mirror of
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docs: deep audit — fix stale config keys, missing commands, and registry drift (#22784)
* docs: deep audit — fix stale config keys, missing commands, and registry drift Cross-checked ~80 high-impact docs pages (getting-started, reference, top-level user-guide, user-guide/features) against the live registries: hermes_cli/commands.py COMMAND_REGISTRY (slash commands) hermes_cli/auth.py PROVIDER_REGISTRY (providers) hermes_cli/config.py DEFAULT_CONFIG (config keys) toolsets.py TOOLSETS (toolsets) tools/registry.py get_all_tool_names() (tools) python -m hermes_cli.main <subcmd> --help (CLI args) reference/ - cli-commands.md: drop duplicate hermes fallback row + duplicate section, add stepfun/lmstudio to --provider enum, expand auth/mcp/curator subcommand lists to match --help output (status/logout/spotify, login, archive/prune/ list-archived). - slash-commands.md: add missing /sessions and /reload-skills entries + correct the cross-platform Notes line. - tools-reference.md: drop bogus '68 tools' headline, drop fictional 'browser-cdp toolset' (these tools live in 'browser' and are runtime-gated), add missing 'kanban' and 'video' toolset sections, fix MCP example to use the real mcp_<server>_<tool> prefix. - toolsets-reference.md: list browser_cdp/browser_dialog inside the 'browser' row, add missing 'kanban' and 'video' toolset rows, drop the stale '38 tools' count for hermes-cli. - profile-commands.md: add missing install/update/info subcommands, document fish completion. - environment-variables.md: dedupe GMI_API_KEY/GMI_BASE_URL rows (kept the one with the correct gmi-serving.com default). - faq.md: Anthropic/Google/OpenAI examples — direct providers exist (not just via OpenRouter), refresh the OpenAI model list. getting-started/ - installation.md: PortableGit (not MinGit) is what the Windows installer fetches; document the 32-bit MinGit fallback. - installation.md / termux.md: installer prefers .[termux-all] then falls back to .[termux]. - nix-setup.md: Python 3.12 (not 3.11), Node.js 22 (not 20); fix invalid 'nix flake update --flake' invocation. - updating.md: 'hermes backup restore --state pre-update' doesn't exist — point at the snapshot/quick-snapshot flow; correct config key 'updates.pre_update_backup' (was 'update.backup'). user-guide/ - configuration.md: api_max_retries default 3 (not 2); display.runtime_footer is the real key (not display.runtime_metadata_footer); checkpoints defaults enabled=false / max_snapshots=20 (not true / 50). - configuring-models.md: 'hermes model list' / 'hermes model set ...' don't exist — hermes model is interactive only. - tui.md: busy_indicator -> tui_status_indicator with values kaomoji|emoji|unicode|ascii (not kawaii|minimal|dots|wings|none). - security.md: SSH backend keys (TERMINAL_SSH_HOST/USER/KEY) live in .env, not config.yaml. - windows-wsl-quickstart.md: there is no 'hermes api' subcommand — the OpenAI-compatible API server runs inside hermes gateway. user-guide/features/ - computer-use.md: approvals.mode (not security.approval_level); fix broken ./browser-use.md link to ./browser.md. - fallback-providers.md: top-level fallback_providers (not model.fallback_providers); the picker is subcommand-based, not modal. - api-server.md: API_SERVER_* are env vars — write to per-profile .env, not 'hermes config set' which targets YAML. - web-search.md: drop web_crawl as a registered tool (it isn't); deep-crawl modes are exposed through web_extract. - kanban.md: failure_limit default is 2, not '~5'. - plugins.md: drop hard-coded '33 providers' count. - honcho.md: fix unclosed quote in echo HONCHO_API_KEY snippet; document that 'hermes honcho' subcommand is gated on memory.provider=honcho; reconcile subcommand list with actual --help output. - memory-providers.md: legacy 'hermes honcho setup' redirect documented. Verified via 'npm run build' — site builds cleanly; broken-link count went from 149 to 146 (no regressions, fixed a few in passing). * docs: round 2 audit fixes + regenerate skill catalogs Follow-up to the previous commit on this branch: Round 2 manual fixes: - quickstart.md: KIMI_CODING_API_KEY mentioned alongside KIMI_API_KEY; voice-mode and ACP install commands rewritten — bare 'pip install ...' doesn't work for curl-installed setups (no pip on PATH, not in repo dir); replaced with 'cd ~/.hermes/hermes-agent && uv pip install -e ".[voice]"'. ACP already ships in [all] so the curl install includes it. - cli.md / configuration.md: 'auxiliary.compression.model' shown as 'google/gemini-3-flash-preview' (the doc's own claimed default); actual default is empty (= use main model). Reworded as 'leave empty (default) or pin a cheap model'. - built-in-plugins.md: added the bundled 'kanban/dashboard' plugin row that was missing from the table. Regenerated skill catalogs: - ran website/scripts/generate-skill-docs.py to refresh all 163 per-skill pages and both reference catalogs (skills-catalog.md, optional-skills-catalog.md). This adds the entries that were genuinely missing — productivity/teams-meeting-pipeline (bundled), optional/finance/* (entire category — 7 skills: 3-statement-model, comps-analysis, dcf-model, excel-author, lbo-model, merger-model, pptx-author), creative/hyperframes, creative/kanban-video-orchestrator, devops/watchers, productivity/shop-app, research/searxng-search, apple/macos-computer-use — and rewrites every other per-skill page from the current SKILL.md. Most diffs are tiny (one line of refreshed metadata). Validation: - 'npm run build' succeeded. - Broken-link count moved 146 -> 155 — the +9 are zh-Hans translation shells that lag every newly-added skill page (pre-existing pattern). No regressions on any en/ page.
