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/position-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/position-review)<a href="https://agentmods.dev/skills/tusharagg1/aifolimizer/position-review"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/position-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/position-review"><img src="https://agentmods.dev/badge/skills/tusharagg1/aifolimizer/position-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 | $0.00136 | $0.01782 |
| Opus 5 | $0.00068 | $0.00891 |
| Sonnet 5 | $0.00027 | $0.00356 |
| Haiku 4.5 | $0.00014 | $0.00178 |
Grade A, and why
position-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 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Position Review (routing orchestrator → hold/sell verdict)
What this is
A router, not a new analysis. It picks the cheapest sufficient analysis per holding and emits one decision. Claude is the orchestrator - it calls shared MCP tools and, for deep names, runs the adversarial pipeline inline. Skills are not self-invoked.
Modes
- Single ticker ("review my NVDA position"): route + verdict for that one name.
- Sweep ("review my holdings" / automated nightly): take top-N holdings by weight (default 6) and route each. Keep total output tight.
Stage 0 - Decision Memory (load FIRST)
Before routing, load prior decisions so verdicts stay consistent across sessions:
mcp__aifolimizer__get_cross_ticker_lessonswithmax_lessons=3- portfolio-level win/loss patterns- For each name reviewed, load
mcp__aifolimizer__get_ticker_decision_history(ticker=…, max_decisions=5) andmcp__aifolimizer__get_ticker_reflection(symbol=…, n=3).
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 get_profile FIRST. Then gather routing signals (parallel):
mcp__aifolimizer__get_portfolio- holdings, weights, cost basis, returnmcp__aifolimizer__get_personal_context- province / marginal_tax_rate_pct / account_waterfall to ground per-name tax + account framing. Ifpresent=false, note the framing is generic and suggest the profile-setup skill.mcp__aifolimizer__get_earnings_calendar- earnings proximity per name (pass watchlistsymbols=only if reviewing a non-held name)mcp__aifolimizer__get_triggered_alerts(since_hours=48) - recent price/RSI/concentration flagsmcp__aifolimizer__get_earnings_resultsfor names that may have just reported
Routing table (apply per ticker, first match wins)
| Condition | Route to | Why |
|---|---|---|
| Earnings within ~7 days | earnings-analyzer flow (get_fundamentals + get_technicals + expected move) |
Pre-earnings risk dominates the decision |
| Reported in last ~5 days OR surprise flagged | earnings-postmortem flow (get_earnings_results + get_news_headlines) |
Beat/miss reaction sets the near-term path |
| Weight ≥ 8% OR a triggered alert OR user asked for depth | adversarial-research pipeline (adversarial-research) run INLINE | High stakes → full bull/bear/risk debate |
| Everything else | stock-analysis flow (get_fundamentals + get_technicals + get_positioning_signals) |
Cheap single-pass read is enough |
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 · 85 lines · 136 tokens per session scan A 38bed9c62c39
position-review is a skill published in the GitHub repository tusharagg1/aifolimizer (2 stars, last pushed 7d ago), licensed MIT. It adds 136 tokens to every session and 1,782 once invoked, about $0.0007 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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