bootstrap-monorepo
Autonomous polyglot monorepo bootstrap meta-prompt. TRIGGERS - new monorepo, polyglot setup, scaffold Python+Rust+Bun, monorepo from scratch.
What this skill does
# Bootstrap Polyglot Monorepo This skill redirects to the canonical reference in mise-tasks. → **See**: [mise-tasks/references/bootstrap-monorepo.md](../mise-tasks/references/bootstrap-monorepo.md) > **Self-Evolving Skill**: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues. ## When to Use This Skill Use this skill when: - Starting a new polyglot monorepo from scratch - Setting up Python + Rust + Bun/TypeScript project structure - Need autonomous 9-phase bootstrap workflow (includes release setup) - Want Pants + mise integration for affected detection ## Stack | Tool | Responsibility | | --------- | ---------------------------------------------------------------------- | | **mise** | Runtime versions (Python, Node, Rust) + environment variables | | **Pants** | Build orchestration + native affected detection + dependency inference | ## Quick Commands ```bash # After bootstrap, use these Pants commands: pants --changed-since=origin/main test # Test affected pants --changed-since=origin/main lint # Lint affected pants tailor # Generate BUILD files pants list :: # List all targets ``` ## Related Skills - `itp:mise-tasks` - Task orchestration and affected detection (Level 11) - `itp:mise-configuration` - Environment and tool version management - `itp:semantic-release` - Release automation (Phase 8 reference) --- ## Troubleshooting | Issue | Cause | Solution | | -------------------------- | -------------------------- | ------------------------------------------------- | | Pants not found | Not installed | Install via `brew install pantsbuild/tap/pants` | | mise not loading | Shell hook not configured | Configure mise shell hook in ~/.zshrc | | BUILD files not generated | Missing `pants tailor` | Run `pants tailor` to generate BUILD files | | Affected detection empty | No base branch set | Ensure `origin/main` exists and is up to date | | Python version mismatch | mise vs Pants conflict | Align Python version in mise.toml and pants.toml | | Rust targets not found | Pants Rust backend missing | Enable Rust backend in pants.toml | | Node/Bun not detected | Not in mise tools | Add to mise.toml: `node = "latest"` or `bun` | | Dependency inference fails | Missing imports in source | Ensure explicit imports, run `pants tailor` again | ## Post-Execution Reflection After this skill completes, check before closing: 1. **Did the command succeed?** — If not, fix the instruction or error table that caused the failure. 2. **Did parameters or output change?** — If the underlying tool's interface drifted, update Usage examples and Parameters table to match. 3. **Was a workaround needed?** — If you had to improvise (different flags, extra steps), update this SKILL.md so the next invocation doesn't need the same workaround. Only update if the issue is real and reproducible — not speculative.
Related 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.