risk-explanation
Skill0 starsChanged 45 days agoMITView on GitHub
Explain the material sources and changes in portfolio risk using authoritative risk analytics.
In this repository18 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- skillevaluator-masterySkill
- skillevaluator-masterySkill
- benchmark-comparisonSkill
- concentration-analysisSkill
- exposure-analysisSkill
- liquidity-analysisSkill
- m1-tier3-smokeSkill
- market-move-explanationSkill
- performance-attributionSkill
- pm-commentary-generationSkill
- portfolio-change-analysisSkill
- portfolio-overviewSkill
- position-investigationSkill
- scenario-analysisSkill
- skillevaluator-masterySkill
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
- Very specific: line 12
Use this skill when the user request clearly requires the capability described above.
- Very specific: line 13
When activated, read this SKILL.md before selecting tools or making risk claims.
- Distinctive: line 18
Do not activate this skill for unrelated market lookup, general education, or requests that can be answered without this business …
- Distinctive: line 23
Call approved logical capabilities from the Agentic Data Pipeline.
- Distinctive: line 33
Prefer authoritative internal analytical capabilities over independent LLM calculations.
- Distinctive: line 34
Never fabricate a portfolio value, benchmark, position, return, exposure, or risk statistic.
- Very specific: line 43
Use risk.factor_exposure for factor-level risk and risk.scenario for stress-scenario impact.
- Distinctive: line 50
Never infer a missing official factor exposure or scenario result.
≈ “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.
Files
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
- Resolve portfolio, scope, and relevant as-of date(s).
- Call approved logical capabilities from the Agentic Data Pipeline.
- Validate returned dates, identifiers, and coverage.
- Identify the largest factor exposures or scenario impacts as the material drivers, rather than narrating every field.
- Include derivative and hedge positions when assessing exposure.
- Explain uncertainty or missing data explicitly.
- 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_NAMEand the relevant flags. Treat its JSON response as the logical-tool result.
Domain rules
- Use
risk.factor_exposurefor factor-level risk andrisk.scenariofor stress-scenario impact; useportfolio.positionswhen 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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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- It worked
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

