funding-digest

What this skill does

**AI DISCLAIMER (MANDATORY):**
You MUST include the following disclaimer text in the powerpoint footer. This is not optional — the report is incomplete without it:

> **"Analysis is AI-generated — please confirm all outputs"**

**Footer** — At the bottom of the generated slide, as a prominent yellow banner: "Analysis is AI-generated — please confirm all outputs"

---

# Weekly Deal Flow Digest

Generate an analyst-quality **single-slide PowerPoint** that summarizes key takeaways from recent funding rounds across watched sectors or companies, using S&P Global Capital IQ data. Each deal links back to its Capital IQ profile for quick drill-down.

## When to Use

Trigger on any of these patterns:
- "Give me a deal flow digest for this week"
- "Weekly funding recap for [sector]"
- "What deals closed in [sector/companies] recently?"
- "Transaction roundup" or "deal roundup"
- "Capital markets update for my coverage universe"
- "Summarize recent funding activity"
- Any periodic briefing request about deals, raises, or rounds

## Nested Skills

This skill produces a one-slide PPTX briefing:
- **Read** `/mnt/skills/public/pptx/SKILL.md` before generating the PowerPoint (and its sub-reference `pptxgenjs.md` for creating from scratch)

## Entity Resolution & Tool Robustness

S&P Global's identifier system resolves company names to legal entities. This works well for most companies but has known failure modes that cause empty results. **Apply these rules throughout the workflow to avoid silent data loss.**

### Rule 0: Pre-validate ALL identifiers before querying funding

**Before** calling any funding tools, run every identifier through `get_info_from_identifiers`. This is the cheapest and most reliable way to catch problems early. Check two things in the response:

1. **Did it resolve at all?** If the identifier returns empty/error, the name doesn't exist in S&P Global. Try the alias from `references/sector-seeds.md`, the legal entity name, or the `company_id` directly.
2. **What is the `status` field?** 
   - `"Operating"` → Safe to query for funding rounds.
   - `"Operating Subsidiary"` → The company exists but is owned by a parent. It will return **zero funding rounds**. Note this in the digest as context (e.g., "acquired by [Parent]") but do not query for funding.
   - Any other status (e.g., closed, inactive) → The company is no longer operating. Historical data may exist but no new activity.

**This single pre-validation step prevents the majority of empty-result issues.** Batch all candidates into a single `get_info_from_identifiers` call (it handles large batches well) and triage before proceeding.

### Rule 1: Never trust empty results without a fallback

If `get_rounds_of_funding_from_identifiers` returns empty for a company you expect to have data:
1. **Try the legal entity name or company_id.** Brand names usually work, but some don't. See the alias table in `references/sector-seeds.md` for known mismatches. Common pattern: "[Brand] AI" → "[Legal Name], Inc." (e.g., Together AI → "Together Computer, Inc.", Character.ai → "Character Technologies, Inc.", Runway ML → "Runway AI, Inc.").
2. **Verify the company exists in S&P.** If you skipped Rule 0, call `get_info_from_identifiers(identifiers=["Company"])` now — if this also returns empty, the company may be too early-stage or not yet indexed.

### Rule 2: Subsidiaries have no funding rounds

Companies that are divisions or wholly-owned subsidiaries of larger companies (e.g., DeepMind under Alphabet, GitHub under Microsoft, BeReal under Voodoo) will return **zero funding rounds**. Their capital events are tracked at the parent level.

**How to detect:** The `status` field from `get_info_from_identifiers` will show `"Operating Subsidiary"`. The `references/sector-seeds.md` file also flags known subsidiaries with ⚠️ warnings. Skip these for funding queries.

### Rule 3: Use `get_rounds_of_funding_from_identifiers` as the primary tool, not `get_funding_summary_from_identifiers`

The summary tool is faster but less reliable — it can return errors or incomplete data even when detailed rounds exist. Always use the detailed rounds tool as the primary data source. The summary tool is acceptable only for quick aggregate checks (total raised, round count) and should be verified against the rounds tool if results seem low.

### Rule 4: Batch carefully and validate

When processing large company universes (50+ companies), batch in groups of 15–20. After each batch, check for companies that returned empty results and run them through the fallback steps in Rule 1 before moving on.

### Rule 5: The `role` parameter is critical

- `company_raising_funds` → "What rounds did X raise?" (company perspective)
- `company_investing_in_round_of_funding` → "What did investor Y invest in?" (investor perspective)

Using the wrong role returns empty results silently. For deal flow digests, you almost always want `company_raising_funds`. Only use the investor role when specifically analyzing an investor's portfolio activity.

### Rule 6: Identifier resolution is case-insensitive but spelling-sensitive

S&P Global handles case variations ("openai" = "OpenAI") but is strict on spelling and punctuation. "Character AI" may fail where "Character.ai" succeeds. When in doubt, use the `company_id` (e.g., `C_1829047235`) which is guaranteed to resolve.

## Workflow

### Step 1: Establish Coverage & Period

Determine what the digest should cover. There are two setups:

**Returning user (has a watchlist):**
If the user has previously defined sectors or companies to track, use that list. Check conversation history for prior watchlists.

**New user:**
Ask for:

| Parameter | Default | Notes |
|-----------|---------|-------|
| **Sectors** | *(at least one)* | e.g., "AI, Fintech, Biotech" |
| **Specific companies** | Optional | Supplement sector-level coverage |
| **Time period** | Last 7 days | "This week", "last 2 weeks", "this month" |

Calculate the exact `start_date` and `end_date` from the time period.

### Step 2: Build the Company Universe

For each sector specified, build a company universe using a validated bootstrapping approach:

1. **Seed companies** from domain knowledge (see `references/sector-seeds.md`)
   - Pay attention to the ⚠️ warnings and alias notes in the seeds file — some well-known companies are subsidiaries, have been acquired, or require a specific legal name to resolve.
   - The seeds file includes `company_id` values for known alias mismatches. Use these directly if the brand name fails.

2. **Pre-validate all seeds immediately** (Rule 0):
   ```
   get_info_from_identifiers(identifiers=[all_seeds_for_this_sector])
   ```
   Triage the results into two buckets:
   - ✅ **Resolved & Operating** (`status` = "Operating") → proceed to competitor expansion
   - ❌ **Unresolved or Subsidiary** → retry with alias/legal name from seeds file; subsidiaries are noted for context but excluded from funding queries

3. **Expand via competitors** (using only the ✅ resolved seeds):
   ```
   get_competitors_from_identifiers(identifiers=[resolved_seeds], competitor_source="all")
   ```

4. **Validate expanded universe:**
   ```
   get_info_from_identifiers(identifiers=[new_competitors])
   ```
   Apply the same triage. Filter by `simple_industry` matching the target sector. Drop any unresolved names or subsidiaries.

If the user provides specific companies, add those directly but still run them through the pre-validation triage. Never skip validation — even well-known brand names can fail silently.

Keep the universe manageable — aim for 15–40 **resolved, operating** companies per sector. For a multi-sector digest, this might total 50–100+ companies.

### Step 3: Pull Funding Rounds

For all companies in the universe:

```
get_rounds_of_funding_from_identifiers(
    identifiers=[batch],
    role="company_raising_funds",
    start_date="YYYY-MM-DD",
    end_date="YYYY-MM-DD"
)
```

Process in batches of 15–20 if the universe is lar