What is china-stock-research-orchestrator?

A coordinator for researching companies listed on China’s A-share stock markets. It selects and orders research modules, while keeping verified facts, interpretation, and unanswered questions separate.

Use it to start a source-backed deep dive on a Chinese public company. It helps choose research steps, add sector-specific analysis when needed, and structure the final report in Simplified Chinese by default.

It provides a consistent route through a complex company investigation and requires important facts and figures to have accessible sources.

china-stock-research-orchestrator — Skill by spikeHongg

Install

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.

Any agent

npx skills add spikeHongg/china-stock-research-skills --skill china-stock-research-orchestrator

Clone the repo

git clone --depth 1 https://github.com/spikeHongg/china-stock-research-skills

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/spikehongg/china-stock-research-skills/china-stock-research-orchestrator/github.svg)](https://agentmods.dev/skills/spikehongg/china-stock-research-skills/china-stock-research-orchestrator)

Your own site

<a href="https://agentmods.dev/skills/spikehongg/china-stock-research-skills/china-stock-research-orchestrator"><img src="https://agentmods.dev/badge/skills/spikehongg/china-stock-research-skills/china-stock-research-orchestrator/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/spikehongg/china-stock-research-skills/china-stock-research-orchestrator"><img src="https://agentmods.dev/badge/skills/spikehongg/china-stock-research-skills/china-stock-research-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 813 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00072	$0.00813
Opus 5.5	$0.00029	$0.00325
Sonnet 5.5	$0.00014	$0.00163
Haiku 4.5	$0.00007	$0.00081

Measured 2mo ago against content hash eccbd7d690fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

china-stock-research-orchestrator 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 2mo 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.

skills/china-stock-research-orchestrator/SKILL.md · 76 lines

How it starts

The opening of the file, as written. The whole thing — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.

China Stock Research Orchestrator

Route an A-share company through the right research workflow before doing deep analysis.

Non-Negotiable Rules

Preserve strict separation between Facts, Interpretation, and Open Questions.
Require every key fact and number to carry an accessible source link.
Prefer official disclosures, regulators, and public official portals over media and commentary.
Use this skill to orchestrate the workflow, not to replace the six core modules.
Default every intermediate and final output to Simplified Chinese unless the user explicitly requests another language.

Read First

skills/shared/references/source-registry.md
skills/shared/references/citation-standard.md
skills/shared/references/company-disclosure-map.md
skills/shared/references/research-output-template.md
skills/shared/references/analysis-pattern-selector.md
skills/shared/references/language-output-standard.md
skills/china-stock-research-orchestrator/references/routing-matrix.md
skills/china-stock-research-orchestrator/references/sector-overlay-escalation-rules.md
skills/china-stock-research-orchestrator/references/final-output-contract.md

Required Inputs

Company name and ticker if available
Research objective or key question
As-of date if the user cares about a specific cutoff
Optional hints about industry, business model, or suspected issues

When to Use

Start a fresh A-share stock deep-dive.
Turn a vague request like "analyze this stock" into a structured workflow.
Decide which of the six core modules are necessary before going deep.
Merge module outputs into one consistent research framework.

Workflow

Identify the company's primary and secondary analysis patterns with skills/shared/references/analysis-pattern-selector.md.
Use skills/china-stock-research-orchestrator/references/routing-matrix.md to decide which core modules are mandatory, optional, or skippable.
Decide whether a sector overlay is needed using skills/china-stock-research-orchestrator/references/sector-overlay-escalation-rules.md.
Run the selected modules in the right order, usually strategy -> business -> financial -> industry -> risk -> valuation.
Require each module to return mergeable 事实, 解读, and 待验证问题 blocks in Simplified Chinese.
Merge the outputs using skills/china-stock-research-orchestrator/references/final-output-contract.md.

Read the full file on GitHub · 76 lines

Files

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.

Changes

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.

2mo ago First seen · 76 lines · 72 tokens per session scan A eccbd7d690fe

Subscribe to this mod's changes

china-stock-research-orchestrator is a skill published in the GitHub repository spikeHongg/china-stock-research-skills (18 stars, last pushed 8mo ago), licensed MIT. It adds 72 tokens to every session and 813 once invoked, about $0.0003 per session on Opus 5.5. 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.

Claude Code Finance and fintech Research agent-skills china-stock codex company-research equity-research

china-stock-research-orchestrator next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
china-stock-research-orchestratorspikeHongg/china-stock-research-skills	Skill	18	8mo ago	MIT	A	72	Claude Code, Codex
private-company-researchxbtlin/ai-berkshire	Skill	17k	2d ago	MIT	A	33	Claude Code
bottleneck-hunterxbtlin/ai-berkshire	Skill	17k	2d ago	MIT	A	41	Claude Code
investment-researchxbtlin/ai-berkshire	Skill	17k	2d ago	MIT	A	43	Claude Code
earnings-teamxbtlin/ai-berkshire	Skill	17k	2d ago	MIT	A	34	Claude Code
income-investmentxbtlin/ai-berkshire	Skill	17k	2d ago	MIT	A	27	Claude Code
industry-funnelxbtlin/ai-berkshire	Skill	17k	2d ago	MIT	A	37	Claude Code