context-master
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.
What this skill does
# Context Master
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.
## When to activate
Activate proactively whenever the request matches one of:
- Multi-file project (3+ related files, websites, apps, APIs, doc sets).
- Architecture or technology decision ("should we use X or Y", "which approach…").
- Research / comparison / tradeoff analysis.
- Refactoring or migration work.
- Long coding session where context efficiency matters.
- Any task with 3+ sequential steps or shared dependencies.
If the request is a single file with no dependencies, skip this skill.
## The 5-step workflow (memorize this)
For **any** multi-file project — websites, apps, APIs, documentation sets — run these five steps in order:
1. **STOP** — do not create any files yet.
2. **PLAN** — use extended thinking *or* a planning document (both equally valid; see `references/multi-file-planning.md`).
3. **ANNOUNCE** — state the file list and creation order to the user.
4. **CREATE** — write files in optimal order (foundations before dependents).
5. **VERIFY** — check that every reference resolves; fix issues before declaring done.
### Worked example (portfolio website)
```text
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`.
## Core principles (one line each)
1. **Plan before you write** — extended thinking or planning doc, never both skipped.
2. **Foundations first** — shared dependencies (CSS, config, base classes, schemas) precede dependents.
3. **Delegate deep analysis to isolated contexts** — subagents (CLI) or thinking artifacts (Web/API) keep the main thread clean.
4. **Phase boundaries** — name each phase ("Phase 1 complete; moving to Phase 2: …") so context doesn't blur.
5. **Artifacts over inline content** — long code or docs live in artifacts/files, not chat history.
6. **Progressive disclosure** — ask for one slice at a time; don't batch unrelated steps.
7. **Signal resets** — say "setting aside the previous approach…" when changing direction.
8. **Reflect after large projects** — was it planned? Did references break? Use the post-project checklist in `references/multi-file-planning.md`.
Full rationale and examples for every principle: `references/universal-best-practices.md`.
## Token-savings rule of thumb
| 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`.
## Passive deep-thinking architecture
The single most powerful pattern for context efficiency: route deep reasoning into an isolated space and return a short summary to the main thread.
- **Claude Code CLI**: `/agent deep-analyzer "Ultrathink about [decision]"` — ~5K tokens of reasoning happens out-of-band; main context receives a ~200-token summary (~23x efficiency).
- **Web / API**: `"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`.
## Common workflows (pointers only)
Each of these has a step-by-step procedure in `references/workflows.md`:
- Workflow 0 — Multi-file website/project creation (the default — same 5 steps above with extra detail).
- Workflow 1 — Complex decision-making (architecture, tech choice).
- Workflow 2 — Complex feature development (analysis → design → implement → test → integrate).
- Workflow 3 — Research and technology evaluation.
- Workflow 4 — Code generation and iteration via artifacts.
- Workflow 5 — Refactoring with isolated analysis.
## Anti-patterns (avoid these)
Quick list — examples and fixes in `references/anti-patterns.md`:
1. Creating files before planning — wastes context on refactors.
2. Asking for everything at once — overloads context and produces shallow output.
3. Inlining long code/docs in chat — bloats history; use artifacts/files.
4. Letting deep reasoning happen in main thread — costs ~5K tokens that could be ~200.
5. No phase boundaries — context blurs across unrelated subtasks.
## Troubleshooting
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.
## Claude Code CLI bonuses
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.
- Helper scripts for generating `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`.
## Reference map
| 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. |
## Success indicators
- You announced the plan before creating any file.
- Foundation files (CSS, config, schemas) were written before dependents.
- No reference broke on first run; no refactor was needed.
- Deep reasoning happened in a subagent or artifact, not the main thread.
- Phases were named as you crossed them.
If any of those failed, walk back through `references/anti-patterns.md` and `references/multi-file-planning.md` post-pRelated in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.