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navoditknavoditk/pm-ai-skills-framework/skills/risk-explanation/SKILL.md

risk-explanation

Skill0 starsChanged 45 days agoMITView on GitHub

Explain the material sources and changes in portfolio risk using authoritative risk analytics.

The file

We split this skill into the rules it gives an agent

8 rules. About 0 are common, 5 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

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

    line 12
  2. Very specific:

    When activated, read this SKILL.md before selecting tools or making risk claims.

    line 13
  3. Distinctive:

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

    line 18
  4. Distinctive:

    Call approved logical capabilities from the Agentic Data Pipeline.

    line 23
  5. Distinctive:

    Prefer authoritative internal analytical capabilities over independent LLM calculations.

    line 33
  6. Distinctive:

    Never fabricate a portfolio value, benchmark, position, return, exposure, or risk statistic.

    line 34
  7. Very specific:

    Use risk.factor_exposure for factor-level risk and risk.scenario for stress-scenario impact.

    line 43
  8. Distinctive:

    Never infer a missing official factor exposure or scenario result.

    line 50

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Risk Explanation

Use when

Use this skill when the user request clearly requires the capability described above. When activated, read this SKILL.md before selecting tools or making risk claims. Treat the procedure below as the required workflow.

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. Identify the largest factor exposures or scenario impacts as the material drivers, rather than narrating every field.
  5. Include derivative and hedge positions when assessing exposure.
  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.factor_exposure for factor-level risk and risk.scenario for stress-scenario impact; use portfolio.positions when exposure needs to be tied to specific holdings.
  • Include derivative-equivalent exposure when derivatives are present in the portfolio's positions.
  • Distinguish gross exposure (sum of position weights) from net exposure (positions netted against hedges) when both are relevant.
  • Never infer a missing official factor exposure or 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: risk-explanation after completing the workflow. This marker is an execution trace, not a substitute for the supporting evidence.

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