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 spikeHongg/china-stock-research-skills --skill industry-competition-moatgit clone --depth 1 https://github.com/spikeHongg/china-stock-research-skillsWrote 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/spikehongg/china-stock-research-skills/industry-competition-moat)<a href="https://agentmods.dev/skills/spikehongg/china-stock-research-skills/industry-competition-moat"><img src="https://agentmods.dev/badge/skills/spikehongg/china-stock-research-skills/industry-competition-moat/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/spikehongg/china-stock-research-skills/industry-competition-moat"><img src="https://agentmods.dev/badge/skills/spikehongg/china-stock-research-skills/industry-competition-moat.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 | $0.00062 | $0.00550 |
| Opus 5.5 | $0.00025 | $0.00220 |
| Sonnet 5.5 | $0.00012 | $0.00110 |
| Haiku 4.5 | $0.00006 | $0.00055 |
Grade A, and why
industry-competition-moat 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.
How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Industry Competition Moat
Place the company inside its real industry structure and test whether it has defensible economics.
Non-Negotiable Rules
- Use public policy, statistics, procurement, and company disclosures to validate industry claims.
- Separate company claims from third-party validation.
- State which competitor comparison points are fact and which are inference.
- Do not repeat detailed company financial scoring here.
- Default every intermediate and final output to Simplified Chinese unless the user explicitly requests another language.
Read First
skills/shared/references/source-registry.mdskills/shared/references/citation-standard.mdskills/shared/references/analysis-pattern-selector.mdskills/shared/references/language-output-standard.mdskills/shared/references/sector-adaptation-checklist.mdskills/industry-competition-moat/references/moat-grid.md- If the company is in a policy-sensitive or asset-heavy sector, load the relevant shared overlay first.
- If the company is an IDC or AI-compute infrastructure case,
skills/shared/references/idc-heavy-asset-transition-overlay.mdandskills/industry-competition-moat/references/idc-value-chain-and-peers.mdare optional example overlays.
When to Use
- Assess industry attractiveness and rivalry.
- Compare the company with listed peers and alternative business models.
- Test whether policy, capital, qualification, data, channel, or switching cost creates a moat.
Workflow
- Define the value chain and the company's exact position in it.
- Pull industry-policy and demand data from official sources.
- Select a source-backed peer set and compare capacity, clients, margins, and business model.
- Apply a moat grid and a five-forces style structure.
- Highlight where the company captures value and where it remains vulnerable.
Required Output
事实: industry size, policy tailwind/headwind, peer positions, value-chain role解读: moat strength, structural advantage, likely pressure points待验证问题: missing peer metrics, uncertain supply additions, unclear regulationValue Chain Map: each link labeled strong, medium, or weak capture ability
What ships with it
2 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.
- 2mo ago First seen · 55 lines · 62 tokens per session scan A d5ae72948354
industry-competition-moat is a skill published in the GitHub repository spikeHongg/china-stock-research-skills (18 stars, last pushed 8mo ago), licensed MIT. It adds 62 tokens to every session and 550 once invoked, about $0.0002 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.
industry-competition-moat compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| industry-competition-moatspikeHongg/china-stock-research-skills | Skill | 18 | 8mo ago | MIT | A | 62 | 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 |