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---
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title: "Comps Analysis"
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sidebar_label: "Comps Analysis"
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description: "Build comparable company analysis in Excel — operating metrics, valuation multiples, statistical benchmarking vs peer sets"
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---
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{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}
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# Comps Analysis
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Build comparable company analysis in Excel — operating metrics, valuation multiples, statistical benchmarking vs peer sets. Pairs with excel-author. Use for public-company valuation, IPO pricing, sector benchmarking, or outlier detection.
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## Skill metadata
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| | |
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|---|---|
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| Source | Optional — install with `hermes skills install official/finance/comps-analysis` |
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| Path | `optional-skills/finance/comps-analysis` |
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| Version | `1.0.0` |
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| Author | Anthropic (adapted by Nous Research) |
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| License | Apache-2.0 |
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| Platforms | linux, macos, windows |
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| Tags | `finance`, `valuation`, `comps`, `excel`, `openpyxl`, `modeling`, `investment-banking` |
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| Related skills | [`excel-author`](/docs/user-guide/skills/optional/finance/finance-excel-author), [`pptx-author`](/docs/user-guide/skills/optional/finance/finance-pptx-author), [`dcf-model`](/docs/user-guide/skills/optional/finance/finance-dcf-model), [`lbo-model`](/docs/user-guide/skills/optional/finance/finance-lbo-model) |
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## Reference: full SKILL.md
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:::info
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The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
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:::
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## Environment
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This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
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Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
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Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.
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# Comparable Company Analysis
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## ⚠️ CRITICAL: Data Source Priority (READ FIRST)
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**ALWAYS follow this data source hierarchy:**
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1. **FIRST: Check for MCP data sources** - If S&P Kensho MCP, FactSet MCP, or Daloopa MCP are available, use them exclusively for financial and trading information
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2. **DO NOT use web search** if the above MCP data sources are available
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3. **ONLY if MCPs are unavailable:** Then use Bloomberg Terminal, SEC EDGAR filings, or other institutional sources
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4. **NEVER use web search as a primary data source** - it lacks the accuracy, audit trails, and reliability required for institutional-grade analysis
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**Why this matters:** MCP sources provide verified, institutional-grade data with proper citations. Web search results can be outdated, inaccurate, or unreliable for financial analysis.
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---
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## Overview
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This skill teaches the agent to build institutional-grade comparable company analyses that combine operating metrics, valuation multiples, and statistical benchmarking. The output is a structured Excel/spreadsheet that enables informed investment decisions through peer comparison.
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**Reference Material & Contextualization:**
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An example comparable company analysis is provided in `examples/comps_example.xlsx`. When using this or other example files in this skill directory, use them intelligently:
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**DO use examples for:**
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- Understanding structural hierarchy (how sections flow)
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- Grasping the level of rigor expected (statistical depth, documentation standards)
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- Learning principles (clear headers, transparent formulas, audit trails)
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**DO NOT use examples for:**
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- Exact reproduction of format or metrics
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- Copying layout without considering context
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- Applying the same visual style regardless of audience
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**ALWAYS ask yourself first:**
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1. **"Do you have a preferred format or should I adapt the template style?"**
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2. **"Who is the audience?"** (Investment committee, board presentation, quick reference, detailed memo)
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3. **"What's the key question?"** (Valuation, growth analysis, competitive positioning, efficiency)
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4. **"What's the context?"** (M&A evaluation, investment decision, sector benchmarking, performance review)
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**Adapt based on specifics:**
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- **Industry context**: Big tech mega-caps need different metrics than emerging SaaS startups
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- **Sector-specific needs**: Add relevant metrics early (e.g., cloud ARR, enterprise customers, developer ecosystem for tech)
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- **Company familiarity**: Well-known companies may need less background, more focus on delta analysis
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- **Decision type**: M&A requires different emphasis than ongoing portfolio monitoring
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**Core principle:** Use template principles (clear structure, statistical rigor, transparent formulas) but vary execution based on context. The goal is institutional-quality analysis, not institutional-looking templates.
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User-provided examples and explicit preferences always take precedence over defaults.
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## Core Philosophy
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**"Build the right structure first, then let the data tell the story."**
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Start with headers that force strategic thinking about what matters, input clean data, build transparent formulas, and let statistics emerge automatically. A good comp should be immediately readable by someone who didn't build it.
