plugins/context-master/skills/context-master/SKILL.md
Universal context management and planning system. PROACTIVELY activate for: (1) ANY complex task requiring planning, (2) Multi-file projects/websites/apps, (3) Architecture decisions, (4) Research tasks, (5) Refactoring, (6) Long coding sessions, (7) Tasks with 3+ sequential steps. Provides: optimal file creation order, context-efficient workflows, extended-thinking delegation (~23x context efficiency), passive deep-analysis architecture, progressive task decomposition. Environment-agnostic — works for Web, API, and Claude Code CLI.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace context-masterInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Universal context management and planning skill for complex tasks, long sessions, and multi-file projects. Works the same way whether you are in the Web app, the API, or Claude Code CLI; CLI-specific bonuses live in references/claude-code-cli.md.
This SKILL.md is intentionally short. Detailed material lives in references/ — load only what you need.
Activate proactively whenever the request matches one of:
If the request is a single file with no dependencies, skip this skill.
For any multi-file project — websites, apps, APIs, documentation sets — run these five steps in order:
references/multi-file-planning.md).User: "Create a portfolio with home, about, projects, and contact pages."
Step 1 STOP -- do not start with index.html.
Step 2 PLAN -- "Think hard about architecture: 5 files needed,
styles.css is the shared dependency."
Step 3 ANNOUNCE -- "I'll create:
1. styles.css (shared styling)
2. index.html (references styles.css)
3. about.html
4. projects.html
5. contact.html"
Step 4 CREATE -- write files in that order.
Step 5 VERIFY -- every HTML file links styles.css; navigation resolves.
Result: no refactor, no re-export, no broken links.
The full template, optimal-order patterns by project type (website / React / backend API / etc.), and verification checklists are in references/multi-file-planning.md.
references/multi-file-planning.md.Full rationale and examples for every principle: references/universal-best-practices.md.
| Project size | Without planning | With planning | Savings | |---|---|---|---| | Small (3-4 files) | ~6,000 tokens | ~2,500 tokens | ~58% | | Medium (7-8 files) | ~12,000 tokens | ~4,500 tokens | ~63% | | Large (20+ files) | ~35,000 tokens | ~12,000 tokens | ~66% |
A 200K-token context window holds roughly 16-17 medium projects with planning, 7-8 without — a 2.1x effective increase. Numbers and methodology in references/multi-file-planning.md.
The single most powerful pattern for context efficiency: route deep reasoning into an isolated space and return a short summary to the main thread.
/agent deep-analyzer "Ultrathink about [decision]" — ~5K tokens of reasoning happens out-of-band; main context receives a ~200-token summary (~23x efficiency)."Create a deep-analysis artifact and ultrathink about [decision]" — same idea, artifact instead of subagent.Triggers, anti-patterns, and ready-made prompts: references/thinking-delegation.md.
Each of these has a step-by-step procedure in references/workflows.md:
Quick list — examples and fixes in references/anti-patterns.md:
When sessions drift (responses unfocused, conversations getting too long, code regenerating instead of editing, extended thinking not engaging), see references/troubleshooting.md for a symptom → remedy table covering Web/API and CLI separately.
If you are running in Claude Code CLI, you also get:
/clear, /compact, /continue — built-in context controls./agent <name> — delegate to an isolated subagent.CLAUDE.md — persistent project memory.CLAUDE.md and subagent definitions.Details, script invocations, and the deep-analysis delegation patterns: references/claude-code-cli.md.
Skill–subagent integration patterns (when both are available) live in references/agent-skills-integration-2025.md. Long-form context strategies and subagent prompt patterns: references/context_strategies.md and references/subagent_patterns.md.
| File | Use when |
|---|---|
| references/multi-file-planning.md | Planning template, optimal-order patterns by project type, verification checklists, post-project reflection. |
| references/universal-best-practices.md | Need the rationale or examples for any of the 8 core principles. |
| references/workflows.md | Need a step-by-step procedure for a specific scenario (decision, feature, research, refactor). |
| references/thinking-delegation.md | Designing a deep-analysis delegation or writing the thinking prompt. |
| references/anti-patterns.md | Reviewing whether the current approach is wasting context. |
| references/troubleshooting.md | Session is drifting, slow, or producing too much explanation. |
| references/claude-code-cli.md | Running in Claude Code CLI and want subagent / CLAUDE.md tooling. |
| references/agent-skills-integration-2025.md | Combining skills with subagents in CLI. |
| references/context_strategies.md | Long-form strategy notes. |
| references/subagent_patterns.md | Subagent prompt patterns. |
If any of those failed, walk back through references/anti-patterns.md and references/multi-file-planning.md post-project section before the next task.
development
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.