| name | stock-analysis |
| description | Generate A-share individual stock fundamental and industrial-chain research reports from a stock code using the reviewed stock-analysis source tree. Use when the user explicitly asks for A-share stock code analysis, 个股分析, 股票基本面分析, 股票研究报告, A股研报, HTML stock report generation, or A股产业链卡脖子/龙头买入逻辑. Do not use for global non-mainland-A-share leader screens. |
| license | MIT |
| source | https://github.com/mingli30119/stock-analysis |
| source_path | ~/.hermes/external-repos/stock-analysis |
| security_review | medium |
Stock Analysis
Use this skill to generate an A-share individual stock fundamental analysis report from a stock code. When the prompt asks for 产业链, 卡脖子, 龙头, 买入逻辑, or AI-era suppliers, include the chokepoint lens below instead of producing a generic valuation-only report. If the user asks for 全球股/全球产业链 and says to exclude mainland A-shares, route away from this skill.
Scope
- Source root:
~/.hermes/external-repos/stock-analysis.
- Runtime data collector:
stock_full_report.py.
- Data source: AkShare public market/financial endpoints.
- Output convention: write results under the current project's
output/ directory unless the user names another destination.
- The report is for research only and is not investment advice.
Safety Rules
- Do not install Python dependencies globally unless the user explicitly asks. Prefer a project-local virtual environment.
- Do not create or edit MCP configs or store API keys unless the user explicitly asks.
- Treat live market/news/financial fetches as external network calls; run them only when the user asks for an actual report.
- If the user only asks for routing or install, do not fetch stock data.
Workflow
- Confirm the stock code is a six-digit A-share code or infer it only when the name is unambiguous.
- From a project/work directory, prepare dependencies if needed:
. ~/.hermes/skills/data-science/stock-analysis/.venv/bin/activate
- Run Phase 1 data collection:
~/.hermes/skills/data-science/stock-analysis/.venv/bin/python ~/.hermes/external-repos/stock-analysis/stock_full_report.py <股票代码>
- Read
output/data_<股票代码>.json.
- Draft the Markdown report with the Step 0-8 framework from the upstream skill.
- If HTML is requested, use the upstream
shared/ assets and examples/个股研究-中国长城.html as visual reference; write the final HTML under output/.
Chokepoint / 产业链 Mode
Use this mode when the user asks from demand waves, AI产业链, 卡脖子, 龙头, 买入逻辑, 前瞻TAM, qualification cycle, 垄断, 功能性独占, or 价值链向上爬.
Output contract:
- First line: only the A-share leader ticker/name when the user explicitly asks for A-share leaders.
- Do not mix mainland A-share leaders into a global-stock leader answer unless the user explicitly asks for A-shares.
- Put all reasoning, caveats, valuation, and buy logic after the first line.
Frame the thesis from demand backward:
- Demand wave: what secular demand shock is forcing a new architecture or capacity bottleneck?
- Architecture bottleneck: which physical/process/material function becomes scarce?
- Cannot be designed away: why customers cannot easily bypass it through redesign, second source, software substitution, or vertical integration.
- Material revenue: how the company can convert the bottleneck into revenue large enough to matter.
- Qualification cycle: evidence from certification, customer qualification, platform inclusion, long-term orders, capacity reservation, or process/tool approval.
- Early/small preference: prefer smaller or earlier-stage names only after the chokepoint filter passes.
Use this scoring formula as the ranking spine:
excess return =
major demand trend
× insufficient supply elasticity
× low market recognition
× catalyst
- valuation / liquidity / dilution / geopolitical risk
Do not let current-period financials dominate when the user's thesis is explicitly qualification-cycle / forward-TAM based. Still name thesis-break conditions: design-out evidence, failed qualification, revenue not scaling after the expected cycle, dilution, customer loss, or policy/geopolitical block.
IBKR/TWS API fallback for US/global watchlists
When the user explicitly requires IBKR/TWS API data (for example port 4002) and the Python ibapi/ib_insync packages are unavailable, use the raw TWS socket protocol rather than silently switching providers:
- Connect to
127.0.0.1:<port> and send the v100 handshake.
- Send
START_API (71) before any request.
- Use
reqMatchingSymbols (81) to resolve symbols to IBKR contracts/conIds.
- Use delayed market data when subscriptions block live data:
REQ_MARKET_DATA_TYPE (59) with type 3.
- Use
REQ_MKT_DATA (1) with message version 11 for stock quotes.
- Treat
10167 Requested market data is not subscribed. Displaying delayed market data... as a valid delayed-data status, not a hard failure.
Reference: references/ibkr-tws-raw-api.md.
Reusable probe: scripts/ibkr_tws_raw_probe.py.
First Probe
~/.hermes/skills/data-science/stock-analysis/.venv/bin/python ~/.hermes/external-repos/stock-analysis/stock_full_report.py 000001 --max-kline-years 1
