Borrowing it
Nothing to install: this file belongs to tusharagg1/aifolimizer. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tusharagg1/aifolimizer/master/.claude/skills/portfolio-review/SKILL.mdgit clone --depth 1 https://github.com/tusharagg1/aifolimizerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tusharagg1/aifolimizer/portfolio-review)<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/portfolio-review"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/portfolio-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/portfolio-review"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/portfolio-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00113 | $0.01605 |
| Opus 5 | $0.00056 | $0.00803 |
| Sonnet 5 | $0.00023 | $0.00321 |
| Haiku 4.5 | $0.00011 | $0.00161 |
Grade A, and why
portfolio-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Portfolio Review (whole-book decision table)
Goal
One coherent, execution-ready plan across EVERY holding and watchlist name - optimized for long-term after-tax wealth. The output is a single decision table, not nine separate analyses. Periodic cadence (monthly/quarterly), not daily. Bias toward FEW high-value actions: most holdings should end "Hold".
When to invoke
- User wants the whole book reviewed at once, not one ticker
- A monthly/quarterly check-in across holdings + watchlist
- NOT for a single-name buy decision (that's
trading-desk) or a morning digest (that'sdaily-briefing)
Decision Memory Protocol (load first, log after)
Before forming any view, load prior decisions so verdicts stay consistent across sessions:
mcp__aifolimizer__get_cross_ticker_lessons(max_lessons=3) - portfolio-level win/loss patterns- For any name you issue a per-ticker BUY/SELL/TRIM/HOLD/ADD on, also load
mcp__aifolimizer__get_ticker_decision_history(ticker=…, max_decisions=5) andmcp__aifolimizer__get_ticker_reflection(symbol=…, n=3). If a prior decision exists and this run flips it, state explicitly WHY (new data / catalyst / price); never silently contradict a logged decision.
After output, log every actionable verdict: for each BUY/SELL/TRIM/ADD/HOLD issued, call mcp__aifolimizer__log_recommendation (skill="portfolio-review", ticker, action, conviction, rationale, target_pct, stop_pct). Skipping breaks the cross-session feedback loop and causes drift.
How to run (call in parallel where independent)
Stage 0 - context:
get_profile(capital, accounts - never hardcode)get_personal_context(tax bracket, FHSA/TFSA/RRSP room, account waterfall)
Stage 1 - portfolio truth:
get_portfolio,get_concentration_warnings,get_xray
Stage 2 - per-name signal (top holdings + watchlist):
get_fundamentals(holdings + watchlist union)get_positioning_signals(crowding - gates ADD decisions)get_tax_loss_candidates(underwater names = harvest candidates)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 126 lines · 113 tokens per session scan A 031d7233827e
portfolio-review is a skill published in the GitHub repository tusharagg1/aifolimizer (2 stars, last pushed 9d ago), licensed MIT. It adds 113 tokens to every session and 1,605 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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