scenario-analyzer

Name: scenario-analyzer
Author: ajeeshworkspace

48stars41forks

How to install scenario-analyzer?

ajeeshworkspace/indian-trading-skills/scenario-analyzer

$npx -y skills add ajeeshworkspace/indian-trading-skills --skill scenario-analyzer

Installs into the current project.

Summary

Analyze news headlines, policy announcements, or geopolitical events to build 18-month probabilistic scenarios for Indian markets. Use when the user provides a headline or asks about the market impact of RBI policy, government announcements, global events, budget, or sector-speci

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SKILL.md

1	# Scenario Analyzer (India Markets)
2
3	## Overview
4
5	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.
6
7	## Architecture
8
9	```
10	Skill (Orchestrator)
11	├── Phase 1: Preparation
12	│ ├── Headline parsing (keywords, entities, actions, numbers)
13	│ ├── Event classification
14	│ └── Load references
15	├── Phase 2: Analysis
16	│ ├── Collect related news (past 2 weeks via WebSearch)
17	│ ├── Build 3 scenarios (Base/Bull/Bear, probabilities sum to 100%)
18	│ ├── Map 1°/2°/3° sector impacts
19	│ └── Identify 3-5 positive + 3-5 negative impact stocks
20	└── Phase 3: Report Generation
21	├── Compile findings
22	├── Assess scenario probability distribution
23	└── Save report
24	```
25
26	## Event Classification
27
28	Classify the headline into one of these categories:
29
30	\| Category \| Indian Context Examples \|
31	\|----------\|----------------------\|
32	\| Monetary Policy \| RBI rate decision, CRR/SLR change, liquidity measures \|
33	\| Fiscal Policy \| Union Budget, GST changes, PLI schemes, disinvestment \|
34	\| Geopolitical \| India-China border, India-Pakistan, Russia-Ukraine, Middle East \|
35	\| Commodity \| Crude oil shock, gold prices, metal tariffs, food inflation \|
36	\| Regulatory \| SEBI rules, RBI NPA norms, telecom spectrum, pharma FDA \|
37	\| Corporate \| Major M&A, earnings surprise, promoter pledging, fraud \|
38	\| Global Macro \| Fed rate decision, US recession, China slowdown, tariffs \|
39	\| Weather/Agriculture \| Monsoon forecast, crop damage, food prices \|
40	\| Elections/Political \| State elections, central govt policy shifts \|
41
42	## Workflow
43
44	### Phase 1: Preparation
45
46	1. Parse the Headline
47	- Extract key entities (companies, sectors, countries, institutions)
48	- Identify the action (increase, decrease, ban, approve, delay)
49	- Note any numbers (rate changes, ₹ amounts, percentages)
50	- Classify the event type
51
52	2. Load References
53	```
54	Read: references/headline_event_patterns.md
55	Read: references/sector_sensitivity_matrix.md
56	Read: references/scenario_playbooks.md
57	```
58
59	### Phase 2: Analysis
60
61	3. Collect Context
62	- Use WebSearch to find related news from the past 2 weeks
63	- Identify any pre-existing trends or expectations
64	- Note market's initial reaction if available
65
66	4. Build 3 Scenarios
67
68	For each scenario:
69	- Name: Descriptive title
70	- Probability: Must sum to 100% across all 3
71	- Timeline: 3 phases (0-6 months, 6-12 months, 12-18 months)
72	- Description: What unfolds in each phase
73	- Key Assumptions: What must hold true
74
75	Typical structure:
76	- Base Case (40-55%): Most likely outcome given current trajectory
77	- Bull Case (20-35%): Optimistic scenario with positive catalysts
78	- Bear Case (15-30%): Pessimistic scenario with adverse developments
79
80	5. Map Sector Impacts
81
82	For each scenario, assess impacts using the sector sensitivity matrix:
83
84	\| Order \| Definition \| Example (RBI Rate Cut) \|
85	\|-------\|-----------\|----------------------\|
86	\| 1st \| Direct, immediate \| Banks: NIM compression, Housing: demand boost \|
87	\| 2nd \| Indirect, 3-6 months \| Auto: loan demand, Real estate: prices \|
88	\| 3rd \| Tertiary, 6-18 months \| Cement: construction demand, Durables: consumer spending \|
89
90	Use NSE sectoral indices:
91	- Nifty Bank, Nifty IT, Nifty Pharma, Nifty Auto, Nifty FMCG
92	- Nifty Metal, Nifty Realty, Nifty Energy, Nifty Infra
93	- Nifty PSU Bank, Nifty Private Bank, Nifty Financial Services
94
95	6. Identify Stock Impacts
96
97	For each scenario:
98	- 3-5 stocks that benefit most (positive impact)
99	- 3-5 stocks that suffer most (negative impact)
100
101	For each stock, provide:
102	- Ticker (NSE symbol)
103	- Current price (use broker MCP `get_ltp` — Groww or Zerodha Kite — if available)
104	- Impact channel (why this stock is affected)
105	- Magnitude estimate (High/Medium/Low)
106
107	### Phase 3: Report Generation
108
109	7. Generate Report
110
111	Save as `reports/scenario_analysis_<topic>_YYYYMMDD.md` with sections:
112
113	1. Related News (5-10 recent articles with sources)
114	2. Scenario Overview (3 scenarios with probabilities)
115	3. Timeline (0-6m, 6-12m, 12-18m phases for base case)
116	4. Sector Impact Matrix (1°/2°/3° impacts per sector)
117	5. Positive Impact Stocks (3-5 with rationale)
118	6. Negative Impact Stocks (3-5 with rationale)
119	7. Investment Implications (actionable takeaways)
120	8. Risk to Scenarios (what could shift probabilities)
121	9. Disclaimer
122
123	## Quality Standards
124
125	- All probabilities must sum to 100%
126	- Every impact cla

