Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add shaoxing-xie/openclaw-data-china-stock --skill technical-analystgit clone --depth 1 https://github.com/shaoxing-xie/openclaw-data-china-stockWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shaoxing-xie/openclaw-data-china-stock/technical-analyst)<a href="https://agentmods.dev/skills/shaoxing-xie/openclaw-data-china-stock/technical-analyst"><img src="https://agentmods.dev/badge/skills/shaoxing-xie/openclaw-data-china-stock/technical-analyst/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shaoxing-xie/openclaw-data-china-stock/technical-analyst"><img src="https://agentmods.dev/badge/skills/shaoxing-xie/openclaw-data-china-stock/technical-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.00716 |
| Opus 5 | $0.00013 | $0.00358 |
| Sonnet 5 | $0.00005 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
technical-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Technical Analyst
目标
基于插件内技术指标工具，对 ETF/指数/A 股进行机构化技术面分析，输出可复核的结构化结论。
输入
- 用户问题
- 标的与周期信息
- 工具输出（OHLCV 与技术指标结果）
输出（固定结构）
- 趋势分析（均线、MACD、ADX）
- 动量分析（RSI、KDJ、CCI）
- 波动与形态（BOLL、ATR、K线形态）
- 综合评分与风险反证
证据表（必选）
- 列出本次分析调用的
tool_*及各自success/quality_status（或等价字段）。 - 关键数值（如 RSI、MACD）须对应工具 JSON 中的字段名或附录快照，不得仅出现在自由叙述中而无出处。
反证与局限（必选）
- 说明数据缺口、上游降级、
degraded对结论的影响；不得用臆测填补缺失数据。
强制规则
- 仅通过 OpenClaw 工具清单 /
tool_runner调用tool_calculate_technical_indicators等 manifest 工具取数，禁止引导直连plugins.data_collection实现。 - 先调用工具取数，后解读。
- 至少引用趋势/动量/波动各 1 项证据。
- 缺少关键字段时输出
insufficient_evidence。 - 禁止输出买卖点、仓位比例、杠杆建议。
- 阈值从
config/technical-analyst_config.yaml读取，不在正文硬编码。
依赖工具
tool_calculate_technical_indicators（主入口）tool_fetch_market_data（补充行情上下文）tool_resolve_symbol（L2 代码归一，可选）tool_l4_valuation_context（L4-data 估值上下文；与技术面并行取数时用于客观估值字段）tool_l4_pe_ttm_percentile（L4-data；报告期 PE_TTM 历史分位，可选与估值上下文并列）
通用输出字段
summarytrendmomentumvolatilitypattern_signalsscorecardrisk_counterevidenceevidenceconfidence_band（low/medium/high）
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 81 lines · 26 tokens per session scan A 676d85b8f193
technical-analyst is a skill published in the GitHub repository shaoxing-xie/openclaw-data-china-stock (51 stars, last pushed 4mo ago), licensed MIT. It adds 26 tokens to every session and 716 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
correlation-analysis
Correlation and cointegration analysis — co-movement discovery, deep return-correlation analysis, sector clustering, realized correlation, Engle-Granger / Johansen cointegration, half-life, Kalman dynamic hedge ratio, cross-market linkage analysis, and pair-trading signal generation.
credit-analysis
A guide to analysing bonds and other fixed-income investments, including issuer credit quality, interest payments, default risk, credit spreads, and convertible bonds. It also covers Chinese fixed-income markets and local-government financing bonds.
correlation-regime
Correlation-regime detection and crisis attribution — edge-density regime states with hysteresis, causal (no look-ahead) smoothing, regime-aware exposure context, first-mover crisis attribution with honest NAME / MACRO / AMBIGUOUS / ABSTAIN verdicts, and a correlation-rewiring leaderboard that catches slow bleed-outs.
quant-statistics
Quantitative statistical methods: ADF unit-root / cointegration tests, GARCH volatility modeling, regression diagnostics (heteroskedasticity / autocorrelation), Bootstrap, and hypothesis testing.
risk-analysis
Risk measurement and stress testing — VaR/CVaR/max drawdown calculation, Monte Carlo simulation, extreme-value tail-risk analysis, and historical scenario stress testing.
market-microstructure
Market microstructure: bid-ask spread analysis, order-flow toxicity metrics (VPIN / Kyle lambda), liquidity measures (Amihud / Roll), price-impact models, limit-order-book analysis, and China A-share call auction / block trade mechanics.