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 leecyno1/boutique-skills --skill anthropic-fs-private-equity-returns-analysisgit clone --depth 1 https://github.com/leecyno1/boutique-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/leecyno1/boutique-skills/anthropic-fs-private-equity-returns-analysis)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/anthropic-fs-private-equity-returns-analysis"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-private-equity-returns-analysis/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/leecyno1/boutique-skills/anthropic-fs-private-equity-returns-analysis"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-private-equity-returns-analysis.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 | $0.00088 | $0.00864 |
| Opus 5 | $0.00044 | $0.00432 |
| Sonnet 5 | $0.00018 | $0.00173 |
| Haiku 4.5 | $0.00009 | $0.00086 |
Grade A, and why
returns-analysis 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 8d 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 returns-analysis — 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Returns Analysis
Workflow
Step 1: Gather Deal Inputs
Ask for (or extract from prior analysis):
Entry:
- Entry EBITDA (LTM or NTM)
- Entry multiple (EV / EBITDA)
- Enterprise value
- Net debt at close
- Equity check size
- Transaction fees & expenses
Financing:
- Senior debt (x EBITDA, rate, amortization)
- Subordinated debt / mezzanine (if any)
- Total leverage at entry (x EBITDA)
- Equity contribution
Operating Assumptions:
- Revenue growth rate (annual)
- EBITDA margin trajectory
- Capex as % of revenue
- Working capital changes
- Debt paydown schedule
Exit:
- Hold period (years)
- Exit multiple (EV / EBITDA)
- Exit EBITDA (calculated from growth assumptions)
Step 2: Base Case Returns
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:
- EBITDA growth contribution
- Multiple expansion/contraction contribution
- Debt paydown contribution
- Fee/expense drag
Step 3: Sensitivity Tables
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).
Step 4: Scenario Analysis
Build 3 scenarios:
| Bull | Base | Bear | |
|---|---|---|---|
| Revenue CAGR | |||
| Exit EBITDA margin | |||
| Exit multiple | |||
| Exit EBITDA | |||
| MOIC | |||
| IRR |
Step 5: Output
- Excel workbook with:
- Assumptions tab
- Returns calculation
- Sensitivity tables (formatted with conditional coloring)
- Scenario summary
- One-page returns summary suitable for IC deck
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.
- 8d ago First seen · 120 lines · 88 tokens per session scan A 362f2a713bc5
returns-analysis is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 88 tokens to every session and 864 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to returns-analysis, differing in 0 lines, and is treated as a copy.
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