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---
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## ⚠️ CRITICAL: Formulas Over Hardcodes + Step-by-Step Verification
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**Formulas, not hardcodes:**
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- Every derived value (margin, multiple, statistic) MUST be an Excel formula referencing input cells — never a pre-computed number pasted in
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- When using Python/openpyxl to build the sheet: write `cell.value = "=E7/C7"` (formula string), NOT `cell.value = 0.687` (computed result)
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- The only hardcoded values should be raw input data (revenue, EBITDA, share price, etc.) — and every one of those gets a cell comment with its source
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- Why: the model must update automatically when an input changes. A hardcoded margin is a silent bug waiting to happen.
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**Verify step-by-step with the user:**
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- After setting up the structure → show the user the header layout before filling data
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- After entering raw inputs → show the user the input block and confirm sources/periods before building formulas
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- After building operating metrics formulas → show the calculated margins and sanity-check with the user before moving to valuation
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- After building valuation multiples → show the multiples and confirm they look reasonable before adding statistics
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- Do NOT build the entire sheet end-to-end and then present it — catch errors early by confirming each section
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---
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## Section 1: Document Structure & Setup
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### Header Block (Rows 1-3)
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```
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Row 1: [ANALYSIS TITLE] - COMPARABLE COMPANY ANALYSIS
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Row 2: [List of Companies with Tickers] • [Company 1 (TICK1)] • [Company 2 (TICK2)] • [Company 3 (TICK3)]
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Row 3: As of [Period] | All figures in [USD Millions/Billions] except per-share amounts and ratios
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```
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**Why this matters:** Establishes context immediately. Anyone opening this file knows what they're looking at, when it was created, and how to interpret the numbers.
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### Visual Convention Standards (OPTIONAL - User preferences and uploaded templates always override)
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**IMPORTANT: These are suggested defaults only. Always prioritize:**
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1. User's explicit formatting preferences
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2. Formatting from any uploaded template files
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3. Company/team style guides
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4. These defaults (only if no other guidance provided)
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**Suggested Font & Typography:**
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- **Font family**: Times New Roman (professional, readable, industry standard)
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- **Font size**: 11pt for data cells, 12pt for headers
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- **Bold text**: Section headers, company names, statistic labels
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**Default Color & Shading — Professional Blue/Grey Palette (minimal is better):**
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- **Keep it restrained** — only blues and greys. Do NOT introduce greens, oranges, reds, or multiple accent colors. A clean comps sheet uses 3-4 colors total.
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- **Section headers** (e.g., "OPERATING STATISTICS & FINANCIAL METRICS"):
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- Dark blue background (`#1F4E79` or `#17365D` navy)
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- White bold text
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- Full row shading across all columns
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- **Column headers** (e.g., "Company", "Revenue", "Margin"):
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- Light blue background (`#D9E1F2` or similar pale blue)
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- Black bold text
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- Centered alignment
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- **Data rows**:
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- White background for company data
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- Black text for formulas; blue text for hardcoded inputs
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- **Statistics rows** (Maximum, 75th Percentile, etc.):
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- Light grey background (`#F2F2F2`)
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- Black text, left-aligned labels
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- **That's the whole palette**: dark blue + light blue + light grey + white. Nothing else unless the user's template says otherwise.
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**Suggested Formatting Conventions:**
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- **Decimal precision**:
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- Percentages: 1 decimal (12.3%)
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- Multiples: 1 decimal (13.5x)
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- Dollar amounts: No decimals, thousands separator (69,632)
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- Margins shown as percentages: 1 decimal (68.7%)
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- **Borders**: No borders (clean, minimal appearance)
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- **Alignment**: All metrics center-aligned for clean, uniform appearance
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- **Cell dimensions**: All column widths should be uniform/even, all row heights should be consistent (creates clean, professional grid)
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**Note:** If the user provides a template file or specifies different formatting, use that instead.
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---
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## Section 2: Operating Statistics & Financial Metrics
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### Core Columns (Start with these)
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1. **Company** - Names with consistent formatting
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2. **Revenue** - Size metric (can be LTM, quarterly, or annual depending on context)
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3. **Revenue Growth** - Year-over-year percentage change
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4. **Gross Profit** - Revenue minus cost of goods sold
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5. **Gross Margin** - GP/Revenue (fundamental profitability)
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6. **EBITDA** - Earnings before interest, tax, depreciation, amortization
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7. **EBITDA Margin** - EBITDA/Revenue (operating efficiency)
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### Optional Additions (Choose based on industry/purpose)
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- **Quarterly vs LTM** - Include both if seasonality matters
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- **Free Cash Flow** - For capital-intensive or SaaS businesses
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- **FCF Margin** - FCF/Revenue (cash generation efficiency)
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- **Net Income** - For mature, profitable companies
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- **Operating Income** - For businesses with varying D&A
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- **CapEx metrics** - For asset-heavy industries
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- **Rule of 40** - Specifically for SaaS (Growth % + Margin %)
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- **FCF Conversion** - For quality of earnings analysis (advanced)
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### Formula Examples (Using Row 7 as example)
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```excel
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// Core ratios - these are always calculated
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Gross Margin (F7): =E7/C7
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EBITDA Margin (H7): =G7/C7
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// Optional ratios - include if relevant
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FCF Margin: =[FCF]/[Revenue]
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Net Margin: =[Net Income]/[Revenue]
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Rule of 40: =[Growth %]+[FCF Margin %]
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```
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**Golden Rule:** Every ratio should be [Something] / [Revenue] or [Something] / [Something from this sheet]. Keep it simple.
