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 w95/awesome-claude-corporate-skills --skill teasergit clone --depth 1 https://github.com/w95/awesome-claude-corporate-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/w95/awesome-claude-corporate-skills/teaser)<a href="https://agentmods.dev/skills/w95/awesome-claude-corporate-skills/teaser"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/teaser/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/w95/awesome-claude-corporate-skills/teaser"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/teaser.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 | $0.00000 | $0.00642 |
| Opus 5 | $0.00000 | $0.00321 |
| Sonnet 5 | $0.00000 | $0.00128 |
| Haiku 4.5 | $0.00000 | $0.00064 |
Grade A, and why
teaser 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.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Teaser
description: Draft anonymous one-page company teasers for sell-side M&A processes. Creates a compelling summary without revealing the company's identity, designed to gauge buyer interest before NDA execution. Triggers on "teaser", "blind teaser", "anonymous profile", "one-pager for process", or "draft teaser for sell-side".
Workflow
Step 1: Gather Inputs
- Company description (what they do, how they make money)
- Sector / industry
- Key financial metrics: revenue, EBITDA, growth rate, margins
- Geographic footprint
- Key selling points (3-5 highlights)
- What to anonymize vs. disclose
- Target buyer audience (strategic, financial, or both)
Step 2: Teaser Structure
One page, professionally formatted:
Header
- Deal code name (e.g., "Project [Name]")
- Sector descriptor (e.g., "Leading Specialty Industrial Services Platform")
- "Confidential — For Discussion Purposes Only"
Company Description (2-3 sentences)
- What the company does, without naming it
- Market position (e.g., "a leading provider of...", "a top-3 player in...")
- Geography (region-level, not city-specific)
Investment Highlights (4-6 bullet points)
- Market leadership / positioning
- Revenue quality (recurring %, retention, diversification)
- Growth profile and trajectory
- Margin profile and expansion opportunity
- Management team strength
- Strategic value / synergy potential
Financial Summary (table or key metrics)
| Metric | Value |
|---|---|
| Revenue | $XXM |
| Revenue Growth | XX% CAGR |
| EBITDA | $XXM |
| EBITDA Margin | XX% |
| Employees | XXX |
Transaction Overview (2-3 sentences)
- What's being offered (100% sale, majority stake, growth equity)
- Indicative timeline
- Contact information for expressions of interest
Step 3: Anonymization Check
Ensure the teaser doesn't inadvertently identify the company:
- No company name, brand names, or product names
- No specific city (use region: "Southeast US", "Midwest")
- No named customers or partners
- No employee count if it's too distinctive
- Revenue ranges instead of exact figures if the sector is small
- No logos, screenshots, or identifiable imagery
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 · 78 lines · 0 tokens per session scan A 75d521f0b538
teaser is a skill published in the GitHub repository w95/awesome-claude-corporate-skills (198 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 642 tokens. 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
financial-expert
A financial research skill for China and Hong Kong securities, funds, company finances, economic indicators, research reports, announcements, news, and business-risk data. It returns data and neutral analysis rather than placing trades or recommending investments.
stock-analysis-lead
Orchestrate a US-stock investment analysis — classify sector archetype, fetch SEC filings, dispatch a tiered fan-out of six vertical equity-research agents (business model, earnings quality, balance sheet, management, industry, peer comparison) over a validated JSON findings contract, then synthesize a buy/hold/sell…
stock-business-review
Review a US-listed company's business model and revenue structure for an equity-research workup. Covers product/service mix, customer concentration, geographic exposure, industry position, revenue-growth decomposition (organic vs acquired vs price vs volume), and information-tier discipline (which numbers are facts vs…
stock-earnings-quality-review
Review a US-listed company's earnings quality, cash-flow integrity, and operating leverage for an equity-research workup. Covers operating cash flow vs net income drift, free cash flow trajectory, capex character (maintenance vs expansion), equity issuance / shareholder-return yield, revenue-quality signals…
stock-industry-review
Review a US-listed company's industry position and competitive moat for an equity-research workup. Covers Porter Five Forces scan, market-share trend (absolute and relative to industry growth), TAM size and trajectory, unit economics where disclosed (LTV/CAC, unit gross margin), moat classification (network / brand /…
stock-management-review
Review a US-listed company's management quality and capital-allocation track record for an equity-research workup. Covers 5-year capital-allocation history (buybacks vs dividends vs M&A vs capex vs debt), buyback timing, M&A return-on-investment, guidance-vs-actuals track record, comp-structure alignment, insider…