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datapack-builder

Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.

65

Quality

79%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./plugins/vertical-plugins/investment-banking/skills/datapack-builder/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

66%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 highly actionable with a well-sequenced, validated workflow, but it is overly long and monolithic — heavy sections that should be split into referenced files are inlined, and formatting rules are repeated across sections.

Suggestions

Move industry-specific adaptations and normalization patterns into separate reference files (e.g. references/industry-metrics.md, references/normalization.md) and link to them from the body to reduce token load.

State each formatting rule once in a canonical section and reference it from Phase 3, Phase 5, and the Final Checklist instead of restating the same rules multiple times.

Split the 8-tab structural detail and the final delivery checklist into a referenced template file so SKILL.md reads as an overview with one-level-deep pointers.

DimensionReasoningScore

Conciseness

At ~650 lines the body is noticeably verbose, with formatting rules restated across the CRITICAL section, Phase 3, Phase 5, and the Final Checklist rather than referenced once.

2 / 5

Actionability

Provides concrete format strings ($#,##0.0, 0.0%), executable Python row-tracking code, and specific per-tab contents — copy-paste ready guidance covering common cases.

5 / 5

Workflow Clarity

Clear 6-phase sequence with explicit validation in Phase 5 (balance-sheet checks, #REF!/#DIV/0! error scans) and feedback loops for error recovery.

5 / 5

Progressive Disclosure

Monolithic: all content is inlined in SKILL.md with no references/ scripts/ assets/ bundles and no external file pointers; industry adaptations and normalization patterns that belong in separate files are inlined.

2 / 5

Total

14

/

20

Passed

Description

92%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 specific, well-triggered, and distinct, with clear what/when guidance and explicit negative boundaries. It is slightly jargon-heavy on trigger terms but otherwise strong.

DimensionReasoningScore

Specificity

Names multiple concrete actions — "Build professional financial services data packs", "Extract, normalize, and standardize financial data" — with comprehensive coverage of sources and outputs.

5 / 5

Completeness

Explicitly answers both what (build/extract/normalize/standardize data packs) and when ("Use for M&A due diligence, private equity analysis, investment committee materials"), plus negative boundary guidance.

5 / 5

Trigger Term Quality

Good coverage of natural triggers ("M&A due diligence", "private equity analysis", "investment committee materials") but leans on domain jargon and misses simpler synonyms a user might say.

4 / 5

Distinctiveness Conflict Risk

Clear niche — financial services data packs for investment committee — with explicit "Do not use for simple financial calculations" boundary reducing conflict risk.

5 / 5

Total

19

/

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (657 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository
anthropics/financial-services
Reviewed

Table of Contents

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