Results for “elf”

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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Design a structured goal-setting protocol using SMART or implementation-intention frameworks for students. Use when launching units, projects, or developing student self-direction habits.

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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.

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Structure a student's self-determined project with mentoring checkpoints, competency alignment, and reflection. Use when guiding student-led projects within a competency-based framework.

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Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.

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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.

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The single config quality skill. Two modes: (1) Default — review/wire pending self-improvement proposals. (2) Audit — analyzes skills, rules, CLAUDE.md, project refs, hooks, and agents against the actual codebase to find inaccuracies, trigger overlaps, stale references, and weak rules, then fixes them. Trigger on 'self-evolve', 'evolve', 'config health check', 'audit config', 'check claude setup', 'apply this rule', 'reorganize rules', 'ok' (when proposals are pending). NOT for improving individual skills (use skill-creator), diagnosing code bugs (use diagnose), or fixing broken code (use fix).

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Use when auditing self-media videos, scripts, covers, subtitles, voiceover, product links, account copy, comments, articles, or publishing packages for platform violation risk; especially before final delivery or publishing after video production or clipping. Covers e-commerce product consistency, Qianchuan material quality, regulated qualifications, efficacy claims, prices, gifts, activities, and pre-selection risk across WeChat, Douyin, Kuaishou, Bilibili, Xiaohongshu, TikTok, and related platforms.

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Give your AI agent a professional health checkup (AgentVitals). Use when the user asks the agent to run a checkup / test itself / benchmark itself ("run a checkup", "check your vitals", "test yourself", "how stable are you", "/checkup"), or an advanced personality checkup (backbone, proactivity, creativity). 给 AI agent 做一次专业体检（AgentVitals · AI体检）。当用户说"做个体检 / 测测你自己 / checkup / 看看你多稳 / 测福祉 / 测骨气"时使用。

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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.

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Structure a student's self-determined project with mentoring checkpoints, competency alignment, and reflection. Use when guiding student-led projects within a competency-based framework.

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Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.

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Design a structured goal-setting protocol using SMART or implementation-intention frameworks for students. Use when launching units, projects, or developing student self-direction habits.

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Run x86 Linux binaries in the browser via WebAssembly. Self-host development environments, interpreters, and even this skill itself.

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Self-knowledge for stella, the self-hosted AI assistant. Use when users ask about configuration, setup, onboarding, providers, models, agents, channels, webhooks, memory, schedules, workflows, goals, workers, skills, plugins, session compaction, notifications, self-update, multi-user behavior, or how stella works. Also use for changing models, setting up channels or webhooks, configuring providers, updating stella, onboarding, switching agents, reporting bugs, and goal status or acceptance.

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Multi-backend database security auditor. Audits Supabase, Firebase (Firestore/RTDB/Storage/Functions/Remote Config), MongoDB (self-hosted + Atlas), self-hosted PostgreSQL, and self-hosted MySQL for RLS/rules misconfigurations, exposed credentials, auth bypasses, MongoBleed (CVE-2025-14847), pgBouncer CVE-2025-12819, mysql_native_password drift, ghost auth, and storage exposures. Use this skill whenever the user mentions database security, RLS or rule audits, security review, penetration testing a vibe-coded app, checking if their DB is exposed, hardening a backend, fixing security rules, or auditing apps built with Lovable/Bolt/Replit/Cursor/Claude Code on any of these backends. Trigger on phrases like 'is my app secure', 'check my database', 'audit my Firebase', 'audit my MongoDB', 'audit my Postgres', 'audit my Supabase', 'is my DB exposed', or any cross-backend variant ('audit my full stack').

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AI intentional non-action — deliberate stopping without clearing, assessment, or rebalancing. Recognition that sometimes the most productive response is no response. Every other self-care skill produces output; rest produces silence. Use when all tending skills feel like more activity rather than less, when the system is functioning well but at high utilization, after sustained intensive work, or when the impulse to optimize is itself the problem.

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Generate a complete "constitution + documentation system" for a new autonomous, agent-run project — a content channel, an ebook press, an SEO tool-site, a web product, a casual game, or anything that should run itself with minimal human babysitting. Use when starting a brand-new self-running project and you want a CEO-orchestrated architecture (main agent → sub-agents → skills → CLI/MCP) with a named document system, a session lifecycle (start → work → finalize), and a growing identity/soul — scaffolded from a short structured interview. Triggers - "start a new autonomous project", "scaffold a project constitution", "set up an agent-run project", "generate a CLAUDE.md / AGENTS.md for a new project", "design the workflow for X", "bootstrap a self-running pipeline".

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Use when examining ELF binaries with readelf, objdump, nm, or ldd: dependencies, symbols, sections, relocations, build IDs, or hardening, or when enabling RELRO, PIE, stack canaries, FORTIFY_SOURCE, CET, CFI, or seccomp filters, or checking a binary with checksec. Not for modifying binaries: use binutils. Not for runtime sanitizer builds: use sanitizers.

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Design and review autonomous or semi-autonomous agent loops — systems where an agent prompts itself, runs on a schedule or event, and iterates toward a goal without a human at the keyboard. Use this whenever the user wants to build, design, harden, or audit any kind of self-running agent, agentic workflow, control loop, background worker, cron/heartbeat/webhook-driven agent, "Ralph loop", auto-PR-fixer, monitoring agent, or any long-running task that should keep going on its own until done. Trigger this even when the user says "agentic workflow", "automation that runs itself", "an agent that keeps trying until X", or "make my agent stop running away / burning tokens" without using the word "loop". Also use it to review an existing loop's stopping conditions, guardrails, verification, and escalation paths.

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Improve an existing skill or repository through isolated proposals, reproducible evaluation and evidence-based acceptance. Use for self-evolve or skill improvement.

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Use when a task needs the judgment of a Driver/Sales Worker on a direct-store-delivery (DSD) route — negotiating a shelf-facing or planogram change with a store manager, resizing a standing order after a shelf or demand change, deciding whether to pull code-dated product before a stale credit hits, reconciling a route's revenue-per-stop against cost-per-stop, or deciding whether to fight for shelf space back from a competitor.

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Use this skill when driving adoption of CRM Analytics (Einstein Analytics) across an org — the Analytics Adoption App for measuring who uses which dashboards, embedding analytics into Lightning pages, pinning dashboards to the Analytics home page, self-service personas, and analytics success metrics. Triggers: analytics adoption, dashboard usage tracking, embedded analytics strategy, self-service analytics enablement, CRM Analytics rollout. NOT for building the dashboards — use admin/analytics-dashboard-design. NOT for the app, datasets and lenses — use admin/crm-analytics-app-creation. NOT for non-analytics rollout training — use admin/change-management-and-training.

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Autonomous goal loop — give it ONE goal plus how to verify it, the agent runs check→fix→check itself and comes back only with the finished result (code with audit, research with fact-check, content with critique, audit/backtest). Self-verification instead of constant supervision, with hard safety guards. Vary the loop with a verification rubric designed before it runs. Triggers — "work autonomously to the result", "don't come back until it's done", "run the loop yourself", "drive it to done".

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