaps and receive a prioritized learning path, with practice projects included in the comprehensive mode.

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• Checking your current Claude Code knowledge
• Choosing which topics to learn next
• Planning team training around knowledge gaps

Understand SenseNova Team Harness, a workspace you host yourself for collaboration between people and local AI agents. Learn its core concepts, startup requirements, and workflow for assigning tasks, sharing input files, and reviewing versioned deliverables.

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• Assess whether SenseNova Team Harness fits a team
• Learn how shared spaces, tasks, and deliverables relate
• Get started with a self-hosted workspace

Revise an academic paper based on internal review comments. Reads review report or annotated manuscript, then applies revisions one by one with user approval. Trigger when user says "self revise" / "paper-selfrevise" / "内部修改" / "根据审稿意见修改".

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Archive the current Cloudroom thread and release its runtime. Use only when the user explicitly asks to self-archive, archive yourself, or archive this thread.

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[omh] Agent is stuck, looping, or drifting: capture a stuck, looping, drifting, or repeatedly failing agent run, diagnose the likely failure pattern, and prepare the smallest safe recovery action. Use when the user says: agent-debug, agent debug, agent debugging, agent introspection, agent self-debug, self-debug, self debugging, looping agent.

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Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. Use when building agents that get better over time, managing auto- memory, or designing self-correcting feedback loops.

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Generate scaffolds supporting student self-regulation across planning, monitoring, and evaluation phases. Use when students struggle to manage their own learning during extended or independent tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.