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### Statistics Block (After company data)
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**CRITICAL: Add statistics formulas for all comparable metrics (ratios, margins, growth rates, multiples).**
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```
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[Leave one blank row for visual separation]
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- Maximum: =MAX(B7:B9)
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- 75th Percentile: =QUARTILE(B7:B9,3)
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- Median: =MEDIAN(B7:B9)
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- 25th Percentile: =QUARTILE(B7:B9,1)
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- Minimum: =MIN(B7:B9)
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```
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**Columns that NEED statistics (comparable metrics):**
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- Revenue Growth %, Gross Margin %, EBITDA Margin %, EPS
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- EV/Revenue, EV/EBITDA, P/E, Dividend Yield %, Beta
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**Columns that DON'T need statistics (size metrics):**
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- Revenue, EBITDA, Net Income (absolute size varies by company scale)
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- Market Cap, Enterprise Value (not comparable across different-sized companies)
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**Note:** Add one blank row between company data and statistics rows for visual separation. Do NOT add a "SECTOR STATISTICS" or "VALUATION STATISTICS" header row.
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**Why quartiles matter:** They show distribution, not just average. A 75th percentile multiple tells you what "premium" companies trade at.
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---
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## Section 3: Valuation Multiples & Investment Metrics
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### Core Valuation Columns (Start with these)
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1. **Company** - Same order as operating section
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2. **Market Cap** - Current market valuation
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3. **Enterprise Value** - Market Cap ± Net Debt/Cash
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4. **EV/Revenue** - How much market pays per dollar of sales
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5. **EV/EBITDA** - How much market pays per dollar of earnings
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6. **P/E Ratio** - Price relative to net earnings
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### Optional Valuation Metrics (Choose based on context)
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- **FCF Yield** - FCF/Market Cap (for cash-focused analysis)
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- **PEG Ratio** - P/E/Growth Rate (for growth companies)
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- **Price/Book** - Market value vs. book value (for asset-heavy businesses)
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- **ROE/ROA** - Return metrics (for profitability comparison)
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- **Revenue/EBITDA CAGR** - Historical growth rates (for trend analysis)
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- **Asset Turnover** - Revenue/Assets (for operational efficiency)
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- **Debt/Equity** - Leverage (for capital structure analysis)
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**Key Principle:** Include 3-5 core multiples that matter for your industry. Don't include every possible metric just because you can.
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### Formula Examples
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```excel
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// Core multiples - always include these
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EV/Revenue: =[Enterprise Value]/[LTM Revenue]
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EV/EBITDA: =[Enterprise Value]/[LTM EBITDA]
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P/E Ratio: =[Market Cap]/[Net Income]
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// Optional multiples - include if data available
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FCF Yield: =[LTM FCF]/[Market Cap]
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PEG Ratio: =[P/E]/[Growth Rate %]
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```
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### Cross-Reference Rule
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**CRITICAL:** Valuation multiples MUST reference the operating metrics section. Never input the same raw data twice. If revenue is in C7, then EV/Revenue formula should reference C7.
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### Statistics Block
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Same structure as operating section: Max, 75th, Median, 25th, Min for every metric. Add one blank row for visual separation between company data and statistics. Do NOT add a "VALUATION STATISTICS" header row.
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---
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## Section 4: Notes & Methodology Documentation
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### Required Components
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**Data Sources & Quality:**
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- Where did the data come from? (S&P Kensho MCP, FactSet MCP, Daloopa MCP, Bloomberg, SEC filings)
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- What period does it cover? (Q4 2024, audited figures)
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- How was it verified? (Cross-checked against 10-K/10-Q)
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- Note: Prioritize MCP data sources (S&P Kensho, FactSet, Daloopa) if available for better accuracy and traceability
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**Key Definitions:**
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- EBITDA calculation method (Gross Profit + D&A, or Operating Income + D&A)
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- Free Cash Flow formula (Operating CF - CapEx)
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- Special metrics explained (Rule of 40, FCF Conversion)
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- Time period definitions (LTM, CAGR calculation periods)
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**Valuation Methodology:**
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- How was Enterprise Value calculated? (Market Cap + Net Debt)
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- What growth rates were used? (Historical CAGR, forward estimates)
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- Any adjustments made? (One-time items excluded, normalized margins)
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**Analysis Framework:**
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- What's the investment thesis? (Cloud/SaaS efficiency)
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- What metrics matter most? (Cash generation, capital efficiency)
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- How should readers interpret the statistics? (Quartiles provide context)
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---
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## Section 5: Choosing the Right Metrics (Decision Framework)
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### Start with "What question am I answering?"
