{"id":25093,"plugin_id":"plugin_asdk_app_69491eceef3c8191beb70788b7840429","kind":"skill","collection_source":null,"comparison_source":null,"observed_at":"2026-09-30T23:19:12.055Z","digest":"badb22d3e5a1e4c3684eb3bd2d0c6a516a5cdfdd690d2b6a62fbdc30773457d8","against":5660,"payload":{"name":"bigdata-scenario-analysis","description":"Build bull, base, and bear cases for a public company using Bigdata.com data — with explicit line-item assumptions, justified probability weights summing to 100%, a value or price per scenario with the bridge shown, a probability-weighted expected value and expected return versus spot, the upside/downside skew and risk-reward ratio, and what would move probability between the cases. Triggers: \"scenario analysis for X\", \"bull base bear for X\", \"what's the upside and downside on X\", \"expected value for X\", \"probability-weighted view on X\", \"risk reward on X\", \"model out the cases for X\".","included_files":[{"relative_path":"README.md","size_in_bytes":1661},{"relative_path":"agents/openai.yaml","size_in_bytes":341},{"relative_path":"assets/bigdata-icon-black.svg","size_in_bytes":3278},{"relative_path":"assets/report-template.md","size_in_bytes":3771},{"relative_path":"references/dcf-methodology.md","size_in_bytes":8775},{"relative_path":"references/reverse-dcf.md","size_in_bytes":7752},{"relative_path":"references/thesis-construction.md","size_in_bytes":9072},{"relative_path":"scripts/dcf_model.py","size_in_bytes":13828},{"relative_path":"scripts/scenario_probability.py","size_in_bytes":18087}],"skill_md_contents":"---\nname: bigdata-scenario-analysis\ndescription: >\n  Build bull, base, and bear cases for a public company using Bigdata.com data — with explicit\n  line-item assumptions, justified probability weights summing to 100%, a value or price per\n  scenario with the bridge shown, a probability-weighted expected value and expected return\n  versus spot, the upside/downside skew and risk-reward ratio, and what would move probability\n  between the cases. Triggers: \"scenario analysis for X\", \"bull base bear for X\", \"what's the\n  upside and downside on X\", \"expected value for X\", \"probability-weighted view on X\",\n  \"risk reward on X\", \"model out the cases for X\".\n---\n\n# Bigdata Scenario Analysis\n\nThree cases, honest probabilities, and the arithmetic. Use Bigdata.com plugin tools for every fact.\n\n**Use this skill when** the user wants outcomes weighted, not a single point estimate. Not this skill when:\n\n| Request | Use instead |\n|---------|-------------|\n| A single valuation read | Valuation snapshot |\n| Full thesis with recommendation | Investment memo |\n| Risks rated by likelihood and impact, not valued | Risk assessment |\n| Scenarios specifically around a print | Earnings preview |\n\n## Data foundation (plugin tools)\n\n| Tool | Purpose | Prerequisite |\n|------|---------|--------------|\n| `find_securities` | Resolve company name → RavenPack `entity_id` | None |\n| `bigdata_company_tearsheet` | Financials, consensus estimates, multiples, spot price | `find_securities` |\n| `bigdata_search` | The live debate, bull and bear arguments, analyst ranges | None |\n\nIf the company name is ambiguous after `find_securities`, ask:\n\n> \"I found multiple companies named [X]. Did you mean [Company A] in [Industry] or [Company B] in [Industry]?\"\n\n## Workflow\n\n### Step 1 — Identify the company and baseline\n\nResolve the entity and pull the tearsheet: current financials, consensus estimates, current multiples, and **spot price** — every scenario is measured against it.\n\n### Step 2 — Find the swing variables\n\nScenarios are only useful if they turn on the **2–3 variables that actually decide the outcome** — not on twenty inputs nudged in the same direction. Search the live debate:\n\n- \"[Company] bull case bear case debate\"\n- \"[Company] key drivers revenue growth margin outlook\"\n- \"[Company] analyst price target range high low\"\n\nPick the swing variables and hold everything else roughly constant across cases. This is what makes the scenarios interpretable.