performance-attribution
Skill0 starsChanged 44 days agoMITView on GitHub
Explain absolute and relative portfolio performance using authoritative attribution outputs and reconciled contributors.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- microsoft/aibast-agents-librarytheir own version, first seen Oct 13, 2026
- IngAs8/ClaudeSkilltheir own version, first seen Sep 28, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- microsoft/aibast-agents-library★ 8same name
- IngAs8/ClaudeSkill★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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
- pm-commentary-generationSkill
- portfolio-change-analysisSkill
- portfolio-overviewSkill
- position-investigationSkill
- risk-explanationSkill
- scenario-analysisSkill
- skillevaluator-masterySkill
The file
We split this skill into the rules it gives an agent
9 rules. About 0 are common, 6 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
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 performance 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 26
Check that the benchmark matches the requested benchmark and that attribution and portfolio snapshots use the same as-of date.
- Distinctive: line 34
Prefer authoritative internal analytical capabilities over independent LLM calculations.
- Distinctive: line 35
Never fabricate a portfolio value, benchmark, position, return, exposure, or risk statistic.
- Very specific: line 44
Use the authoritative performance.attribution capability for official attribution.
- Distinctive: line 48
Do not invent residual contributors.
≈ “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
Performance Attribution
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
performance 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.
- Reconcile quantitative claims whenever authoritative totals exist.
- Check that the benchmark matches the requested benchmark and that attribution and portfolio snapshots use the same as-of date.
- Identify material results rather than narrating every field.
- 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 the authoritative
performance.attributioncapability for official attribution. - Reconcile component contributions to total relative return within configured tolerance.
- Distinguish absolute return from relative return.
- Include derivative and hedge coverage when positions contain derivatives.
- Do not invent residual contributors.
Output
Provide:
- key conclusion;
- material drivers;
- supporting quantitative evidence;
- important limitations/data gaps.
For traceability, end the response with Workflow: performance-attribution
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.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- 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.

