dcf-model

Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.

Quality

75%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

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tessl review fix ./plugins/agent-plugins/model-builder/skills/dcf-model/SKILL.md

Quality

Content

58%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 and exceptionally well-sequenced with validation feedback loops, but it is far too verbose — heavy internal repetition and conceptual padding — and fails to split its bulk into reference files, leaving navigation poor and one reference dangling.

Suggestions

Consolidate the repeated sensitivity-table, scenario-block, and cell-comment guidance into a single authoritative section; remove the duplicated restatements across Critical Constraints, correct_patterns, and common_mistakes.

Move the large formatting/border/number-format standards and the detailed sensitivity-table implementation into separate reference files (e.g. FORMATTING.md, SENSITIVITY.md) and link to them one level deep, turning SKILL.md into an overview.

Fix or remove the broken ./TROUBLESHOOTING.md reference and actually wire up the bundled scripts/validate_dcf.py from the body so the existing bundle is used instead of only the external xlsx skill's recalc.py.

Dimension	Reasoning	Score
Conciseness	At ~1263 lines the body is noticeably verbose with substantial duplication: sensitivity-table instructions are restated ~4 times, scenario-block structure ~4 times, cell-comment format ~3 times, and WACC formulas appear in multiple places, plus the common_mistakes section restates constraints already in Critical Constraints — even if much is domain-specific rather than filler.	2 / 5
Actionability	Provides concrete executable guidance — exact cell references (e.g. '=E29*(1+$E$10)'), full sheet-structure CSV templates, and code patterns — with only minor gaps such as pseudocode placeholders like '=<DCF recalc using {wacc_value} and {term_growth_value}>' instead of a complete sensitivity formula.	4 / 5
Workflow Clarity	A clearly sequenced 10-step DCF workflow with explicit validation checkpoints and feedback loops (verify-with-user after each stage, terminal-value 50-70% sanity check, sensitivity center-cell = base-case check, 're-run recalc.py until status is success') — matching the top anchor.	5 / 5
Progressive Disclosure	The skill is a monolithic single 1263-line file; large blocks (assumption-table structure, sensitivity-table implementation, formatting standards) clearly belong in separate reference files, the only body reference './TROUBLESHOOTING.md' is a broken link (file absent), and the bundled scripts/validate_dcf.py is never referenced from the body.	2 / 5
	Total	13 / 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.

A strong, specific description that clearly names the domain, concrete actions, and explicit use-when triggers in third person. Minor trigger-term synonym coverage gaps keep it just shy of perfect on keywords.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries' — giving comprehensive coverage of capabilities.	5 / 5
Completeness	Explicitly answers both what (DCF model creation, cash flow projections, WACC, sensitivity, Excel output) and when ('Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations').	5 / 5
Trigger Term Quality	Good natural-keyword coverage ('DCF', 'intrinsic value analysis', 'financial modeling', 'growth projections', 'terminal value', 'SEC filings', 'Excel models') but missing common synonyms like 'valuation' and any file-extension triggers; not quite comprehensive enough for a 5.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear, narrow niche (DCF equity valuation) with distinct triggers ('DCF methodology', 'intrinsic value', 'terminal value calculations') and minimal overlap risk with adjacent finance skills.	5 / 5
	Total	19 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (1264 lines); consider splitting into references/ and linking	Warning
relative_links	Relative link issues: 2 missing	Warning

	Total	14 / 16 Passed

Repository: anthropics/financial-services
Path: plugins/agent-plugins/model-builder/skills/dcf-model/SKILL.md
Commit: 69cbc81

Reviewed: 20 days ago

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