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passive-deal-screener

Screens private investment opportunities from the passive LP (limited-partner) perspective — equity syndications, preferred equity, hard money / bridge funds, and private credit — pasted from any source (an investment platform listing, a direct GP email, raw terms). Produces a deal snapshot, stress-tests returns against public benchmarks, deconstructs the fee stack to a gross-to-net drag, surfaces severity-ranked red flags and missing disclosures, assesses GP alignment, and generates prioritized GP questions with bad-answer signals. Use whenever asked to analyze, evaluate, screen, or review a real-estate or private-market deal, syndication, offering, or investment opportunity — e.g. 'is this deal worth pursuing', 'what should I ask the GP', 'analyze this offering memo'. LP lens only; not operator-side underwriting (acquisition/development/asset-management). For corporate-finance DCF/ratio work use financial-analyst.

74

Quality

93%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is passive-deal-screener in alirezarezvani/claude-skills

SKILL.md
Quality
Evals
Security

Quality

Content

85%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.

A tightly written, highly operational instruction skill with excellent routing, an explicit output schema, and a built-in self-check — but the bundle is broken: every referenced reference file and script is absent, so the skill's core citation-driven workflow cannot execute as shipped. Fixing the bundle and showing script invocations would lift this to near-top marks across the board.

Suggestions

Ship the referenced bundle files: references/01-asset-class-norms.md through 05-benchmark-returns.md and scripts/benchmark_comparator.py and scripts/fee_drag_calculator.py are cited as the factual foundation (every flag, norm, question, and benchmark is cited from them) but none exist in the bundle, leaving the routing table and ID-citation rules pointing at nothing.

Add one-line invocation examples for both scripts (command syntax, inputs, expected output) — e.g. how benchmark_comparator.py is called and what it returns — so the §2 and §4 steps are executable rather than inferential.

Include a small example of the JSON artifact object (or per-field types) so the 'Output as JSON' mode has an unambiguous target shape instead of just a field name list.

DimensionReasoningScore

Conciseness

The 118-line body is dense and telegraphic throughout — it assumes Claude knows what a syndication, waterfall, J-curve, or cap rate is, uses routing tables instead of prose, and devotes essentially every line to operational rules ('Never gross-to-gross', 'pass 0 for undisclosed fees'). It matches the 5 anchor (lean, assumes competence, every token earns its place) rather than 4, since there is no over-explanation of known concepts, only scope-driven length.

5 / 5

Actionability

Highly concrete guidance — a per-deal-type routing table (02 section, 03 flag prefixes, 05 comparator), a 10-section output schema, ID-citation rules, and named helper scripts. It falls short of the 5 anchor because script usage is never shown: 'scripts/benchmark_comparator.py' gets no invocation syntax at all, 'scripts/fee_drag_calculator.py' only hints at args ('pass 0 for undisclosed fees'), and the JSON artifact is a bare field list with no example object.

4 / 5

Workflow Clarity

Clear sequenced workflow (Classify & parse → Route by deal type → Analyze & assemble) with explicit branching that reconverges on the same 10 sections, mode handling, an artifacts table mapping asks to outputs, and an explicit pre-emission validation step: 'Self-check before emitting: every flag family touched in §§2–3 appears by ID in §5, and every stated pref / waterfall / debt term has its 04 probe cited by ID in §8.' This analysis skill is neither destructive nor batch, so the cap-3 rule doesn't apply; the checklist satisfies the 5 anchor.

5 / 5

Progressive Disclosure

The in-file design is strong — a load-condition table ('Always' vs 'GP identified'), one-level-deep references, and clear pointers — but scored against the actual bundle, all five 'references/*.md' files and both 'scripts/*.py' files cited as the skill's 'factual foundation' are missing from the bundle entirely. The routing leads nowhere as shipped, and the mandate that 'every norm, fee, flag, question, benchmark comes from references/' cannot be satisfied. This sits at 3: better organized than the 2 anchor's buried/inlined failure, but the broken pointers preclude 4.

3 / 5

Total

17

/

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.

An exemplary description: concrete, third-person action inventory; explicit 'Use whenever' trigger clause with natural quoted example phrases; and clear boundary routing to adjacent skills. No vague fluff or over-claims despite its length — every clause does routing or capability work.

DimensionReasoningScore

Specificity

The description lists six concrete, comprehensive actions in third person — 'Produces a deal snapshot, stress-tests returns against public benchmarks, deconstructs the fee stack to a gross-to-net drag, surfaces severity-ranked red flags and missing disclosures, assesses GP alignment, and generates prioritized GP questions with bad-answer signals.' This matches the 5 anchor (multiple specific concrete actions, comprehensive coverage) rather than 4, since no capability area is missing or generic.

5 / 5

Completeness

Both 'what' and 'when' are explicitly and concretely answered: the action list defines what the skill does, and 'Use whenever asked to analyze, evaluate, screen, or review...' with example trigger phrases defines when. This is the 5 anchor verbatim in structure (explicit what AND when with concrete trigger phrases), not 4, since neither half could be more explicit.

5 / 5

Trigger Term Quality

Comprehensive natural-term coverage: 'analyze, evaluate, screen, or review a real-estate or private-market deal, syndication, offering, or investment opportunity' with quoted phrases users would actually say — 'is this deal worth pursuing', 'what should I ask the GP', 'analyze this offering memo'. Synonyms and domain vocabulary (LP, GP, hard money, bridge, private credit) are all covered, matching the 5 anchor; nothing common is missing.

5 / 5

Distinctiveness Conflict Risk

A clear niche (passive-LP screening of private-market deals) with explicit carve-outs that route away competing skills: 'LP lens only; not operator-side underwriting (acquisition/development/asset-management). For corporate-finance DCF/ratio work use financial-analyst.' Distinct triggers and minimal conflict risk match the 5 anchor; the only mild overlap (generic investment analysis) is explicitly fenced off.

5 / 5

Total

20

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

referenced_paths_exist

Referenced path issues: 2 missing

Warning

Total

15

/

16

Passed

Repository

alirezarezvani/claude-skills

Path

.gemini/skills/passive-deal-screener/SKILL.md

Reviewed

Table of Contents

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