Claude Skills by thedixitjain

| Drive the user's desktop in the background — clicking, typing, scrolling, dragging — without stealing the cursor, keyboard focus, or switching virtual desktops / Spaces. Cross-platform: macOS, Windows, Linux. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.

Configure a Rails project to work with Conductor (parallel coding agents)

Use when targeting Conference on Lifelong Learning Agents (CoLLAs) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for continual learning.

Garbage collection for your Claude Code configuration. Periodically scans ~/.claude (skills, memory, hooks, permissions, MCP servers, caches) for redundant, stale, orphaned, or low-value items, then walks the user through a confirm-each-deletion cleanup. Use when the user says \"clean up my config\", \"config GC\", \"too many skills\", \"audit my setup\", \"my .claude is bloated\", or asks for a periodic config review.

Interactive installer for Everything Claude Code — guides users through selecting and installing skills and rules to user-level or project-level directories, verifies paths, and optionally optimizes installed files.

Interactive installer for Everything Claude Code — guides users through selecting and installing skills and rules to user-level or project-level directories, verifies paths, and optionally optimizes installed files.

Content research and SEO writing methodology. Guides the agent through topic research, keyword identification, competitive analysis, and writing SEO-optimized content that ranks well and provides genuine value to readers.

Context window manager. Snapshots session state, compacts conversation, and resumes via hook. Use when context exceeds 70% or a large new task begins.

Agente de contexto para continuidade entre sessoes. Salva resumos, decisoes, tarefas pendentes e carrega briefing automatico na sessao seguinte.

Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.

This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions.

When agent sessions generate millions of tokens of conversation history, compression becomes mandatory. The naive approach is aggressive compression to minimize tokens per request.

This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context.

A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.

This skill should be used to explain or reason about the foundational concepts of context engineering: what context is, the anatomy of a context window, how attention mechanics work, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. Use this for conceptual explanation, onboarding, and background reading. Route operational work to the specialized skills: debugging attention failures ...

Guides you through creating context items — instructions, skills, knowledge pointers, and memory events — that give the AI analyst persistent knowledge about your organization. Covers when to create each type, naming conventions, folder organization, and the hard boundary between context (rules and procedures) and the semantic layer (logic and calculations).

Evaluate, adapt, and safely install Context Kit personal context artifacts for Claude Code or adjacent agent workflows.

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.

Manage context-mode GitHub issues, PRs, releases, and marketing with parallel subagent army. Orchestrates 10-20 dynamic agents per task. Use when triaging issues, reviewing PRs, releasing versions, writing LinkedIn posts, announcing releases, fixing bugs, merging contributions, validating ENV vars, testing adapters, or syncing branches.

MANDATORY routing rules for context-mode. Invoke whenever you analyze, count, filter, compare, search, parse, or transform data; fetch a URL; or run a data-heavy command — so raw bytes stay out of the context window.

ALWAYS-ON time-sensitivity discipline for the OpenClaw reference fixture.

Surface the rules, ADRs, specs, patterns, and reference docs that apply to a code area before changing it — or recap project focus when picking up work. Use for 'what rules apply to X', 'before I touch Y', 'what governs my current changes' (--git-changes), 'pick up where we left off'. With uncommitted changes in play you MAY run --git-changes once per task to load the rules for what you've touched (not per-edit). Not for creating docs, planning, or audits.

Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents.

Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.

具有质量门、评估和恢复控制的连续自主代理循环模式。

品質ゲート、評価、リカバリーコントロールを備えた継続的な自律エージェントループのパターン。

> 基于 instinct 的持续学习系统，通过 hooks 观察会话，创建带置信度评分的 atomic instincts， 并将高置信度 instinct 演进为 skills/commands/agents。v2.1 增加项目级 instincts 防止跨项目污染。

Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.

