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/stock-analysis/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/stock-analysis)<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/stock-analysis"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/stock-analysis/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/stock-analysis"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/stock-analysis.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 | $0.00067 | $0.03045 |
| Opus 5 | $0.00034 | $0.01522 |
| Sonnet 5 | $0.00013 | $0.00609 |
| Haiku 4.5 | $0.00007 | $0.00304 |
Grade A, and why
stock-analysis 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 9d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stock Analysis (Goldman Sachs + Citadel)
Data grounding (REQUIRED - anti-hallucination contract)
Every numeric claim (price, P/E, RSI, target, FCF, weight) MUST come from a tool call in THIS run. After fetching, restate the verified figures in a short "Verified data" block and cite ONLY those numbers downstream. If a figure is not in any tool response, say "not available" - never estimate, recall, or invent it. WebSearch is allowed only for narrative (earnings quotes, upgrades), not for numbers that a tool already provides.
Stage 0 - Decision Memory (load BEFORE forming any verdict)
Before fetching market data, load prior decisions on this ticker so the verdict stays consistent across sessions:
mcp__aifolimizer__get_ticker_decision_historywithticker=TICKER, max_decisions=5- prior actions, outcomes, reflectionsmcp__aifolimizer__get_ticker_reflectionwithsymbol=TICKER, n=3- prior recs + realized alphamcp__aifolimizer__get_cross_ticker_lessonswithmax_lessons=3- portfolio-level win/loss patterns
Reconciliation rule: if a prior decision exists and your new read flips it, state explicitly WHY it changed (new data / catalyst / price move). Never silently contradict a logged decision - that drift is exactly what this prevents.
How to run
- Call
mcp__aifolimizer__get_profile- account types, cash balances, total capital. Frame tax placement recommendation at end - Identify ticker user is asking about (or use largest position if unspecified)
- Call
mcp__aifolimizer__get_portfolio- confirm ticker is in portfolio + get cost basis and current weight - Call
mcp__aifolimizer__get_fundamentalswithsymbols=[ticker]- P/E, EPS, dividend yield, market cap, analyst target, institutional ownership, beta - Call
mcp__aifolimizer__get_technicalswithsymbols=[ticker]- SMA20/50/200, RSI, MACD, Bollinger Bands, trend signal - Call
mcp__aifolimizer__get_news_headlineswithticker=ticker- recent news - Call
mcp__aifolimizer__get_positioning_signalswithsymbols=[ticker]- crowding score, institutional ownership, short interest, headline velocity. Flag "edge already priced" before issuing buy - Call
mcp__aifolimizer__get_insider_sentimentwithticker=ticker- insider MSPR (net buying-pressure) trend; feeds fundamental item 6 (insider trend). US-listed only - Call
mcp__aifolimizer__get_finnhub_newswithticker=ticker- news bull/bear tally + net_sentiment; cross-check theget_news_headlinesnarrative for divergence - Call
mcp__aifolimizer__get_recent_filingswithticker=ticker- recent material SEC filings; flag any 8-K filed in the last 5 days as event risk before issuing a call. US-listed only - Call
mcp__aifolimizer__get_factor_exposurewithticker=ticker- dominant style factor (value/momentum/quality/size); use to pick which INVESTOR LENS applies 11b. (US tickers only, when Buffett lens applies) Callmcp__aifolimizer__get_dcf_valuationwithsymbol=tickerfor the FCF history (owner-earnings anchor) andmcp__aifolimizer__get_sec_financialswithsymbols=[ticker]for the 3-4yr revenue/income/EPS trend (capital-allocation read). Skip for .TO names - EDGAR has no Canadian filings - Use MCP data as primary source. WebSearch only for: recent earnings call quotes, analyst upgrade/downgrade news, or gaps in MCP response
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.
- 9d ago First seen · 129 lines · 67 tokens per session scan A 7d447f0746f1
stock-analysis is a skill published in the GitHub repository tusharagg1/aifolimizer (2 stars, last pushed 7d ago), licensed MIT. It adds 67 tokens to every session and 3,045 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.
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.