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 skloxo/TideTrading --skill private-company-researchgit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/private-company-research)<a href="https://agentmods.dev/skills/skloxo/tidetrading/private-company-research"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/private-company-research/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/skloxo/tidetrading/private-company-research"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/private-company-research.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 | $0.00153 | $0.03287 |
| Opus 5 | $0.00077 | $0.01643 |
| Sonnet 5 | $0.00031 | $0.00657 |
| Haiku 4.5 | $0.00015 | $0.00329 |
Grade A, and why
private-company-research 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 12d 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.
This is a copy
100% identical to private-company-research — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Private-Company Research: Multi-Lens Deep Framework
Deep research on an unlisted company (e.g. Ant Group, ByteDance, SpaceX, Stripe).
Ultimate goal: under information scarcity, recover the company's true value — not the market valuation, but what the business is actually worth.
Framework Characteristics
Private vs public research: no standardized financials (multi-source patchwork + cross-validation); few valuation anchors (funding rounds, comparables, scenarios); large information asymmetry ("jigsaw" research); uncertain exit path (IPO / M&A / secondary).
AI Research Bias Self-Check (core premise)
Private companies are where AI bias is worst. Watch for:
- False conservatism — with little data, AI gives conservative/vague conclusions, but scarce data ≠ bad company.
- False precision — to fill the template, AI disguises "reasonable guess" as "sourced analysis".
- Comparables trap — forcing a public-comp overlay inherits public-market logic and misses private-specific value.
- Survivorship bias — what's searchable online is mostly company-propagated good news.
Counter: prefer leaving blanks ("I don't know") over filling tables with speculation to fake certainty; label every data point with confidence (🟢high/🟡medium/🔴low); separate verifiable fact from inference; when information is extremely scarce, switch to "first-principles mode" and answer only: ① what real problem does this business solve? ② why this team? ③ ceiling if it succeeds / how it dies if it fails? ④ the key validation node at this stage?
Invert the asymmetry: the market knows little about private companies → pricing is inefficient → that's exactly where alpha may live.
Execution
Six lenses, best run in parallel (via run_swarm, one worker per lens; or sequentially via web_search):
| Role | Lens |
|---|---|
| business-decoder | Business model + product/user analysis: "what is this business, essentially" |
| financial-detective | Financial patchwork + valuation: "recover the true financial picture under missing data" |
| competitive-mapper | Industry + competition + substitution: "who competes, who could disrupt" |
| risk-governance-analyst | Risk全景 + management/governance/investors: "what could go wrong, who's at the helm" |
| tech-ip-analyst | Tech stack / patents / R&D / moat: "is the tech barrier real and durable" |
| signal-miner | Alternative data (hiring / patents / litigation / app / supply chain): "clues beyond the usual sources" |
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.
- 12d ago First seen · 161 lines · 153 tokens per session scan A 1581466137bc
private-company-research is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed yesterday), licensed MIT. It adds 153 tokens to every session and 3,287 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to private-company-research, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
daily-deep-brief
A scheduled, pre-market investment briefing for Hong Kong and United States stocks. A deterministic preparation step gathers data and an agent adds judgment, while a later step validates and publishes the result.
hk-stock-analysis
A workspace-aware analysis workflow for Hong Kong-listed stocks. It retrieves prices, technical indicators, market comparisons, and news through a local data pipeline, then adds Hong Kong-specific investment context.
us-stock-analysis
Workspace-aware US stock analysis for kcn. Routes through clawock analyze-us / clawock us-quotes instead of generic web search, then layers fundamental/technical/news analysis on top. Use when user asks to analyze a US ticker (e.g. "analyze AAPL", "look at RKLB", "compare TSLA vs NVDA"), check earnings, run…
portfolio-swarm-review
Multi-agent swarm review of kcn's current holdings. Inspired by TauricResearch/TradingAgents framework already in workspace — three-tier analysis (analysts → bull/bear debate → risk debate + judge) with confidence scoring. Use for post-close reviews, holiday/next-session planning, pre-add sizing decisions, and any…
investment-decision
Run a clawock investment decision — read the prepared request, research with the host's own tools, write decision.json with evidence and an explicit bull/bear debate, and let Python validate and settle. Use when the user asks for an investment decision or a clawock run request is present.
invest-analyst
A framework for producing professional investment research, including company reports, industry studies, event analysis, analyst-expectation reviews, comparisons, and market summaries. It connects several investment research workflows into one process.