\n\n### Step 3 — Build the three cases\n\nFor each of **bull / base / bear**, state assumptions at the line-item level:\n\n| Assumption | Bear | Base | Bull |\n|------------|------|------|------|\n| Revenue growth | | | |\n| Operating margin | | | |\n| [Swing variable 3] | | | |\n| Exit multiple or terminal assumption | | | |\n\nThe **base case should be roughly consensus** — if it isn't, say so explicitly and explain why, because that gap is itself the finding.\n\n### Step 4 — Value each scenario\n\nDerive a value or price per case and **show the bridge** — the multiple applied to which earnings, or the DCF assumptions changed. Methodology: [references/dcf-methodology.md](./references/dcf-methodology.md), [references/reverse-dcf.md](./references/reverse-dcf.md).\n\n### Step 5 — Assign and justify probabilities\n\nWeights must sum to ~100%, and each needs a **one-line justification** grounded in evidence. Guard against the usual failure: a comfortable 25/50/25 that was never really thought about. If the distribution is skewed, say so. Methodology: [references/thesis-construction.md](./references/thesis-construction.md).\n\n### Step 6 — Expected value and skew\n\n- **EV** = Σ (probability × value). Show the arithmetic.\n- **Expected return** versus spot, in %.\n- **Upside/downside ratio** = (bull − spot) / (spot − bear).\n- Note whether the distribution is symmetric or skewed, and what that means for the setup.\n\nRun [scripts/scenario_probability.py](./scripts/scenario_probability.py) or [scripts/dcf_model.py](./scripts/dcf_model.py) only when the user explicitly asks for scripted math.\n\n### Step 7 — What moves probability\n\nFor each case, name the **specific, observable** developments that would raise or lower its weight. Scenarios without triggers are static and go stale within a quarter.\n\n## Output\n\nFollow [assets/report-template.md](./assets/report-template.md) exactly — section order, tables, sources, and footer.\n\n- Add inline citations `[1]`, `[2]` immediately after claims, hyperlinked to the document URL.\n- Every deliverable ends with the **Powered by Bigdata.com** line and the **Disclaimer**, verbatim.\n- Default format is Markdown; offer a Word (.docx) version if useful.\n\n## Quality bar\n\nNon-negotiables:\n\n- 2–3 **swing variables** identified; everything else held roughly constant\n- Assumptions at line-item level per case, not narrative adjectives\n- Probabilities sum to ~100% and each is **justified**, not defaulted\n- Base case tied to consensus, or the divergence stated explicitly\n- Value bridge shown per scenario — no unexplained price targets\n- EV arithmetic written out, plus expected return versus spot and the skew\n- Probability triggers named and observable\n"},"changes":[{"path":"/included_files","type":"changed","before":[{"relative_path":"assets/bigdata-icon-black.svg","size_in_bytes":3278},{"relative_path":"assets/report-template.md","size_in_bytes":3771},{"relative_path":"references/dcf-methodology.md","size_in_bytes":8775},{"relative_path":"references/reverse-dcf.md","size_in_bytes":7752},{"relative_path":"references/thesis-construction.md","size_in_bytes":9072},{"relative_path":"scripts/dcf_model.py","size_in_bytes":13828},{"relative_path":"scripts/scenario_probability.py","size_in_bytes":18087}],"after":[{"relative_path":"README.md","size_in_bytes":1661},{"relative_path":"agents/openai.yaml","size_in_bytes":341},{"relative_path":"assets/bigdata-icon-black.svg","size_in_bytes":3278},{"relative_path":"assets/report-template.md","size_in_bytes":3771},{"relative_path":"references/dcf-methodology.md","size_in_bytes":8775},{"relative_path":"references/reverse-dcf.md","size_in_bytes":7752},{"relative_path":"references/thesis-construction.md","size_in_bytes":9072},{"relative_path":"scripts/dcf_model.py","size_in_bytes":13828},{"relative_path":"scripts/scenario_probability.py","size_in_bytes":18087}]}],"summary":"Fields changed: 1. /included_files.","summary_kind":"deterministic","summary_metadata":{}}