MAIA Investment Analysis (Tododeia)
Five AI agents research crypto, stocks, forex and commodities in parallel, adapt to your risk profile, and render a bilingual interactive market report — educational analysis, not financial advice.
Positives: strict read-only SKILL_DIR vs writable cache separation, mandatory educational disclaimer, XSS-hardened HTML fallback with JSON escaping, local-only runtime, standard optional env-var API keys. Deducted for: README promotes curl|bash one-line install (unreviewed supply-chain risk), unauthenticated dashboard, heavy reliance on runtime third-party web fetches with no integrity verification, and installer privileges not fully least-privilege.
Positives: explicit stepwise workflow, single date capture, fallback ladders (primary→alternate→search→null), partial-failure rule, JSON retry, corrupted-history handling — error paths are unusually thorough. Deducted for: static review cannot reproduce key paths; quality of 5 sub-agent outputs depends on model behavior; no committed tests cover these paths, so end-to-end stability is unproven; capped below 10.
Positives: explicit trigger phrases, interactive risk-profile confirmation, declared non-fit boundaries (educational, not advice), HTML fallback without Node. Deducted for: core function depends entirely on overseas APIs (CoinGecko, Yahoo, Frankfurter) with doubtful mainland-China reachability and no mitigation disclosed; no Chinese-language support; broad triggers (e.g. 'investment advice') risk false invocation.
Positives: MIT license, version 2.1.0 with CI-enforced plugin./SKILL.md parity, referenced CHANGELOG, thorough bilingual README, JSON Schema data contract validated in CI. Deducted for: CI version-parity script appears to grep SKILL.md at repo root while the skill lives in a subdirectory (possible script defect), maintenance/update path rests on a single personal account, no FAQ or detailed known-issues list.
Positives: claims a directly usable bilingual interactive report with sample fixtures, template, and schema; clear marginal value via automated multi-asset research. Deducted for: static review cannot verify outputs are directly usable; free-endpoint prices may lag; screenshots unverifiable here; cost/benefit of 5 parallel web-research agents unquantified.
Positives: a real CI workflow with lint, build, ajv schema validation, version parity and installer syntax checks — auditable primary material. Deducted for: no tests over the skill's key orchestration paths, no third-party execution evidence, historical accuracy self-described as a local heuristic rather than audited, thin coverage — capped below 5.
- Core data sources (CoinGecko/Yahoo/Frankfurter) may be unreachable from mainland-China networks; no alternate endpoints or local caching are provided, so the whole chain may degrade to null values.
- The README's curl | bash one-line installer executes an unreviewed script with user privileges; prefer manual clone and read install.sh before installing.
- The dashboard has no authentication — never expose it publicly; output is educational analysis, not financial advice, and the historical-accuracy metric is a local heuristic, not an audited track record.
- This was a static review with no execution: the 5-agent orchestration, fallback ladders and partial-failure rule are unverified in practice; run a small-scale trial first and verify data accuracy.
What does this skill do, and when should you use it?
This is the investment-analysis skill (v2.1.0) from the Hainrixz/maia-skill repository, a multi-agent investment research system for Claude Code branded "Tododeia." It asks your risk tolerance (conservative/moderate/aggressive), spawns four parallel sector research agents (crypto, stocks, currencies, commodities), then a strategy agent performs cross-sector synthesis, risk-adjusted ranking, and portfolio allocation, serving an interactive EN/ES Next.js dashboard locally. Market data comes primarily from free keyless APIs (CoinGecko, Yahoo Finance, Frankfurter) with optional Finnhub/Polygon premium keys; history is cached locally with accuracy tracking. An educational disclaimer is mandatory and output uses analytical phrasing rather than buy/sell instructions.
Loads five agent prompts from references/agent-prompts.md; asks the user's risk profile; fetches prices via keyless endpoints (CoinGecko / Yahoo v8 chart / Frankfurter) with a fallback ladder (alternate endpoint → WebSearch → null), using WebSearch for news and social sentiment; four sector agents return structured JSON under a numeric data contract; a strategy agent synthesizes macro environment, allocation, cross-sector insights, and risk-adjusted picks; report data is written to ~/.claude/cache/tododeia (keeping the last 30 runs) and a translation agent produces a Spanish report-es.; the primary path serves a Next.js dashboard at localhost:3420, falling back to a standalone HTML report served by Python's http.server on port 8420 when Node.js is unavailable.
- A retail investor who wants a daily or weekly panoramic market briefing across crypto, stocks, forex and commodities
- Spanish-speaking users who want both the UI and the report content to toggle between English and Spanish
- Traders who want scoring, position sizing, and allocation reshaped by a conservative, moderate, or aggressive risk profile
- Anyone who wants to track how previous AI signals performed (a local heuristic, not an audited record)
- Checking social sentiment and key news for an asset class before a trade
- Running in an environment without Node.js, using the standalone HTML report served via Python
What are this skill's strengths and limitations?
- Parallel four-sector research plus a cross-sector strategy agent, with a numeric data contract validated in CI
- Hybrid keyless data sources with a deterministic fallback ladder and an explicit partial-failure rule
- Fully bilingual EN/ES — UI and report data translate on toggle while numbers and tickers stay intact
- Strong security and privacy posture: CSP, XSS-hardened HTML fallback, local-only data cache, mandatory educational disclaimer
- Accessible (ARIA, keyboard navigation, reduced-motion) and customizable agent prompts, risk multipliers, and styling
- Deeply dependent on Claude Code-specific mechanisms (Agent tool subagents, AskUserQuestion); porting requires rewriting the orchestration
- Free public APIs may lag the market; some instruments fall back to search estimates
- The "historical accuracy" metric is a local heuristic, not an audited track record
- The dashboard has no authentication; the README explicitly warns against exposing it to the public internet
- No evidence of a test suite or independent performance benchmarks in the source; requires internet — offline runs produce null prices
How do you install this skill?
Choose one of three ways: 1) One-liner: curl -sL https://raw.githubusercontent.com/Hainrixz/maia-skill/main/install.sh | bash; 2) Claude Code plugin: claude plugin install Hainrixz/maia-skill; 3) Manual: git clone https://github.com/Hainrixz/maia-skill.git, then ln -s "$(pwd)/maia-skill/.claude/skills/investment-analysis" ~/.claude/skills/investment-analysis and npm install --prefix maia-skill/dashboard. Optionally set FINNHUB_API_KEY or POLYGON_API_KEY for premium stock data. MIT licensed. Note: the repo bundles 2 skills; this profile covers only investment-analysis.
How do you use this skill?
In a Claude Code conversation, use a trigger phrase such as "Run an investment analysis", "Analyze the markets", "Run tododeia", or the Spanish "analiza mercados". Answer the risk-profile question; the five agents then run and the report opens at http://localhost:3420. On first open, acknowledge the educational not-advice notice and pick a language. Generated data goes to ~/.claude/cache/tododeia, not your project folder. Schedule recurring runs with /loop 24h /investment-analysis (daily) or /loop 168h /investment-analysis (weekly).