returns-analysis

Build quick IRR/MOIC sensitivity tables for PE deal evaluation. Models returns across entry multiple, leverage, exit multiple, growth, and hold period scenarios. Use when sizing up a deal, stress-testing assumptions, or preparing IC returns exhibits. Triggers on "returns analysis", "IRR sensitivity", "MOIC table", "what's the return at", "model the returns", or "back of the envelope".

Quality

84%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

68%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is concise and well-structured with a clear workflow, but actionability and workflow clarity are held back by blank template tables, a non-executable IRR method, and missing validation checkpoints for the batch output.

Suggestions

Add an executable IRR solver (e.g., a Python snippet using numpy.irr/financial or an Excel RATE/XIRR formula) so returns are computable rather than templated.

Insert a validation step after Step 2/3 to sanity-check results (e.g., confirm MOIC and IRR are internally consistent, flag negative or implausible IRR values).

Populate at least one example sensitivity cell or base-case row with sample numbers to make the tables actionable templates rather than empty shells.

Dimension	Reasoning	Score
Conciseness	Lean and efficient; assumes Claude's competence and avoids explaining finance concepts it already knows. Every section earns its place.	5 / 5
Actionability	Provides blank template tables and high-level formulas (MOIC, IRR, attribution) but no executable method for actually solving IRR or populating cells, leaving key computational detail missing.	3 / 5
Workflow Clarity	A clear 5-step sequence is present, but a modeling/batch deliverable lacks explicit validation checkpoints (e.g., sanity-check IRR/MOIC, cross-foot the waterfall), capping workflow clarity per the batch-operation guideline.	3 / 5
Progressive Disclosure	Single self-contained SKILL.md with well-organized sections and no nested or buried references; minor opportunity to split large sensitivity templates into a reference file.	4 / 5
	Total	15 / 20 Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is exemplary: it states concrete capabilities, supplies natural trigger phrases, and explicitly separates what the skill does from when to use it, all in a distinct niche.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('Build quick IRR/MOIC sensitivity tables', 'Models returns across entry multiple, leverage, exit multiple, growth, and hold period scenarios') with comprehensive coverage of capability.	5 / 5
Completeness	Explicitly answers both 'what' (build sensitivity tables, model returns) and 'when' via a 'Use when...' clause with concrete trigger phrases.	5 / 5
Trigger Term Quality	Includes natural phrases users would actually say ('returns analysis', 'IRR sensitivity', 'MOIC table', "what's the return at", 'model the returns', 'back of the envelope').	5 / 5
Distinctiveness Conflict Risk	Targets a clear PE-deal-returns niche with domain-specific triggers, minimal overlap with other skills.	5 / 5
	Total	20 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: anthropics/financial-services
Path: plugins/agent-plugins/valuation-reviewer/skills/returns-analysis/SKILL.md
Commit: 3865222

Reviewed: 5 days ago

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