[DEPRECATED - use continuous-learning-v2] Legacy v1 stop-hook skill extractor. v2 is a strict superset with instinct-based, project-scoped, hook-reliable learning. Do not invoke v1; route continuous learning, session learning, and pattern extraction requests to continuous-learning-v2.

Wire Promptfoo and DeepTeam into CI/CD for automated regression red-teaming of LLM apps against OWASP LLM Top 10 and OWASP Agentic presets, failing the build when jailbreak or injection vulnerabilities regress.

> Use this skill when changing a machine-readable contract — a JSON Schema, an API spec, or a config schema — and bumping its version: tightening a constraint, adding/removing/renaming a field, introducing a breaking change, raising an API version, or writing the changelog entry for a schema change. Trigger on \"change the schema\", \"tighten this constraint\", \"bump the schema version\", \"breaking change to the API\", \"new API version\", \"changelog entry for a schema change\". Runs six p...

> Analyze and design control flows and data structures. Produces compact ASCII tree diagrams showing triggers, call chains, payload shapes, state mutations, and re-render effects. Use when user asks to diagram, trace, visualize, or design a flow or data structure.

>- Interactive Control Plan builder — takes PFMEA failure modes and process flow steps as input and builds a complete Control Plan row by row, with correct control methods, sample plans, and reaction plans for each characteristic. Use when building a new Control Plan, updating after a corrective action (8D D7), or converting a PFMEA into Control Plan format. Covers AIAG Control Plan reference manual and IATF 16949 §8.5.1.

Convert AgentOps skill formats.

Automate Convolo AI tasks via Rube MCP (Composio). Always search tools first for current schemas.

Use when adding, changing, testing, or debugging Cookiecutter templates, including cookiecutter.json variables, Jinja-rendered files, hooks, _copy_without_render, optional feature cleanup, and generated-project validation.

Connect, authenticate, configure, reset, or troubleshoot the Coolify Codex plugin connection. Use when the user asks to set up Coolify from the Codex marketplace, connect Coolify, configure saved credentials, log in, log out, switch Coolify instances, avoid environment variables, or resolve missing/invalid Coolify API token errors.

Build applications powered by GitHub Copilot using the Copilot SDK. Use when creating programmatic integrations with Copilot across Node.js/TypeScript, Python, Go, or .NET. Covers session management, custom tools, streaming, hooks, MCP servers, BYOK providers, session persistence, custom agents, skills, and deployment patterns. Requires GitHub Copilot CLI installed and a GitHub Copilot subscription (unless using BYOK).

Evaluate model performance — check for accuracy drops, data drift, and error patterns. Use when asked about \"model accuracy dropped\", \"evaluate the model\", \"check for drift\", or \"model performance\".

Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to \"build ML model\", \"train a model\", \"prediction pipeline\", \"classification\", or \"regression\".

ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked \"what ML do we have\", \"model inventory\", or \"ML assessment\".

Apply a simple code transform via agent-booster's WASM engine — sub-millisecond, deterministic, $0 (no LLM call). Companion to cost-booster-route.

Route tasks through hooks_route, partition by Agent Booster availability, and report Tier 1 bypass utilization with $0 cost

Generate a cost report showing token usage and USD costs by agent and model

Auto-capture per-session token usage from the Claude Code session jsonl and persist to the cost-tracking namespace

Track and report Claude Code token usage, spending, and budgets from the local ECC cost-tracker metrics log. Use when the user asks about costs, spending, usage, tokens, budgets, or cost breakdowns by model, session, or date.

4-voice parallel deliberation (Architect · Skeptic · Pragmatist · Critic) for architecture, tech selection, or design decisions with no clear answer. Anti-anchoring: each voice gets independent context. Records decision in harness-mem.

Collect independent perspectives for an

/cs:cpo-review <plan> — JTBD-driven interrogation of product roadmap, PMF signal, and portfolio focus. Use when committing a quarter's roadmap, deciding whether to kill a feature, or claiming PMF without a retention curve.

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