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- Aditya232-rtx/Ouroboros
- 최근 소스 활동
- 2026년 8월 22일 22:16
- 감지된 SKILL.md 언어
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설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/Aditya232-rtx/Ouroboros --skill lbo-model명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Add security scanning to CI/CD with Fang — GitHub Actions, GitLab CI, or any pipeline — so every pull request gets a diff-scoped AI pentest that blocks vulnerable code before it merges, with results as PR comments and SARIF uploaded to code scanning. Covers both the self-hosted open-source CLI (runs in your runner) and the managed app.fang.ai platform (GitHub/GitLab app or API, no runner infra). Use when the user asks to add security scanning, SAST/DAST, pentesting, vulnerability checks, or automated security review to their CI pipeline, pre-merge gate, or PR workflow.
Fix security vulnerabilities found by a Fang pentest (open-source CLI or app.fang.ai cloud) — triage by severity, patch the root cause rather than the symptom, and re-run Fang to prove each fix actually closes the exploit. Handles injection, XSS, SSRF, broken access control, IDOR, and other validated findings. Use after a Fang scan reports findings, or when the user asks to remediate, patch, or fix security issues from a fang_runs report, vulnerabilities.json, findings.sarif, or a cloud scan.
Run a managed pentest of a web app or API through the app.fang.ai REST API — no local Docker, LLM key, or install needed. Create an API token, register domain/repository assets, launch and poll scans, triage vulnerabilities, export SARIF, download PDF/DOCX pentest reports for SOC 2 and other compliance evidence (Enterprise plan), start PR reviews, and set up schedules and webhooks. Use when the user wants continuous or scheduled pentesting-as-a-service, an auditor-ready pentest report, scans tracked in a team dashboard, or security testing from a sandboxed agent/CI environment with no infrastructure.
SKILL.md 표시 중
| name | lbo-model |
| description | Build leveraged buyout workbooks with IRR/MOIC in Excel. |
| version | 1.0.0 |
| author | Anthropic (adapted by Nous Research) |
| license | Apache-2.0 |
| platforms | ["linux","macos","windows"] |
| metadata | {"hermes":{"tags":["finance","valuation","lbo","private-equity","excel","openpyxl","modeling"],"related_skills":["excel-author","pptx-author","dcf-model","3-statement-model"]}} |
This skill assumes headless openpyxl — you are producing an .xlsx file on disk.
Follow the excel-author skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx.
This skill uses templates for LBO models. Always check for an attached template file first.
Before starting any LBO model:
examples/LBO_Model.xlsx as your starting point and populate it with the user's assumptionsIMPORTANT: When a file like LBO_Model.xlsx is attached, you MUST use it as your template - do not build from scratch. Even if the template seems complex or has more features than needed, copy it and adapt it to the user's requirements. Never decide to "build from scratch" when a template is provided.
Use Python/openpyxl. Write formula strings (ws["D20"] = "=B5*B6"), then run the excel-author skill's recalc.py helper before delivery.
cell.value = "=B5*B6" (formula string), NOT cell.value = 1250 (computed result). The model must be dynamic and update when inputs change.examples/LBO_Model.xlsx or the user's provided template. Do not invent your own layout.=B4*B5, =SUM(), =-MAX(0,B4))=B9, =B45)=Assumptions!B5, ='Operating Model'!C10)#1F4E79 with white bold text#D9E1F2 with black bold text#F2F2F2 (or just white) — the blue font is the signal, fill is secondary#BDD7EE with black bold text$#,##0;($#,##0);"-" or $#,##0.0 depending on template0.0% (one decimal)0.0"x" (one decimal)0.00"x" (two decimals for precision)Before filling any formulas:
Before filling any formulas, examine the template thoroughly:
Map the structure - Identify where each section lives and how they relate to each other. Note which sections feed into others.
Understand the timeline - Which columns represent which periods? Is there a "Closing" or "Pro Forma" column? Where does the projection period start?
Identify input vs formula cells - Templates often use color coding, borders, or shading to indicate which cells need inputs vs formulas. Respect these conventions.
Read existing labels carefully - The row labels tell you exactly what calculation is expected. Don't assume - read what the template is asking for.
Check for existing formulas - Some templates come partially filled. Don't overwrite working formulas unless specifically asked.
Note template-specific conventions - Sign conventions, subtotal structures, how sections are organized, whether there are separate tabs for different components, etc.
For each cell that needs a formula, follow this hierarchy:
The following calculation patterns frequently cause issues across LBO models. Pay special attention when you encounter these:
[8.0x, 9.0x, 10.0x, 11.0x, 12.0x]). The center cell's IRR/MOIC MUST then equal the model's actual IRR/MOIC output — this is the proof the table is wired correctly.#BDD7EE) + bold font so the base case is visually anchored.$A5 for row input, B$4 for column input)python /path/to/excel-author/scripts/recalc.py model.xlsx
Must return success with zero errors.
[base-2Δ, base-Δ, base, base+Δ, base+2Δ])#BDD7EE, bold font)| Error | What Goes Wrong | How to Fix |
|---|---|---|
| Hardcoding calculated values | Model doesn't update when inputs change | Always use formulas that reference source cells |
| Wrong cell references after copying | Formulas point to wrong cells | Verify all links, use appropriate $ anchoring |
| Circular reference errors | Model can't calculate | Use beginning balances for interest-type calcs, break the circle |
| Sections don't balance | Totals that should match don't | Ensure one item is the plug (calculated as difference) |
| Negative balances where impossible | Paying/using more than available | Use MAX(0, ...) or MIN functions appropriately |
| IRR/return errors | Wrong signs or incomplete ranges | Check cash flow signs and ensure formula covers all periods |
| Sensitivity table shows same value | Formula not varying with inputs | Check cell references - need mixed references ($A5, B$4) |
| Roll-forwards don't tie | Beginning ≠ prior ending | Verify links between periods |
| Inconsistent sign conventions | Additions become subtractions or vice versa | Follow template's convention consistently throughout |
This skill produces investment banking-quality LBO models by filling templates with correct formulas, proper formatting, and validated calculations. The skill adapts to any template structure while ensuring financial accuracy and professional presentation standards.
Many passages below say "use the S&P Kensho MCP / Daloopa MCP / FactSet MCP". Those are commercial financial-data MCPs from the original Cowork plugin context. In Ouro:
native-mcp skill), prefer it for point-in-time comps, precedent transactions, and filings.web_search / web_extract against SEC EDGAR (https://www.sec.gov/cgi-bin/browse-edgar) for US filingsbrowser_navigate for interactive data portals[UNSOURCED] and surface it to the user.This skill is adapted from Anthropic's Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the excel-author skill's conventions. Original: https://github.com/anthropics/financial-services