Portfolio review

portfolio-review

You hand over positions + book value. The skill runs the full 7-tool review chain in the right order, threads the shared context (tickers, weights, lookback windows), skips ETFs on the equities-only tools (earnings, 8-K), and stitches the outputs into a single briefing.

The headline block is the read-first summary: regime, rotation theme, next earnings, next macro event, top corporate action, and the rebalance verdict in six lines. Full per-section detail follows.

When to invoke

• The operator says "review my portfolio", "run the full workflow", "portfolio check", "what should I do with my book"
• Weekly / monthly portfolio hygiene
• After a material market move to sanity-check the book
• Before a large trade to see whether the new sizing survives the variance-share cap

What you need

• MASSIVE_API_KEY (Stocks Starter minimum). One skill in the chain (corporate-actions-scanner) also hits SEC EDGAR, which is free.
• Positions in the shape TICKER=WEIGHT,TICKER=WEIGHT,... where weights sum to ~1.0.
• Book value in dollars for the rebalancer's trade-ticket sizing.

What you get back

Layer 1 canonical JSON with a sections map keyed by the sub- skill name, each holding the full sub-payload. The headline block distills one fact from each. errors array captures per-section failures without aborting the run.

Layer 2 rendered briefing. Header, headline block (7 lines), then each section rendered by its own render() helper under a titled divider. See references/rendering.md.

How it works

1. Parse positions, split into equities vs ETFs (ETFs skipped for earnings + 8-K scanners).
2. Run the 7 sub-skills in sequence, sharing a single MassiveClient for connection reuse:
   1. market-regime
   2. sector-rotation-signal
   3. risk-report
   4. earnings-blackout (equities only)
   5. macro-event-calendar
   6. corporate-actions-scanner (equities only)
   7. portfolio-rebalancer (skippable via include_rebalance=False)
3. Build the headline by pulling the top-fact from each sub-payload.
4. Emit the composite payload with per-section detail and errors.

Foundations used

• All 7 sub-skills. This is a pure composition — no new data pulls beyond what the sub-skills already fetch.

Endpoints used

• Aggregate of every sub-skill's endpoints. Chain sharing a MassiveClient means daily-aggs fetches are cached per ticker across sub-skills where the cache applies.

Doesn't handle (yet)

• Fixed income context. No rates/credit read yet (waiting on fixed-income-context, Part 3 #4).
• Historical analogs. Not included in the default chain because regime-conditional forecasting is thesis-driven, not portfolio review. Callers who want it can invoke historical-analog-finder directly.
• Options context. Neither options-flow nor options-structure- analyzer are part of the default chain; they're view-dependent, not review-dependent.
• No changed-since-last-run diff. Every review is stateless. A future version could take a prior review payload and highlight what moved.