Build quick IRR/MOIC sensitivity tables for PE deal evaluation. Models returns across entry multiple, leverage, exit multiple, growth, and hold period scenarios. Use when sizing up a deal, stress-testing assumptions, or preparing IC returns exhibits. Triggers on "returns analysis", "IRR sensitivity", "MOIC table", "what's the return at", "model the returns", or "back of the envelope".
npx skills add https://github.com/anthropics/financial-services --skill returns-analysis
Ask for (or extract from prior analysis):
Entry:
Financing:
Operating Assumptions:
Exit:
Calculate:
| Metric | Value |
|--------|-------|
| Entry EV | |
| Equity invested | |
| Exit EBITDA | |
| Exit EV | |
| Net debt at exit | |
| Exit equity value | |
| MOIC | |
| IRR | |
| Cash-on-cash | |
Show the returns waterfall:
Build 2-way sensitivity matrices:
Entry Multiple vs. Exit Multiple
| | Exit 6x | Exit 7x | Exit 8x | Exit 9x | Exit 10x |
|---|---------|---------|---------|---------|----------|
| Entry 7x | | | | | |
| Entry 8x | | | | | |
| Entry 9x | | | | | |
| Entry 10x | | | | | |
EBITDA Growth vs. Exit Multiple (at fixed entry)
Leverage vs. Exit Multiple (at fixed entry and growth)
Hold Period vs. Exit Multiple
Show both IRR and MOIC in each cell (IRR / MOIC format).
Build 3 scenarios:
| | Bull | Base | Bear |
|---|------|------|------|
| Revenue CAGR | | | |
| Exit EBITDA margin | | | |
| Exit multiple | | | |
| Exit EBITDA | | | |
| MOIC | | | |
| IRR | | | |
Plan, write, and diagnose Instagram Reels that earn cold-audience reach. Use whenever someone wants a reels script or reels hook for a specific Reel, is debugging why a Reel flopped, wants to know if a draft is worth testing with Trial Reels before going public, or needs a reels caption tuned for the post-hashtag instagram algorithm. Built around what Mosseri has publicly named as the signal hierarchy (watch time, sends per reach, likes per reach), the Trial Reels test-then-publish loop, the Original Content Guidelines and 30-day recovery window, the Edits app, and Reels Insights metrics (skip rate, share rate, followers from this post). Covers a Reels-specific reels strategy: send-driving CTAs, originality without watermarks, audio licensing by account type, captions as the primary SEO signal, and the anti-patterns that quietly cap distribution. Pattern-based guidance, not a virality promise.
Design lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.
Amazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.
When the user wants to create UGC ad campaigns, recruit UGC creators, generate AI UGC content, or scale with user-generated content. Also use when the user mentions 'UGC,' 'user-generated content,' 'creator ads,' 'Spark Ads,' 'whitelisting,' 'AI UGC,' 'Arcads,' 'Creatify,' 'creator brief,' or 'UGC testing.' This skill covers the UGC growth framework from creator recruitment through AI-powered scaling. Do NOT use for technical implementation, code review, or software architecture.
Parse, modify, validate, and patch simulator input files. Use when working with reservoir simulation input files, testing scenarios, or validating simulation configurations. This implementation supports reference format (.DATA); other simulators use different extensions (e.g., .afi, .DAT). Supports natural language modifications, keyword patching, and syntax validation.
Triage ASM/recon output for ownership before testing — separate the target's real assets from namespace-collision noise. Automated recon keyword-matches on the brand name, so for any target whose name is a common/dictionary word, the output is dominated by assets belonging to UNRELATED same-named companies (repos, cloud buckets, mobile apps, breach corpora, typosquats). Built from an authorized engagement where an ASM report's "Criticals" were overwhelmingly false positives and the combo/repos/mobile/bucket lists were polluted with unrelated same-named orgs. Use at the START of any engagement, immediately on receiving any ASM/recon/OSINT dataset, BEFORE testing anything.
Use when platform-reported conversions disagree with GA4/ecommerce, when you suspect Meta and Google are double-counting the same sales, or for a standing (monthly) reconciliation workbook that de-dups stacked credit against an order-ID truth set, normalizes attribution windows and currency, compares attribution models, and reads incrementality from a geo/holdout test. Not for the point-in-time R2 veto or RQS gate — use ad-account-auditor; not for the ROI/ROAS ratio math itself — use roi-calculator; not for organic dark-social share attribution or GA4 direct-traffic decomposition — use dark-social-attributor. 付费广告归因对账/去重/增量
Use when the user asks to "QA my conversion tracking before launch", "check my UTMs / pixel / event firing", "set up a tracking pre-flight", or "set the dedup rule so Meta and Google stop double-counting"; builds and fixes the measurement plumbing — conversion-event firing, UTM hygiene, cross-platform dedup rules, attribution-window alignment, and offline/iOS-ATT modeled-gap flags — as a pre-flight checklist plus a UTM/event-spec builder. Not for scoring R1/R2 — that is a scored veto in ad-account-auditor; not for account structure — use campaign-architect. 付费广告转化追踪QA/UTM规范/跨平台去重
Take anthropics/returns-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.