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**"Which company is undervalued?"**
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→ Focus on: EV/Revenue, EV/EBITDA, P/E, Market Cap
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→ Skip: Operational details, growth metrics
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**"Which company is most efficient?"**
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→ Focus on: Gross Margin, EBITDA Margin, FCF Margin, Asset Turnover
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→ Skip: Size metrics, absolute dollar amounts
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**"Which company is growing fastest?"**
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→ Focus on: Revenue Growth %, EBITDA CAGR, User/Customer Growth
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→ Skip: Margin metrics, leverage ratios
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**"Which is the best cash generator?"**
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→ Focus on: FCF, FCF Margin, FCF Conversion, CapEx intensity
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→ Skip: EBITDA, P/E ratios
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|
||||
### Industry-Specific Metric Selection
|
||||
|
||||
**Software/SaaS:**
|
||||
Must have: Revenue Growth, Gross Margin, Rule of 40
|
||||
Optional: ARR, Net Dollar Retention, CAC Payback
|
||||
Skip: Asset Turnover, Inventory metrics
|
||||
|
||||
**Manufacturing/Industrials:**
|
||||
Must have: EBITDA Margin, Asset Turnover, CapEx/Revenue
|
||||
Optional: ROA, Inventory Turns, Backlog
|
||||
Skip: Rule of 40, SaaS metrics
|
||||
|
||||
**Financial Services:**
|
||||
Must have: ROE, ROA, Efficiency Ratio, P/E
|
||||
Optional: Net Interest Margin, Loan Loss Reserves
|
||||
Skip: Gross Margin, EBITDA (not meaningful for banks)
|
||||
|
||||
**Retail/E-commerce:**
|
||||
Must have: Revenue Growth, Gross Margin, Inventory Turnover
|
||||
Optional: Same-Store Sales, Customer Acquisition Cost
|
||||
Skip: Heavy R&D or CapEx metrics
|
||||
|
||||
### The "5-10 Rule"
|
||||
|
||||
**5 operating metrics** - Revenue, Growth, 2-3 margins/efficiency metrics
|
||||
**5 valuation metrics** - Market Cap, EV, 3 multiples
|
||||
**= 10 total columns** - Enough to tell the story, not so many you lose the thread
|
||||
|
||||
If you have more than 15 metrics, you're probably including noise. Edit ruthlessly.
|
||||
|
||||
---
|
||||
|
||||
## Section 6: Best Practices & Quality Checks
|
||||
|
||||
### Before You Start
|
||||
1. **Define the peer group** - Companies must be truly comparable (similar business model, scale, geography)
|
||||
2. **Choose the right period** - LTM smooths seasonality; quarterly shows trends
|
||||
3. **Standardize units upfront** - Millions vs. billions decision affects everything
|
||||
4. **Map data sources** - Know where each number comes from
|
||||
|
||||
### As You Build
|
||||
1. **Input all raw data first** - Complete the blue text before writing formulas
|
||||
2. **Add cell comments to ALL hard-coded inputs** - Right-click cell → Insert Comment → Document source OR assumption
|
||||
|
||||
**For sourced data, cite exactly where it came from:**
|
||||
- Example: "Bloomberg Terminal - MSFT Equity DES, accessed 2024-10-02"
|
||||
- Example: "Q4 2024 10-K filing, page 42, line item 'Total Revenue'"
|
||||
- Example: "FactSet consensus estimate as of 2024-10-02"
|
||||
- **Include hyperlinks when possible**: Right-click cell → Link → paste URL to SEC filing, data source, or report
|
||||
|
||||
**For assumptions, explain the reasoning:**
|
||||
- Example: "Assumed 15% EBITDA margin based on peer median, company does not disclose"
|
||||
- Example: "Estimated Enterprise Value as Market Cap + $50M net debt (from Q3 balance sheet, Q4 not yet available)"
|
||||
- Example: "Forward P/E based on street consensus EPS of $3.45 (average of 12 analyst estimates)"
|
||||
|
||||
**Why this matters**: Enables audit trails, data verification, assumption transparency, and future updates
|
||||
3. **Build formulas row by row** - Test each calculation before moving on
|
||||
4. **Use absolute references for headers** - $C$6 locks the header row
|
||||
5. **Format consistently** - Percentages as percentages, not decimals
|
||||
6. **Add conditional formatting** - Highlight outliers automatically
|
||||
|
||||
### Sanity Checks
|
||||
- **Margin test**: Gross margin > EBITDA margin > Net margin (always true by definition)
|
||||
- **Multiple reasonableness**:
|
||||
- EV/Revenue: typically 0.5-20x (varies widely by industry)
|
||||
- EV/EBITDA: typically 8-25x (fairly consistent across industries)
|
||||
- P/E: typically 10-50x (depends on growth rate)
|
||||
- **Growth-multiple correlation**: Higher growth usually means higher multiples
|
||||
- **Size-efficiency trade-off**: Larger companies often have better margins (scale benefits)
|
||||
|
||||
### Common Mistakes to Avoid
|
||||
❌ Mixing market cap and enterprise value in formulas
|
||||
❌ Using different time periods for numerator and denominator (LTM vs quarterly)
|
||||
❌ Hardcoding numbers into formulas instead of cell references
|
||||
❌ **Hard-coded inputs without cell comments citing the source OR explaining the assumption**
|
||||
❌ Missing hyperlinks to SEC filings or data sources when available
|
||||
❌ Including too many metrics without clear purpose
|
||||
❌ Including non-comparable companies (different business models)
|
||||
❌ Using outdated data without disclosure
|
||||
❌ Calculating averages of percentages incorrectly (should be median)
|
||||
|
||||
---
|
||||
|
||||
## Section 6: Advanced Features
|
||||
|
||||
### Dynamic Headers
|
||||
For columns showing calculations, use clear unit labels:
|
||||
```
|
||||
Revenue Growth (YoY) % | EBITDA Margin | FCF Margin | Rule of 40
|
||||
```
|
||||
|
||||
### Quartile Analysis Benefits
|
||||
Instead of just mean/median, quartiles show:
|
||||
- **75th percentile** = "Premium" companies trade here
|
||||
- **Median** = Typical market valuation
|
||||
- **25th percentile** = "Discount" territory
|
||||
|
||||
This helps answer: "Is our target company trading rich or cheap vs. peers?"