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scenario-analyzer

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View on GitHub

How to install scenario-analyzer?

ajeeshworkspace/indian-trading-skills/scenario-analyzer

$npx -y skills add ajeeshworkspace/indian-trading-skills --skill scenario-analyzer

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Scenario Analyzer (India Markets)
2
3	## Overview
4
5	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.
6
7	## Architecture
8
9	```
10	Skill (Orchestrator)
11	├── Phase 1: Preparation
12	│ ├── Headline parsing (keywords, entities, actions, numbers)
13	│ ├── Event classification
14	│ └── Load references
15	├── Phase 2: Analysis
16	│ ├── Collect related news (past 2 weeks via WebSearch)
17	│ ├── Build 3 scenarios (Base/Bull/Bear, probabilities sum to 100%)
18	│ ├── Map 1°/2°/3° sector impacts
19	│ └── Identify 3-5 positive + 3-5 negative impact stocks
20	└── Phase 3: Report Generation
21	├── Compile findings
22	├── Assess scenario probability distribution
23	└── Save report
24	```
25
26	## Event Classification
27
28	Classify the headline into one of these categories:
29
30	\| Category \| Indian Context Examples \|
31	\|----------\|----------------------\|
32	\| Monetary Policy \| RBI rate decision, CRR/SLR change, liquidity measures \|
33	\| Fiscal Policy \| Union Budget, GST changes, PLI schemes, disinvestment \|
34	\| Geopolitical \| India-China border, India-Pakistan, Russia-Ukraine, Middle East \|
35	\| Commodity \| Crude oil shock, gold prices, metal tariffs, food inflation \|
36	\| Regulatory \| SEBI rules, RBI NPA norms, telecom spectrum, pharma FDA \|
37	\| Corporate \| Major M&A, earnings surprise, promoter pledging, fraud \|
38	\| Global Macro \| Fed rate decision, US recession, China slowdown, tariffs \|
39	\| Weather/Agriculture \| Monsoon forecast, crop damage, food prices \|
40	\| Elections/Political \| State elections, central govt policy shifts \|
41
42	## Workflow
43
44	### Phase 1: Preparation
45
46	1. Parse the Headline
47	- Extract key entities (companies, sectors, countries, institutions)
48	- Identify the action (increase, decrease, ban, approve, delay)
49	- Note any numbers (rate changes, ₹ amounts, percentages)
50	- Classify the event type
51
52	2. Load References
53	```
54	Read: references/headline_event_patterns.md
55	Read: references/sector_sensitivity_matrix.md
56	Read: references/scenario_playbooks.md
57	```
58
59	### Phase 2: Analysis
60
61	3. Collect Context
62	- Use WebSearch to find related news from the past 2 weeks
63	- Identify any pre-existing trends or expectations
64	- Note market's initial reaction if available
65
66	4. Build 3 Scenarios
67
68	For each scenario:
69	- Name: Descriptive title
70	- Probability: Must sum to 100% across all 3
71	- Timeline: 3 phases (0-6 months, 6-12 months, 12-18 months)
72	- Description: What unfolds in each phase
73	- Key Assumptions: What must hold true
74
75	Typical structure:
76	- Base Case (40-55%): Most likely outcome given current trajectory
77	- Bull Case (20-35%): Optimistic scenario with positive catalysts
78	- Bear Case (15-30%): Pessimistic scenario with adverse developments
79
80	5. Map Sector Impacts
81
82	For each scenario, assess impacts using the sector sensitivity matrix:
83
84	\| Order \| Definition \| Example (RBI Rate Cut) \|
85	\|-------\|-----------\|----------------------\|
86	\| 1st \| Direct, immediate \| Banks: NIM compression, Housing: demand boost \|
87	\| 2nd \| Indirect, 3-6 months \| Auto: loan demand, Real estate: prices \|
88	\| 3rd \| Tertiary, 6-18 months \| Cement: construction demand, Durables: consumer spending \|
89
90	Use NSE sectoral indices:
91	- Nifty Bank, Nifty IT, Nifty Pharma, Nifty Auto, Nifty FMCG
92	- Nifty Metal, Nifty Realty, Nifty Energy, Nifty Infra
93	- Nifty PSU Bank, Nifty Private Bank, Nifty Financial Services
94
95	6. Identify Stock Impacts
96
97	For each scenario:
98	- 3-5 stocks that benefit most (positive impact)
99	- 3-5 stocks that suffer most (negative impact)
100
101	For each stock, provide:
102	- Ticker (NSE symbol)
103	- Current price (use broker MCP `get_ltp` — Groww or Zerodha Kite — if available)
104	- Impact channel (why this stock is affected)
105	- Magnitude estimate (High/Medium/Low)
106
107	### Phase 3: Report Generation
108
109	7. Generate Report
110
111	Save as `reports/scenario_analysis_<topic>_YYYYMMDD.md` with sections:
112
113	1. Related News (5-10 recent articles with sources)
114	2. Scenario Overview (3 scenarios with probabilities)
115	3. Timeline (0-6m, 6-12m, 12-18m phases for base case)
116	4. Sector Impact Matrix (1°/2°/3° impacts per sector)
117	5. Positive Impact Stocks (3-5 with rationale)
118	6. Negative Impact Stocks (3-5 with rationale)
119	7. Investment Implications (actionable takeaways)
120	8. Risk to Scenarios (what could shift probabilities)
121	9. Disclaimer
122
123	## Quality Standards
124
125	- All probabilities must sum to 100%
126	- Every impact cla

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