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scenario-analysis

Explain portfolio behavior under approved deterministic market or risk scenarios.

Skill by PM AI <pm-ai@example.com>

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/navoditk-scenario-analysis

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Explain portfolio behavior under approved deterministic market or risk scenarios.

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Secrets
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Install scripts
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Runs as
Python package

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/scenario-analysis

What's inside

Skill name
scenario-analysis
Description
Explain portfolio behavior under approved deterministic market or risk scenarios.
License
MIT
Metadata
author: PM AI <pm-ai@example.com>
Instructions
51 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (8)

  • CHANGELOG.md
  • SKILL.md
  • EVAL.md · evals/
  • config.yml · evals/
  • evals.json · evals/
  • grader.py · evals/
  • skill.yaml
  • README.md · tests/

SKILL.md (first lines)

# Scenario Analysis

## Use when

Use this skill when the user request clearly requires the capability described above.

## Do not use when

Do not activate this skill for unrelated market lookup, general education, or requests that can be answered without this business capability.

## Procedure

1. Resolve portfolio, scope, and relevant as-of date(s).
2. Call approved logical capabilities from the Agentic Data Pipeline.
3. Validate returned dates, identifiers, and coverage.
4. Reconcile quantitative claims whenever authoritative totals exist.
5. Identify material results rather than narrating every field.
6. Explain uncertainty or missing data explicitly.
7. Produce a concise PM-oriented answer grounded in tool results.

## Data and tool rules

- Prefer authoritative internal analytical capabilities over independent LLM calculations.
- Never fabricate a portfolio value, benchmark, position, return, exposure, or risk statistic.
- Never bypass authorization.
- Preserve source and as-of metadata for quantitative claims.
- In the sandboxed reference evaluation, invoke the staged deterministic bridge
  with `python /workspace/repo/synthetic_data_pipeline/tool_cli.py TOOL_NAME`
  and the relevant flags. Treat its JSON response as the logical-tool result.

## Domain rules

- Use `risk.scenario` for the scenario impact and `portfolio.positions` when
  the impact needs to be tied to specific holdings.
- Only use scenarios explicitly approved for this evaluation; never invent
  a scenario or extrapolate an unapproved one.
- Never infer a missing scenario result; report it as unavailable instead.

## Output

Provide:
- key conclusion;
- material drivers;
- supporting quantitative evidence;
- important limitations/data gaps.

For traceability, end the response with `Workflow: scenario-analysis`
after completing the workflow. This marker is an execution trace, not a
substitute for the supporting evidence.

From navoditk/pm-ai-skills-framework, MIT.

More from navoditk/pm-ai-skills-framework

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Last commit (this folder)
Sep 1, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from PM AI <pm-ai@example.com>

Cite this page

scenario-analysis skill. Agent Mart. https://agentmart.dev/skills/navoditk-scenario-analysis (last updated 2026-10-24)