|
||||
|
||||
### Industry-Specific Modifications
|
||||
|
||||
**Software/SaaS:**
|
||||
- Add: ARR, Net Dollar Retention, CAC Payback Period
|
||||
- Emphasize: Rule of 40, FCF margins, gross margins >70%
|
||||
|
||||
**Healthcare:**
|
||||
- Add: R&D/Revenue, Pipeline value, Regulatory status
|
||||
- Emphasize: EBITDA margins, growth rates, reimbursement risk
|
||||
|
||||
**Industrials:**
|
||||
- Add: Backlog, Order book trends, Geographic mix
|
||||
- Emphasize: ROIC, asset turnover, cyclical adjustments
|
||||
|
||||
**Consumer:**
|
||||
- Add: Same-store sales, Customer acquisition cost, Brand value
|
||||
- Emphasize: Revenue growth, gross margins, inventory turns
|
||||
|
||||
---
|
||||
|
||||
## Section 7: Workflow & Practical Tips
|
||||
|
||||
### Step-by-Step Process
|
||||
1. **Set up structure** (30 minutes)
|
||||
- Create all headers
|
||||
- Format cells (blue for inputs, black for formulas)
|
||||
- Lock in units and date references
|
||||
|
||||
2. **Gather data** (60-90 minutes)
|
||||
- Pull from primary sources (S&P Kensho MCP, FactSet MCP, Daloopa MCP if available; otherwise Bloomberg, SEC)
|
||||
- Input all raw numbers in blue
|
||||
- Document sources in notes section
|
||||
|
||||
3. **Build formulas** (30 minutes)
|
||||
- Start with simple ratios (margins)
|
||||
- Progress to multiples (EV/Revenue)
|
||||
- Add cross-checks (do margins make sense?)
|
||||
|
||||
4. **Add statistics** (15 minutes)
|
||||
- Copy formula structure for all columns
|
||||
- Verify ranges are correct (B7:B9, not B7:B10)
|
||||
- Check quartile logic
|
||||
|
||||
5. **Quality control** (30 minutes)
|
||||
- Run sanity checks
|
||||
- Verify formula references
|
||||
- Check for #DIV/0! or #REF! errors
|
||||
- Compare against known benchmarks
|
||||
|
||||
6. **Documentation** (15 minutes)
|
||||
- Complete notes section
|
||||
- Add data sources
|
||||
- Define methodologies
|
||||
- Date-stamp the analysis
|
||||
|
||||
### Pro Tips
|
||||
- **Save templates**: Build once, reuse forever
|
||||
- **Color-code outliers**: Conditional formatting for values >2 standard deviations
|
||||
- **Link to source files**: Hyperlink to Bloomberg screenshots or SEC filings
|
||||
- **Version control**: Save as "Comps_v1_2024-12-15" with clear dating
|
||||
- **Collaborative reviews**: Have someone else check your formulas
|
||||
|
||||
### Excel Formatting Checklist (Optional - adapt to user preferences)
|
||||
- [ ] Font set to user's preferred style (default: Times New Roman, 11pt data, 12pt headers)
|
||||
- [ ] Section headers formatted per user's template (default: dark blue #17365D with white bold text)
|
||||
- [ ] Column headers formatted per user's template (default: light blue/gray #D9E2F3 with black bold text)
|
||||
- [ ] Statistics rows formatted per user's template (default: light gray #F2F2F2)
|
||||
- [ ] No borders applied (clean, minimal appearance)
|
||||
- [ ] **Column widths set to uniform/even width** (creates clean, professional appearance)
|
||||
- [ ] **Row heights set to consistent height** (typically 20-25pt for data rows)
|
||||
- [ ] Numbers formatted with proper decimal precision and thousands separators
|
||||
- [ ] **All metrics center-aligned** for clean, uniform appearance
|
||||
- [ ] **One blank row for separation between company data and statistics rows**
|
||||
- [ ] **No separate "SECTOR STATISTICS" or "VALUATION STATISTICS" header rows**
|
||||
- [ ] **Every hard-coded input cell has a comment with either: (1) exact data source, OR (2) assumption explanation**
|
||||
- [ ] **Hyperlinks added to cells where applicable** (SEC filings, data provider pages, reports)
