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/risk-assessment/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/risk-assessment)<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/risk-assessment"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/risk-assessment/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/risk-assessment"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/risk-assessment.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 | $0.00056 | $0.01186 |
| Opus 5 | $0.00028 | $0.00593 |
| Sonnet 5 | $0.00011 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00119 |
Grade A, and why
risk-assessment 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 12d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Risk Assessment (Bridgewater style)
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="risk-assessment", ticker, action, conviction, rationale, target_pct, stop_pct). Skipping breaks the cross-session feedback loop and causes drift.
How to run
- Call
mcp__aifolimizer__get_profile- account context - Call
mcp__aifolimizer__get_personal_context- province, marginal tax rate, risk tolerance, time horizon, account waterfall. Use the live risk tolerance/horizon instead of the generic buckets below, and ground §11 tax-aware rebalancing. Ifpresent=false, note the risk framing is generic and suggest profile-setup - Call
mcp__aifolimizer__get_portfolio- current holdings - Call
mcp__aifolimizer__get_risk_metrics- vol, Sharpe, Sortino, VaR, expected shortfall - Call
mcp__aifolimizer__get_correlation_matrix- which positions move together - Call
mcp__aifolimizer__get_concentration_warnings- over-allocation flags - Call
mcp__aifolimizer__get_factor_exposurefor the top 3-5 holdings by weight - multi-factor betas (market/size/value/profitability/investment/momentum) + annualized alpha. Surfaces hidden FACTOR concentration that name/sector diversification hides (e.g. 4 different names all loaded on momentum = one factor bet) - Call
mcp__aifolimizer__get_factor_snapshot- current factor regime. A book heavy on a factor that is rolling over is a live risk even if the names look uncorrelated - Use Ray Dalio's all-weather/radical-transparency framework
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.
- 12d ago First seen · 63 lines · 56 tokens per session scan A 287a0cf56045
risk-assessment is a skill published in the GitHub repository tusharagg1/aifolimizer (1 stars, last pushed 10d ago), licensed MIT. It adds 56 tokens to every session and 1,186 once invoked, about $0.0003 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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