Use when reading today's US-market capital flow across multiple sectors to identify rotation direction — e.g. "今天资金流向", "板块强弱", "rotation map", "卖芯买云", "where is money moving today", "scan flows across sectors". Produces a cross-section snapshot of net inflows by cohort (indices / semis / software-cloud / mega-tech / AI applications), names the dominant narrative, and writes a dated journal file. Different from `market-session-tracker` (intraday live monitoring of a single watchlist) — this is a one-shot end-of-session rotation read.
npx skills add https://github.com/kansoku-trade/kansoku --skill capital-rotation
Scans capital flow across standard US cohorts in one session, identifies rotation direction, classifies winners / losers, names the dominant narrative, and logs a journal file.
> Scope: US-only. Do NOT query HK / CN / SG markets (user preference).
> Sources: Longbridge capital, market-temp. Cite as 长桥证券.
> Units: ambiguous — see TD-UNIT-01 in trading-discipline. Longbridge does not label the unit. Record the raw API number and the unit you inferred; do NOT silently convert (no 亿).
longbridge-capital-flow directly)market-session-tracker)| Cohort | Symbols |
| -------------------- | ------------------------------------------------------------------------------------------------- |
| Indices | SPY, QQQ, DIA, IWM |
| Semis | NVDA, AMD, MU, MRVL, TSM, AVGO, SMH, SOXX, AMKR, ASX |
| Software / Cloud | NOW, ORCL, CRM, ADBE, SNOW, DDOG, MDB, PLTR, PANW, CRWD, NET, IGV, CLOU |
| Mega-tech | AAPL, MSFT, GOOGL, AMZN, META, TSLA |
| Risk-off proxy | VXX, TLT, GLD (optional, for cross-asset confirmation) |
User watchlist override: read stocks/ directory for symbols the user already tracks; promote those to first-tier in their respective cohort.
date + confirm US session state (pre / intraday / post / closed). Adjust analysis date in filename: use the US session date, not Asia local date. longbridge market-temp US --format json
Report Temperature / Valuation / Sentiment.
longbridge capital SPY.US --format json
longbridge capital QQQ.US --format json
Net large = capital_in.large - capital_out.large. Flag distribution if large net ≪ 0 while small net > 0 (主力—散户背离).
longbridge capital <SYM> --flow --format json | tail -8 to grab the latest cumulative inflow value (the last array element is the running total in 万 USD). Parallelize across symbols.~/git/trade/journal/YYYY-MM-DD-flow.md using the US session date. Use templates/rotation-snapshot.md as scaffold. If the file exists (e.g. re-run same day), append a new section with timestamp; do not overwrite.Use these triggers to label index behavior:
| Pattern | Label |
| ----------------------------------------------------- | ----------------- |
| SPY large net < 0 AND \|large net\| > 5 × small net | 机构派发 |
| All 3 buckets (large / medium / small) net < 0 | 全档抛压 |
| Large net < 0, small net > 0, magnitudes similar | 主力—散户背离 |
| Large net > 0, small net < 0 | 主力吸筹 |
| All 3 buckets > 0 | 全档吸金 |
Always state the pattern explicitly; do not say "weak / strong" vaguely.
A common useful narrative axis. Classify cohort flow winners / losers by AI revenue maturity:
When flow winners cluster in "已变现" and losers in "未变现", call out "narrative 收敛至 AI 已变现窄口" — this is a key macro signal of late-cycle AI selectivity.
longbridge market-temp US --format json
longbridge capital SPY.US --format json # snapshot (large/med/small)
longbridge capital QQQ.US --flow --format json | tail -8 # time-series cumulative
longbridge capital --flow --format json < SYM > .US | tail -8 # per-symbol
The --flow last-row inflow field is the cumulative net for the session in 万 USD. No date parameter — today's data only.
Error: request timeout / connect timeout → retry 1-2 times; do not block the report. Mark unavailable symbols with n/a and proceed..SOX.US) → substitute ETF proxy (SMH/SOXX).Tone: 中文白话, no jargon — see TD-LANG-01 / TD-LANG-02 in trading-discipline.
market-session-tracker — live intraday monitoring of one watchlistlongbridge-capital-flow — single-symbol drill-downlongbridge-market-temp — sentiment-only snapshotstock-deep-dive — multi-lens single-name researchcapital-rotation/
├── SKILL.md
└── templates/
└── rotation-snapshot.md
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take kansoku-trade/capital-rotation from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.