|
||||
|
||||
---
|
||||
|
||||
## Section 8: Example Template Layout
|
||||
|
||||
**Simple Version (Start here):**
|
||||
<!-- ascii-guard-ignore -->
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ TECHNOLOGY - COMPARABLE COMPANY ANALYSIS │
|
||||
│ Microsoft • Alphabet • Amazon │
|
||||
│ As of Q4 2024 | All figures in USD Millions │
|
||||
├─────────────────────────────────────────────────────────────┤
|
||||
│ OPERATING METRICS │
|
||||
├──────────┬─────────┬─────────┬──────────┬──────────────────┤
|
||||
│ Company │ Revenue │ Growth │ Gross │ EBITDA │ EBITDA │
|
||||
│ │ (LTM) │ (YoY) │ Margin │ (LTM) │ Margin │
|
||||
├──────────┼─────────┼─────────┼──────────┼─────────┼────────┤
|
||||
│ MSFT │ 261,400 │ 12.3% │ 68.7% │ 205,100 │ 78.4% │
|
||||
│ GOOGL │ 349,800 │ 11.8% │ 57.9% │ 239,300 │ 68.4% │
|
||||
│ AMZN │ 638,100 │ 10.5% │ 47.3% │ 152,600 │ 23.9% │
|
||||
│ │ │ │ │ │ │ [blank row]
|
||||
│ Median │ =MEDIAN │ =MEDIAN │ =MEDIAN │ =MEDIAN │=MEDIAN │
|
||||
│ 75th % │ =QUART │ =QUART │ =QUART │ =QUART │=QUART │
|
||||
│ 25th % │ =QUART │ =QUART │ =QUART │ =QUART │=QUART │
|
||||
├─────────────────────────────────────────────────────────────┤
|
||||
│ VALUATION MULTIPLES │
|
||||
├──────────┬──────────┬──────────┬──────────┬────────────────┤
|
||||
│ Company │ Mkt Cap │ EV │ EV/Rev │ EV/EBITDA │ P/E│
|
||||
├──────────┼──────────┼──────────┼──────────┼───────────┼────┤
|
||||
│ MSFT │3,550,000 │3,530,000 │ 13.5x │ 17.2x │36.0│
|
||||
│ GOOGL │2,030,000 │1,960,000 │ 5.6x │ 8.2x │24.5│
|
||||
│ AMZN │2,226,000 │2,320,000 │ 3.6x │ 15.2x │58.3│
|
||||
│ │ │ │ │ │ │ [blank row]
|
||||
│ Median │ =MEDIAN │ =MEDIAN │ =MEDIAN │ =MEDIAN │=MED│
|
||||
│ 75th % │ =QUART │ =QUART │ =QUART │ =QUART │=QRT│
|
||||
│ 25th % │ =QUART │ =QUART │ =QUART │ =QUART │=QRT│
|
||||
└──────────┴──────────┴──────────┴──────────┴───────────┴────┘
|
||||
```
|
||||
<!-- ascii-guard-ignore-end -->
|
||||
|
||||
**Add complexity only when needed:**
|
||||
- Include quarterly AND LTM if seasonality matters
|
||||
- Add FCF metrics if cash generation is key story
|
||||
- Include industry-specific metrics (Rule of 40 for SaaS, etc.)
|
||||
- Add more statistics rows if you have >5 companies
|
||||
|
||||
---
|
||||
|
||||
## Section 9: Industry-Specific Additions (Optional)
|
||||
|
||||
Only add these if they're critical to your analysis. Most comps work fine with just core metrics.
|
||||
|
||||
**Software/SaaS:**
|
||||
Add if relevant: ARR, Net Dollar Retention, Rule of 40
|
||||
|
||||
**Financial Services:**
|
||||
Add if relevant: ROE, Net Interest Margin, Efficiency Ratio
|
||||
|
||||
**E-commerce:**
|
||||
Add if relevant: GMV, Take Rate, Active Buyers
|
||||
|
||||
**Healthcare:**
|
||||
Add if relevant: R&D/Revenue, Pipeline Value, Patent Timeline
|
||||
|
||||
**Manufacturing:**
|
||||
Add if relevant: Asset Turnover, Inventory Turns, Backlog
|
||||
|
||||
---
|
||||
|
||||
## Section 10: Red Flags & Warning Signs
|
||||
|
||||
### Data Quality Issues
|
||||
🚩 Inconsistent time periods (mixing quarterly and annual)
|
||||
🚩 Missing data without explanation
|
||||
🚩 Significant differences between data sources (>10% variance)
|
||||
|
||||
### Valuation Red Flags
|
||||
🚩 Negative EBITDA companies being valued on EBITDA multiples (use revenue multiples instead)
|
||||
🚩 P/E ratios >100x without hypergrowth story
|
||||
🚩 Margins that don't make sense for the industry
|
||||
|
||||
### Comparability Issues
|
||||
🚩 Different fiscal year ends (causes timing problems)
|
||||
🚩ixing pure-play and conglomerates
|
||||
🚩 Materially different business models labeled as "comps"
