Draft Investment Memos

Draft investment memos — skill template

Turn this template into a working skill by resolving every TODO. The seed prompt below is the published Anthropic example this template was derived from; your skill should generalize it for repeated, hands-off use.

Outcome

Generate investment memos from platform data, formatted to match your firm's structure and requirements.

TODO: Restate the outcome for YOUR context in one sentence: who runs this, on what input, producing what artifact, how often.

Seed prompt (verbatim from source, © Anthropic PBC)

I'm evaluating CloudBridge Technologies (ticker: CLDG) for a potential equity investment and need an initial memo for our IC meeting next week.

Pull the data I need: Using Daloopa, get CloudBridge's revenue, operating margins, and free cash flow for the last 12 quarters, plus their segment revenue breakdowns. Using Kensho, identify who CloudBridge lists as competitors in their SEC filings and pull revenue growth and margins for those competitors. Also get CloudBridge's key business relationships and major customers.

Analyze this: Calculate cloud platform segment growth versus overall company growth. Determine free cash flow conversion rate. Compare margins year-over-year. Benchmark CloudBridge against the competitors we identified. Flag customer concentration risks.

Create a professional investment memo in Word format: executive summary with recommendation, business overview with segment analysis, financial performance highlighting trends, competitive positioning, valuation assessment, and key risks. Use IC-ready formatting.

Required context and inputs

• Connectors to financial data platforms: Daloopa and S&P Global (Kensho referenced in prompt)
• Existing subscriptions/licenses with underlying providers
• Claude for Enterprise access
• Optional: Web Search enabled in chat settings
• Optional: Extended Thinking enabled
• TODO: exact file paths / folders / message formats this skill should expect
• TODO: domain context the model cannot infer (naming conventions, thresholds, house style)

Tools, connectors, APIs, and authentication

• Daloopa
• S&P Global
• TODO: confirm which connectors are enabled in the runtime that will execute this skill
• Authentication: connectors authenticate via their own OAuth flows — this skill must

NEVER ask for, store, or echo credentials, tokens, or API keys. If auth is missing, stop and tell the user which connector to enable.

Permissions and sensitive actions

• Reads: TODO (folders, channels, records this skill may read)
• Writes: TODO (what it may create/modify, and where)
• Held back for the primary agent / human: sending external communications, financial

transactions, deleting or overwriting originals, submitting web forms

Workflow

1. Describe the task: specify the company being evaluated, key metrics needed, and deliverable format required
2. Give Claude context: connect data platforms (Daloopa, S&P Global) via connectors; enable Web Search and Extended Thinking
3. What Claude creates: pulls data, performs calculations, and generates a professional Word document with full analysis
4. Follow up prompts: cite exact sources for each metric; show the DCF math step-by-step with a sensitivity table; convert the analysis into a 6-slide PowerPoint
5. Tricks, tips, and troubleshooting: clear instructions, downloading actual files, model selection, and accessing specialized capabilities

TODO: adapt the steps above (from the source page) into imperative instructions for the executing agent, including what to do between steps.

Decision points

• TODO: list each point where the skill must choose between paths, with the rule to apply
• Default rule: prefer the reversible option; when two readings of the input are

plausible, surface both rather than picking silently.

Validation criteria

• Output matches the outcome statement above (spot-check against the seed prompt's asks)
• Every factual claim is traceable to a provided input, connector record, or cited source
• All figures reconcile to source statements/workbooks; totals recomputed programmatically, not by eye
• Flag (never silently correct) discrepancies between model and source data
• TODO: add one domain-specific check a reviewer in your org would apply

Failure modes and fallbacks

• Connector unavailable or unauthenticated → pause, tell the user exactly which connector to enable and why; offer a degraded run from uploaded files
• Search returns thin/conflicting results → present both readings with sources instead of picking one silently
• Ambiguous or missing input → ask one targeted question; if unattended, state the assumption inline and proceed
• Any step would take a sensitive/irreversible action → stop and hand back to the user (see Delegation)

Delegation

Apply the repo's delegation policy (docs/delegation-policy.md — bundle or restate it if you install this skill outside the repo). Defaults for this template:

• Run single-agent unless a step fans out over independent items (files, records,

vendors, channels). Only independent work parallelizes.

• Each delegated task must ship with: the minimal context slice it needs, an explicit

output contract, a validation check the primary agent runs on the result, and a fallback if it returns empty or fails.

• Final review, synthesis, and every sensitive action listed above stay with the

primary agent.

• TODO: name the concrete subtasks (if any) that qualify for delegation here.

Attribution

Derived from Draft investment memos (retrieved 2026-07-26). Seed prompt and workflow content © Anthropic PBC. Scaffold structure original to this repository.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: iankiku
• Source: iankiku/agent-skill-factory
• License: MIT

Install and usage instructions live in the source repository linked above.