Agent skill

capital-rotation

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.

kansoku-tradegithub.com/kansoku-tradeGitHub ↗
claude-codeNOASSERTION
Install
npx skills add kansoku-trade/kansoku --skill capital-rotation --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Path: .claude/skills/capital-rotation/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 272
Language: TypeScript

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Capital Rotation Scanner (US-only) 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). ## When to use - "今天的资金流向", "板块强弱", "rotation map" - "卖芯买云", "AI 资金挪去哪了" - "软件 / 云强不强", "半导体板块今天主力撤了吗" - End-of-session debrief or pre-Asia-open prep - **Not** for single-symbol deep-dive (use `longbridge-capital-flow` directly) - **Not** for live intraday tracking (use `market-session-tracker`) ## Standard cohorts | Cohort | Symbols | | -------------------- | ------------------------------------------------------------------------------------------------- | | **Indices** | `SPY`, `QQQ`, `DIA`, `IWM` | | **Semis** | `NVDA`, `AMD`, `MU`, `MRVL`, `TSM`, `AVGO`, `SMH`, `SOXX`, `AMKR`, `ASX` | | **Software / Cloud** | `NOW`, `ORCL`, `CRM`, `ADBE

What's inside
Steps it walks through
  1. When to use
  2. Standard cohorts
  3. Workflow
  4. Distribution detection rules
  5. Narrative criteria — "AI 已变现 vs 未变现"
  6. CLI quick reference
  7. Failure modes
  8. Output format (chat reply)
  9. Anti-patterns
  10. Related skills
  11. File layout
Ships with 1 file
  • templates/rotation-snapshot.md
Commands it runs
longbridge market-temp US --format json
longbridge capital SPY.US --format json
longbridge capital QQQ.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
More from kansoku
All skills →
About this skill
What does the capital-rotation skill do?

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.

How do I install it?

Run `npx skills add kansoku-trade/kansoku --skill capital-rotation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From kansoku-trade/kansoku, a repository with 272 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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