|
||||
|
||||
**When in doubt, exclude the company.** Better to have 3 perfect comps than 6 questionable ones.
|
||||
|
||||
---
|
||||
|
||||
## Section 11: Formulas Reference Guide
|
||||
|
||||
### Essential Excel Formulas
|
||||
```excel
|
||||
// Statistical Functions
|
||||
=AVERAGE(range) // Simple mean
|
||||
=MEDIAN(range) // Middle value
|
||||
=QUARTILE(range, 1) // 25th percentile
|
||||
=QUARTILE(range, 3) // 75th percentile
|
||||
=MAX(range) // Maximum value
|
||||
=MIN(range) // Minimum value
|
||||
=STDEV.P(range) // Standard deviation
|
||||
|
||||
// Financial Calculations
|
||||
=B7/C7 // Simple ratio (Margin)
|
||||
=SUM(B7:B9)/3 // Average of multiple companies
|
||||
=IF(B7>0, C7/B7, "N/A") // Conditional calculation
|
||||
=IFERROR(C7/D7, 0) // Handle divide by zero
|
||||
|
||||
// Cross-Sheet References
|
||||
='Sheet1'!B7 // Reference another sheet
|
||||
=VLOOKUP(A7, Table1, 2) // Lookup from data table
|
||||
=INDEX(MATCH()) // Advanced lookup
|
||||
|
||||
// Formatting
|
||||
=TEXT(B7, "0.0%") // Format as percentage
|
||||
=TEXT(C7, "#,##0") // Thousands separator
|
||||
```
|
||||
|
||||
### Common Ratio Formulas
|
||||
```excel
|
||||
Gross Margin = Gross Profit / Revenue
|
||||
EBITDA Margin = EBITDA / Revenue
|
||||
FCF Margin = Free Cash Flow / Revenue
|
||||
FCF Conversion = FCF / Operating Cash Flow
|
||||
ROE = Net Income / Shareholders' Equity
|
||||
ROA = Net Income / Total Assets
|
||||
Asset Turnover = Revenue / Total Assets
|
||||
Debt/Equity = Total Debt / Shareholders' Equity
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Key Principles Summary
|
||||
|
||||
1. **Structure drives insight** - Right headers force right thinking
|
||||
2. **Less is more** - 5-10 metrics that matter beat 20 that don't
|
||||
3. **Choose metrics for your question** - Valuation analysis ≠ efficiency analysis
|
||||
4. **Statistics show patterns** - Median/quartiles reveal more than average
|
||||
5. **Transparency beats complexity** - Simple formulas everyone understands
|
||||
6. **Comparability is king** - Better to exclude than force a bad comp
|
||||
7. **Document your choices** - Explain which metrics and why in notes section
|
||||
|
||||
---
|
||||
|
||||
## Output Checklist
|
||||
|
||||
Before delivering a comp analysis, verify:
|
||||
- [ ] All companies are truly comparable
|
||||
- [ ] Data is from consistent time periods
|
||||
- [ ] Units are clearly labeled (millions/billions)
|
||||
- [ ] Formulas reference cells, not hardcoded values
|
||||
- [ ] **All hard-coded input cells have comments with either: (1) exact data source with citation, OR (2) clear assumption with explanation**
|
||||
- [ ] **Hyperlinks added where relevant** (SEC EDGAR filings, Bloomberg pages, research reports)
|
||||
- [ ] Statistics include at least 5 metrics (Max, 75th, Med, 25th, Min)
|
||||
- [ ] Notes section documents sources and methodology
|
||||
- [ ] Visual formatting follows conventions (blue = input, black = formula)
|
||||
- [ ] Sanity checks pass (margins logical, multiples reasonable)
|
||||
- [ ] Date stamp is current ("As of [Date]")
|
||||
- [ ] Formula auditing shows no errors (#DIV/0!, #REF!, #N/A)
|
||||
|
||||
---
|
||||
|
||||
## Continuous Improvement
|
||||
|
||||
After completing a comp analysis, ask:
|
||||
1. Did the statistics reveal unexpected insights?
|
||||
2. Were there any data gaps that limited analysis?
|
||||
3. Did stakeholders ask for metrics you didn't include?
|
||||
4. How long did it take vs. how long should it take?
|
||||
5. What would make this more useful next time?
|
||||
|
||||
The best comp analyses evolve with each iteration. Save templates, learn from feedback, and refine the structure based on what decision-makers actually use.
|
||||
|
||||
|
||||
## Data sources — MCP first, web fallback
|
||||
|
||||
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 Hermes:
|
||||
|
||||
- **If you have any structured financial-data MCP configured** (Hermes supports MCP — see `native-mcp` skill), prefer it for point-in-time comps, precedent transactions, and filings.
|
||||
- **Otherwise**, fall back to:
|
||||
- `web_search` / `web_extract` against SEC EDGAR (`https://www.sec.gov/cgi-bin/browse-edgar`) for US filings
|
||||
- Company IR pages for press releases, earnings decks
|
||||
- `browser_navigate` for interactive data portals
|
||||
- User-provided data (explicitly ask when the context doesn't have it)
|
||||
- **Never fabricate**. If a multiple, precedent, or filing number can't be sourced, flag the cell as `[UNSOURCED]` and surface it to the user.
|
||||
|
||||
## Attribution
|
||||
|
||||
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
|
||||
Loading…
Add table
Add a link
Reference in a new issue