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 deal-screeninggit 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/deal-screening)<a href="https://agentmods.dev/skills/w95/awesome-claude-corporate-skills/deal-screening"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/deal-screening/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/deal-screening"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/deal-screening.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 | $0.00534 |
| Opus 5 | $0.00000 | $0.00267 |
| Sonnet 5 | $0.00000 | $0.00107 |
| Haiku 4.5 | $0.00000 | $0.00053 |
Grade A, and why
deal-screening 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
97% identical to deal-screening — 7 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deal Screening
description: Quickly screen inbound deal flow — CIMs, teasers, and broker materials — against the fund's investment criteria. Extracts key deal metrics, runs a pass/fail framework, and outputs a one-page screening memo. Use when reviewing new deal flow, triaging inbound materials, or deciding whether to take a first call. Triggers on "screen this deal", "review this CIM", "should we look at this", "triage this teaser", or "deal screening".
Workflow
Step 1: Extract Deal Facts
From the provided CIM, teaser, or description, extract:
- Company: Name, location, sector/subsector
- Description: What they do (1-2 sentences)
- Financials: Revenue, EBITDA, margins, growth rate
- Deal type: Platform, add-on, recap, minority, carve-out
- Asking price / valuation: Multiple, enterprise value if stated
- Seller motivation: Why selling now
- Management: Rolling or exiting
- Key customers: Concentration risk
- Key risks: Obvious red flags
Step 2: Screen Against Criteria
Apply the fund's investment criteria (ask user if not known):
| Criterion | Target | Actual | Pass/Fail |
|---|---|---|---|
| Revenue range | |||
| EBITDA range | |||
| EBITDA margin | |||
| Growth profile | |||
| Sector fit | |||
| Geography | |||
| Deal size / EV | |||
| Valuation (x EBITDA) | |||
| Customer concentration | |||
| Management continuity |
Step 3: Quick Assessment
Provide a 3-part assessment:
- Verdict: Pass / Further Diligence / Hard Pass
- Bull case (2-3 bullets): Why this could be a good deal
- Bear case (2-3 bullets): Key risks and concerns
- Key questions: What you'd need to answer on a first call
Step 4: Output
One-page screening memo suitable for sharing with partners or an IC quick screen.
Important Notes
- Speed matters — screening should take minutes, not hours
- Be direct about red flags. Don't bury concerns
- If financials seem inconsistent or incomplete, flag it explicitly
- Ask for the fund's criteria upfront if this is the first screening
- Save screening criteria in memory for future deals once confirmed
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 · 58 lines · 0 tokens per session scan A 9ced2d63f272
deal-screening 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 534 tokens. A static security scan graded it A with 0 findings. It is 97% identical to deal-screening, differing in 7 lines, and is treated as a copy.
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-balance-sheet-review
Review a US-listed company's balance sheet health for an equity-research workup. Covers net-debt/EBITDA leverage, current ratio, cash runway, goodwill concentration and impairment history, DSO trend, inventory days, off-balance-sheet items (operating leases, contingent liabilities), and pension underfunding. Trigger…
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 /…