Capital Rotation logo

Capital Rotation

Organization
kansoku-trade
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.

Overview

Publisherkansoku-trade
Repositorykansoku
Skill namecapital-rotation
Stars
329
Forks
40
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by kansoku-trade on GitHub. Read the source before you install it.

Installation

Install the Capital Rotation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kansoku-trade/kansoku/tree/main/.claude/skills/capital-rotation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kansoku-trade/kansoku.git /tmp/kansoku
mkdir -p .claude/skills
cp -r /tmp/kansoku/.claude/skills/capital-rotation .claude/skills/capital-rotation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Capital Rotation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Capital Rotation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Capital Rotation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

CohortSymbols
IndicesSPY, QQQ, DIA, IWM
SemisNVDA, AMD, MU, MRVL, TSM, AVGO, SMH, SOXX, AMKR, ASX
Software / CloudNOW, ORCL, CRM, ADBE, SNOW, DDOG, MDB, PLTR, PANW, CRWD, NET, IGV, CLOU
Mega-techAAPL, MSFT, GOOGL, AMZN, META, TSLA
Risk-off proxyVXX, 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.

Workflow

  1. Time check — date + confirm US session state (pre / intraday / post / closed). Adjust analysis date in filename: use the US session date, not Asia local date.

  2. Market temperature — single call:

    bash
    longbridge market-temp US --format json

    Report Temperature / Valuation / Sentiment.

  3. Index baseline — snapshot mode (gives large/medium/small breakdown):

    bash
    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).

  4. Cohort scan — for each cohort, 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.

  5. Rotation classification — for each cohort, sum net flows; rank symbols within cohort; identify:

    • Cohort net (sector-level direction)
    • Cohort leader (largest +)
    • Cohort outlier (largest −, especially if cohort net is positive)
    • Cross-cohort rotation: which cohort gained vs lost net flow
  6. Narrative identification — pick ONE of:

    • 卖芯买云 (semis − / software +)
    • 卖云买芯 (inverse)
    • 全板派发 (all cohorts negative + indices large-out)
    • 全板吸金 (all positive + indices large-in)
    • 窄口集中 (one cohort dominated by 1-2 names; rest flat)
    • 风险偏好切换 (cyclicals out / defensives in)
    • AI 已变现 vs 未变现 (rank by AI revenue maturity, see §Narrative criteria)
  7. Write journal file — ~/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.

  8. Surface 3 insights + tomorrow watch — concise, actionable. No vague "市场分化", always name the split.

Distribution detection rules

Use these triggers to label index behavior:

PatternLabel
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.

Narrative criteria — "AI 已变现 vs 未变现"

A common useful narrative axis. Classify cohort flow winners / losers by AI revenue maturity:

  • 已变现 (likely to attract flow): MU (HBM revenue confirmed), NOW (AI workflow ARR), ORCL (AI cloud bookings), AMD (MI-series sales), AMZN (AWS Bedrock), PLTR (gov + commercial AIP)
  • 未变现 / 纯叙事: SNOW, MRVL (AI guidance hasn't translated), CRWD (AI security narrative), AAPL (Apple Intelligence vague), GOOGL (Gemini monetization weak)

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.

CLI quick reference

bash
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.

Failure modes

  • Error: request timeout / connect timeout → retry 1-2 times; do not block the report. Mark unavailable symbols with n/a and proceed.
  • Cohort scan during US pre-market (04:00–09:30 ET) → data exists but thin; flag report as "pre-market preliminary, not full-session".
  • Symbol unavailable on Longbridge (e.g. .SOX.US) → substitute ETF proxy (SMH/SOXX).

Output format (chat reply)

  1. Header: market state + session date
  2. Index baseline table
  3. Cohort tables (one per cohort)
  4. Narrative label in bold
  5. 3 insights — each with a number and 1-2 sentence claim
  6. Tomorrow watch — 4-6 bullets, each with explicit symbol + condition

Tone: 中文白话, no jargon — see TD-LANG-01 / TD-LANG-02 in trading-discipline.

Anti-patterns

  • ❌ Querying HK / CN / SG markets (user said US-only)
  • ❌ Reporting "市场分化" without naming the split
  • ❌ Treating one-symbol-dominated cohort net as broad strength (e.g. semis "+3.4 亿" but MU alone is +4.2 亿)
  • ❌ Skipping the journal write step
  • ❌ Skipping the narrative label (must pick one)
  • ❌ Single-point prediction; use scenario language for tomorrow watch

Related skills

  • market-session-tracker — live intraday monitoring of one watchlist
  • longbridge-capital-flow — single-symbol drill-down
  • longbridge-market-temp — sentiment-only snapshot
  • stock-deep-dive — multi-lens single-name research

File layout

capital-rotation/
├── SKILL.md
└── templates/
    └── rotation-snapshot.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Capital Rotation AI 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.

Why use Capital Rotation on TypingMind?

Because you install it once and use it with any model. Capital Rotation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Capital Rotation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kansoku-trade/kansoku/tree/main/.claude/skills/capital-rotation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Capital Rotation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Capital Rotation?

As many as you like. As long as a model supports skills, you can use Capital Rotation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Capital Rotation AI skill free?

It is published on GitHub by kansoku-trade. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