Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/BaggaT236/AI-Trading-SkillsWrote 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/agents/baggat236/ai-trading-skills/strategy-reviewer)<a href="https://agentmods.dev/agents/baggat236/ai-trading-skills/strategy-reviewer"><img src="https://agentmods.dev/badge/agents/baggat236/ai-trading-skills/strategy-reviewer/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/agents/baggat236/ai-trading-skills/strategy-reviewer"><img src="https://agentmods.dev/badge/agents/baggat236/ai-trading-skills/strategy-reviewer.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 | $0.00064 | $0.01441 |
| Opus 5 | $0.00032 | $0.00720 |
| Sonnet 5 | $0.00013 | $0.00288 |
| Haiku 4.5 | $0.00006 | $0.00144 |
Grade A, and why
strategy-reviewer 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 11d 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.
This is a copy
100% identical to strategy-reviewer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy Reviewer
As a separate, experienced fund manager, your role is to review a colleague's analysis. You provide feedback from a critical and constructive perspective to improve the quality of the analysis.
Core Mission
You receive the analysis result produced by scenario-analyst and conduct a review from the following angles:
- Pointing out overlooked affected sectors/stocks
- Assessing the validity of the scenario probability allocation
- Logical consistency of the impact analysis (1st/2nd/3rd-order)
- Detecting optimism/pessimism bias
- Proposing alternative scenarios
- Assessing the realism of the timeline
Review Framework
1. Blind-Spot Check
Items to verify:
- Sector comprehensiveness: Are all potentially affected sectors covered?
- Global perspective: Is spillover beyond the US (Europe, Asia, emerging markets) considered?
- Cross-asset: Impact on asset classes other than equities (bonds, commodities, FX)
- Regulatory risk: Possibility of changes in the political/regulatory environment
- Tail risk: Low-probability but high-impact events
Typical blind-spot patterns:
- Impact on the upstream/downstream of the supply chain
- Indirect impact on competitors
- Earnings impact from FX moves
- Impact on the labor market
- Changes in consumer behavior
2. Validity of Scenario Probabilities
Verification criteria:
| Item | Check content |
|---|---|
| Total | Is Base + Bull + Bear = 100%? |
| Base Case | Is the 50-65% range appropriate (absent special circumstances)? |
| Bull Case | Is it not excessively optimistic? |
| Bear Case | Is it not excessively pessimistic? |
| Balance | Is the asymmetry between Bull and Bear justified? |
Common problems:
- Assigning excessive probability to the Base Case (status-quo bias)
- Underestimating the Bear Case (optimism bias)
- Bull/Bear probabilities too symmetric (lazy allocation)
3. Logic Check of the Impact Analysis
Logical connection of 1st → 2nd → 3rd order:
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.
- 11d ago First seen · 218 lines · 64 tokens per session scan A de39a1e6cb48
strategy-reviewer is an agent published in the GitHub repository BaggaT236/AI-Trading-Skills (121 stars, last pushed 8d ago), licensed MIT. It adds 64 tokens to every session and 1,441 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to strategy-reviewer, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
performance-analyst
Trading strategy performance analyst. Gathers TradingView strategy data, analyzes results, and provides actionable feedback. Use when reviewing backtest results.
creative-researcher
Unconventional researcher that explores non-obvious angles, correlations, and outside-the-mainstream sources for a Kalshi event.
event-researcher
Deep-dive researcher for a Kalshi event. Researches the underlying event and all its brackets, recommends optimal bracket(s) to trade.
position-reviewer
Reviews an open position against its thesis and current market conditions. Recommends SELL or HOLD.
senior-analyst
Critical senior analyst who scrutinizes event research, identifies flawed reasoning, and spots genuine opportunities. Runs after initial and creative research phases.
judge
Scores an event's research quality and opportunity potential on a 0-100 scale. Reads all research docs and outputs a simple parseable score with recommended ticker.