Portfolio Exposure Review

Use this skill after single-name evidence and thesis work when the question is about portfolio fit. It does not execute trades or prescribe an order; it frames position-level risks, constraints, and watch items for the user.

Inputs

• target thesis, valuation scenario, market signals, and risk diligence where

available

• current or proposed position size if the user provides it
• portfolio holdings, sector weights, factor exposures, cash, liquidity needs,

or risk limits when available

• benchmark, mandate, time horizon, and tax or restriction context when relevant

Workflow

1. Load the target archive and any supplied portfolio context.
2. Identify whether the task is about a new idea, existing holding, watchlist

candidate, trim/add decision, or portfolio-level risk review.

1. Map exposures by issuer, sector, industry, geography, currency, factor,

customer/supplier dependency, and catalyst overlap.

1. Review position sizing context: concentration, liquidity, drawdown

contribution, conviction, valuation range, risk severity, and time horizon.

1. Identify correlated risks and duplicate bets across holdings or watchlist

names.

1. Produce user-controlled options such as watch, research more, add to

watchlist, size smaller, rebalance candidate, or revisit after catalyst.

1. Save the review under

research/targets/<target>/artifacts/portfolio-reviews/ or a portfolio archive when one exists.

Read references/portfolio-review-framework.md before writing the review.

Output

Return:

• portfolio context used and missing context
• exposure map and concentration notes
• sizing considerations, not an autonomous trade instruction
• liquidity, correlation, drawdown, and catalyst-overlap risks
• fit with thesis, valuation, and risk evidence
• user-controlled options and watch triggers
• archive files created or updated

Quality Gate

Before finishing:

• state whether actual portfolio holdings were available or assumed absent
• do not recommend or execute trades
• distinguish target-specific risk from portfolio-level exposure
• label stale or missing prices, weights, liquidity, and benchmark data
• avoid precise sizing math without complete portfolio inputs
• preserve user control over any allocation decision