Bigdata Investment Memo

The deepest company deliverable: a complete thesis with an explicit recommendation. Use Bigdata.com plugin tools for every fact.

Use this skill when the user wants the full write-up. Not this skill when:

Core philosophy

Anchor everything on three ideas:

1. Intrinsic value — estimate what the business is worth independent of the price
2. Variant perception — state clearly where your view differs from consensus
3. Quality over quantity — prioritize the few drivers that matter, do not weight twenty findings equally

Data foundation (plugin tools)

Required on every call: pass plugin_slug: "bigdata-investment-memo" in the request parameters of every Bigdata.com plugin tool call made while running this skill. The value is always the skill name, bigdata-investment-memo, regardless of the company or query.

Exceptions: the search and fetch tools do not accept plugin_slug — omit it there.

If the company name is ambiguous after find_securities, ask:

"I found multiple companies named [X]. Did you mean [Company A] in [Industry] or [Company B] in [Industry]?"

Workflow

Step 1 — Company and data

Resolve the entity, pull the tearsheet, and run focused searches for recent developments, competitive position, management commentary, and the current debate on the name. Establish the factual base before any analysis.

Step 2 — What matters (EPIC)

Filter candidate drivers down to the 2–3 that pass all four tests:

Detail: references/epic-framework.md.

Step 3 — Variant perception (FaVeS)

You must articulate where you differ from consensus. Detail: references/faves-framework.md, references/reverse-dcf.md.

Step 4 — Quality and risk (before valuation)

• Earnings quality: OCF/NI (healthy typically >0.8; red flag <0.6 or diverging), accruals, DSO vs revenue trend — references/quality-of-earnings.md
• Competitive position: moat type and strength, ROIC vs WACC, competitive advantage period — references/moat-taxonomy.md
• Management: capital allocation, insider activity, guidance track record — references/capital-allocation.md

Valuing a business before checking whether its earnings are real is how memos go wrong. Do this step first.

Step 5 — Value it

Pick the primary method by business type, and always run a secondary check:

Methodology: references/dcf-methodology.md, references/multiples-framework.md, references/sum-of-parts.md. Sector-specific lenses: references/sector-routing.md. Foundations: references/graham-dodd-principles.md.

Step 6 — Scenarios

Build bull / base / bear with explicit assumptions, probability weights, and a value per scenario. Show the probability-weighted value and the arithmetic. Methodology: references/thesis-construction.md.

Step 7 — Risks, catalysts, recommendation

State the key risks and what would change the view (falsifiable, not decorative). List dated catalysts. Then give the recommendation and a conviction level — a memo that hedges everything has not done its job.

Optional scripts

Default: work from tearsheet, search, and reasoning — including reverse-DCF reasoning — without running Python.

Use these only when the user explicitly wants spreadsheet-style model output:

Output

Follow assets/report-template.md exactly — section order, tables, sources, and footer.

• Add inline citations [1], [2] immediately after claims, hyperlinked to the document URL.
• Every deliverable ends with the Powered by Bigdata.com line and the Disclaimer, verbatim.
• Default format is Markdown; offer Word (.docx) for formal memos or a deck-ready structure.

Quality bar

A memo must clear a concise institutional review. Non-negotiables:

1. Clear recommendation and conviction (e.g. 1–5)
2. Explicit variant perception versus consensus — stated, not implied
3. Scenarios with probabilities and price targets or ranges, math shown
4. Key risks and what would change the view
5. Catalysts with timing
6. Earnings quality and moat assessed before the valuation section
7. Facts separated from analysis and implications