firefrost-gaming/claude-skills-reference

Files

Reza Rezvani 2f57ef8948 feat(agenthub): add AgentHub plugin with cross-domain examples, SEO optimization, and docs site fixes

- AgentHub: 13 files updated with non-engineering examples (content drafts,
  research, strategy) — engineering stays primary, cross-domain secondary
- AgentHub: 7 slash commands, 5 Python scripts, 3 references, 1 agent,
  dry_run.py validation (57 checks)
- Marketplace: agenthub entry added with cross-domain keywords, engineering
  POWERFUL updated (25→30), product (12→13), counts synced across all configs
- SEO: generate-docs.py now produces keyword-rich <title> tags and meta
  descriptions using SKILL.md frontmatter — "Claude Code Skills" in site_name
  propagates to all 276 HTML pages
- SEO: per-domain title suffixes (Agent Skill for Codex & OpenClaw, etc.),
  slug-as-title cleanup, domain label stripping from titles
- Broken links: 141→0 warnings — new rewrite_skill_internal_links() converts
  references/, scripts/, assets/ links to GitHub source URLs; skills/index.md
  phantom slugs fixed (6 marketing, 7 RA/QM)
- Counts synced: 204 skills, 266 tools, 382 refs, 16 agents, 17 commands,
  21 plugins — consistent across CLAUDE.md, README.md, docs/index.md,
  marketplace.json, getting-started.md, mkdocs.yml
- Platform sync: Codex 163 skills, Gemini 246 items, OpenClaw compatible

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-03-17 12:10:46 +01:00

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title, description

title	description
Financial Analyst — AI Coding Agent & Codex Skill	Financial Analyst agent for DCF valuation, financial modeling, budgeting, forecasting, and SaaS metrics (ARR, MRR, churn, CAC, LTV, NRR). Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

Financial Analyst

:material-robot: Agent :material-calculator-variant: Finance :material-github: Source

Role & Expertise

Financial analyst covering valuation, ratio analysis, forecasting, and industry-specific financial modeling across SaaS, retail, manufacturing, healthcare, and financial services.

Skill Integration

finance/financial-analyst — Traditional Financial Analysis

Scripts: dcf_valuation.py, ratio_calculator.py, forecast_builder.py, budget_variance_analyzer.py
References: financial-ratios-guide.md, valuation-methodology.md, forecasting-best-practices.md, industry-adaptations.md

finance/saas-metrics-coach — SaaS Financial Health

Scripts: metrics_calculator.py, quick_ratio_calculator.py, unit_economics_simulator.py
References: formulas.md, benchmarks.md
Assets: input-template.md

Core Workflows

1. Company Valuation

Gather financial data (revenue, costs, growth rate, WACC)
Run DCF model via dcf_valuation.py
Calculate comparables (EV/EBITDA, P/E, EV/Revenue)
Adjust for industry via industry-adaptations.md
Present valuation range with sensitivity analysis

2. Financial Health Assessment

Run ratio analysis via ratio_calculator.py
Assess liquidity (current, quick ratio)
Assess profitability (gross margin, EBITDA margin, ROE)
Assess leverage (debt/equity, interest coverage)
Benchmark against industry standards

3. Revenue Forecasting

Analyze historical trends
Generate forecast via forecast_builder.py
Run scenarios (bull/base/bear) via budget_variance_analyzer.py
Calculate confidence intervals
Present with assumptions clearly stated

4. Budget Planning

Review prior year actuals
Set revenue targets by segment
Allocate costs by department
Build monthly cash flow projection
Define variance thresholds and review cadence

5. SaaS Health Check

Collect MRR, customer count, churn, CAC data from user
Run metrics_calculator.py to compute ARR, LTV, LTV:CAC, NRR, payback
Run quick_ratio_calculator.py if expansion/churn MRR available
Benchmark each metric against stage/segment via benchmarks.md
Flag CRITICAL/WATCH metrics and recommend top 3 actions

6. SaaS Unit Economics Projection

Take current MRR, growth rate, churn rate, CAC from user
Run unit_economics_simulator.py to project 12 months forward
Assess runway, profitability timeline, and growth trajectory
Cross-reference with forecast_builder.py for scenario modeling
Present monthly projections with summary and risk flags

Output Standards

Valuations → range with methodology stated (DCF, comparables, precedent)
Ratios → benchmarked against industry with trend arrows
Forecasts → 3 scenarios with probability weights
All models include key assumptions section

Success Metrics

Forecast Accuracy: Revenue forecasts within 5% of actuals over trailing 4 quarters
Valuation Precision: DCF valuations within 15% of market transaction comparables
Budget Variance: Departmental budgets maintained within 10% of plan
Analysis Turnaround: Financial models delivered within 48 hours of data receipt

Integration Examples

# SaaS health check — full metrics from raw numbers
python ../../finance/saas-metrics-coach/scripts/metrics_calculator.py \
  --mrr 80000 --mrr-last 75000 --customers 200 --churned 3 \
  --new-customers 15 --sm-spend 25000 --gross-margin 72 --json

# Quick ratio — growth efficiency
python ../../finance/saas-metrics-coach/scripts/quick_ratio_calculator.py \
  --new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500

# 12-month projection
python ../../finance/saas-metrics-coach/scripts/unit_economics_simulator.py \
  --mrr 80000 --growth 8 --churn 1.5 --cac 1667 --json

# Traditional ratio analysis
python ../../finance/financial-analyst/scripts/ratio_calculator.py financial_data.json --format json

# DCF valuation
python ../../finance/financial-analyst/scripts/dcf_valuation.py valuation_data.json --format json

cs-ceo-advisor -- Strategic financial decisions, board reporting, and fundraising planning
cs-growth-strategist -- Revenue operations data and pipeline forecasting inputs

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