Generate portfolio rebalancing candidates for licensed advisor review.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "rebalance-recommendation" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/aibast-agents-library/main/solutions/portfolio-rebalancing/manual/skills/aibast_rebalance-recommendation_02/SKILL.md 2. Save it as ~/.claude/skills/aibast_rebalance-recommendation_02/SKILL.md 3. Reload skills and tell me it's ready
Using the synthetic operating snapshot, list allocation-change candidates for this fictional client record that an advisor should review. Separate observed evidence, calculated or heuristic output, and proposed next steps, and state that this is not investment advice and that no trade or account change occurred.
A source-backed list of rebalancing candidates with clear evidence, proposed changes, disclaimers, and required human review.
Answer only for this specified fictional report scope: what allocation changes should be discussed with the client before any trading? Do not substitute another record, and name the authorized human review required.
A candidate-adjustment response scoped to the specified record, without using other records, plus a note that licensed advisor review is required.
Prepare nonbinding allocation-change candidates for licensed advisor review. You must state that the result is not legal, tax, investment, or financial advice, and that no approval, communication, order, payment, or other external action occurred.
Candidate adjustment output with full limitations and disclaimers, suitable for human review rather than direct execution.
Before discussing changes with a client, a team can prepare allocation-change candidates worth reviewing. The skill provides nonbinding results from a synthetic snapshot and requires licensed advisor review.
When a user wants rebalancing candidates only within a specific fictional record or report scope, this skill emphasizes exact scoping and avoids substituting another record. It also separates observed evidence, calculated output, and next steps.
In the Portfolio Rebalancing Agent synthetic pilot, it can answer questions about rebalancing candidates. The result includes disclaimers and explicitly states that no trade, account change, or other external action occurred.
The README describes a skill that prepares nonbinding portfolio rebalancing candidates for licensed-advisor review in a synthetic pilot. It emphasizes using the exact fictional record or report scope, grounding responses in a synthetic operating snapshot, and separating observed evidence from calculated or heuristic output and proposed next steps. It also requires clear disclaimers that the result is not professional advice and that no transaction, account change, or external action has occurred.
Prepares nonbinding allocation-change candidates for licensed-advisor review.
Persona: Financial Advisor
Prompt: Show me the allocation changes I should review with the client before anyone trades.
Expected synthetic evidence: VTI, candidate.
It prepares nonbinding allocation-change candidates for portfolio rebalancing, to be reviewed by a licensed advisor. The original description indicates it is used for questions in the Portfolio Rebalancing Agent synthetic pilot.
No. The document excerpt explicitly requires stating that no approval, communication, filing, account change, payment, order, transaction, or other external action occurred.
The output should separate observed evidence, calculated or heuristic results, and proposed next steps, and state that it is not legal, regulatory, insurance, lending, tax, investment, or financial advice. It should also name the authorized human review required before action.
Helps assess request context and approval level from synthetic procurement records.
Generate sentiment splits and recent excerpts from synthetic records.
Extract a source-coded problem list from synthetic clinical evidence deterministically.
Frames retirement scenarios with conservative, base, and higher-volatility assumptions.
Extract and summarize medication inventories from packaged synthetic clinical evidence.
Prioritize churn reviews with transparent evidence and suggested next steps.
Identifies tax-loss-harvesting candidates and required review controls.
Generates illustrative tax impact estimates for professional review.
Generates controlled implementation checklists for human review.
Analyze portfolio drift and identify allocation threshold breaches.
Run multi-agent debates and produce consensus recommendations with ranked options.
Draft prioritized cross-sell recommendations and a reviewable engagement plan from synthetic data.