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.
npx skills add ajeeshworkspace/indian-trading-skills --skill scenario-analyzergit clone --depth 1 https://github.com/ajeeshworkspace/indian-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/skills/ajeeshworkspace/indian-trading-skills/scenario-analyzer)<a href="https://agentmods.dev/skills/ajeeshworkspace/indian-trading-skills/scenario-analyzer"><img src="https://agentmods.dev/badge/skills/ajeeshworkspace/indian-trading-skills/scenario-analyzer/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/ajeeshworkspace/indian-trading-skills/scenario-analyzer"><img src="https://agentmods.dev/badge/skills/ajeeshworkspace/indian-trading-skills/scenario-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Data Exfiltration · line 66 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
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 | $0.00061 | $0.01622 |
| Opus 5 | $0.00030 | $0.00811 |
| Sonnet 5 | $0.00012 | $0.00324 |
| Haiku 4.5 | $0.00006 | $0.00162 |
Grade A, and why
scenario-analyzer 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 13d 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scenario Analyzer (India Markets)
Overview
This skill takes a news headline or event and builds probabilistic 18-month scenarios with cascading 1st, 2nd, and 3rd order sector impacts and specific stock recommendations for the Indian market.
Architecture
Skill (Orchestrator)
├── Phase 1: Preparation
│ ├── Headline parsing (keywords, entities, actions, numbers)
│ ├── Event classification
│ └── Load references
├── Phase 2: Analysis
│ ├── Collect related news (past 2 weeks via WebSearch)
│ ├── Build 3 scenarios (Base/Bull/Bear, probabilities sum to 100%)
│ ├── Map 1°/2°/3° sector impacts
│ └── Identify 3-5 positive + 3-5 negative impact stocks
└── Phase 3: Report Generation
├── Compile findings
├── Assess scenario probability distribution
└── Save report
Event Classification
Classify the headline into one of these categories:
| Category | Indian Context Examples |
|---|---|
| Monetary Policy | RBI rate decision, CRR/SLR change, liquidity measures |
| Fiscal Policy | Union Budget, GST changes, PLI schemes, disinvestment |
| Geopolitical | India-China border, India-Pakistan, Russia-Ukraine, Middle East |
| Commodity | Crude oil shock, gold prices, metal tariffs, food inflation |
| Regulatory | SEBI rules, RBI NPA norms, telecom spectrum, pharma FDA |
| Corporate | Major M&A, earnings surprise, promoter pledging, fraud |
| Global Macro | Fed rate decision, US recession, China slowdown, tariffs |
| Weather/Agriculture | Monsoon forecast, crop damage, food prices |
| Elections/Political | State elections, central govt policy shifts |
Workflow
Phase 1: Preparation
-
Parse the Headline
- Extract key entities (companies, sectors, countries, institutions)
- Identify the action (increase, decrease, ban, approve, delay)
- Note any numbers (rate changes, ₹ amounts, percentages)
- Classify the event type
-
Load References
Read: references/headline_event_patterns.md Read: references/sector_sensitivity_matrix.md Read: references/scenario_playbooks.md
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 166 lines · 61 tokens per session scan A 82df5ed90ab6
scenario-analyzer is a skill published in the GitHub repository ajeeshworkspace/indian-trading-skills (72 stars, last pushed 20d ago), licensed MIT. It adds 61 tokens to every session and 1,622 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 2026-08-30.
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