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/auto-rebalance/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/auto-rebalance)<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/auto-rebalance"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/auto-rebalance/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/auto-rebalance"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/auto-rebalance.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 | $0.00117 | $0.02686 |
| Opus 5 | $0.00059 | $0.01343 |
| Sonnet 5 | $0.00023 | $0.00537 |
| Haiku 4.5 | $0.00012 | $0.00269 |
Grade A, and why
auto-rebalance 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 10d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto-Rebalance (Long-Term Core Maintenance)
Goal
Keep the boring-core sleeve on target with minimum effort. This skill is for the wealth-building bucket, not the trading bucket. Output is a monthly DCA + rebalance instruction sheet the user can execute in 5 minutes.
Math behind it: rebalancing by adding new cash to underweighted positions (vs selling overweighted) avoids tax events and keeps drift small. Combined with biweekly/monthly DCA, this captures dollar-cost averaging benefits and removes timing decisions.
When to invoke
- 1st of each month (can be scheduled via /loop)
- User asks "where do I put this paycheck?"
- User asks "is my allocation off?"
- Settled cash in TFSA/RRSP > $500 with no immediate trade plan
- After any contribution-room reset (TFSA Jan 1, RRSP March 1)
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="auto-rebalance", ticker, action, conviction, rationale, target_pct, stop_pct). Skipping breaks the cross-session feedback loop and causes drift.
How to run
Step 1 - Pull state (parallel):
mcp__aifolimizer__get_profile- per-account cash, contribution room if available, total NAVmcp__aifolimizer__get_portfolio- current holdings per accountmcp__aifolimizer__get_xray- ETF exposure expansion (so VFV+XEQT overlap is detected)mcp__aifolimizer__get_concentration_warnings- single-name or sector flags from xraymcp__aifolimizer__get_macro_snapshot- current regime label (informational only; this skill does NOT time the market)mcp__aifolimizer__recall_preferenceswithquery="rebalance core allocation"- user's preferred target weights if previously setmcp__aifolimizer__get_personal_context- ground TFSA/RRSP/Non-Reg routing in the actual account waterfall, contribution room, and horizon. Ifpresent=false, fall back to generic routing rules and suggest the user run profile-setup for personalized tax-account placement.
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.
- 10d ago First seen · 155 lines · 117 tokens per session scan A aea3f055c87c
auto-rebalance is a skill published in the GitHub repository tusharagg1/aifolimizer (2 stars, last pushed 9d ago), licensed MIT. It adds 117 tokens to every session and 2,686 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.
Other skills, from other repositories
quantoracle
63 deterministic quantitative finance calculators + 10 composite workflows via MCP. Options pricing, Greeks, exotic derivatives, risk metrics, portfolio optimization, Monte Carlo, statistics, crypto/DeFi, FX/macro, TVM, strategy backtesting, rebalance planning, options strategy selection, hedging. 1,000 free…
gitee-expert
You have access to Gitee (gitee.com), China's largest GitHub-style platform. This skill teaches the effective workflows for discovery, intel, translation and webhook monitoring.
transcribe-filing
Transcribe an insurance rate filing or internal rating manual into an OpenRater workbook, then validate, build, verify, and rate it through the OpenRater MCP tools. Use when the user shares a filing/manual (PDF or pages) and wants it executable — "build this filing", "digitize this rating manual", "make this rateable"…
tushare
A Python interface for Tushare, a financial data service that provides market and company information for stocks, funds, futures, and digital assets. It returns queried data as pandas tables.
social-media-intelligence
Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.
geopolitical-risk
Geopolitical risk analysis: quantify crisis signals, identify precursors, and build event-driven strategies for war, sanctions, and supply disruption scenarios.