DCF valuation model, discounted cash flow, intrinsic value, WACC calculation, terminal value, free cash flow projection, equity value per share, DCF sensitivity analysis, unlevered free cash flow…

Apache-2.0Auto-check passedBusiness, Finance & HR

Install Dcf

skills CLI
$ npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install agentii-ai/agentii-investment-intelligence dcf --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf .claude/skills/dcf && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dcf
GitHub stars
207
Token cost
~2.1k tokens
SKILL.md length
876 words
Files
10 (incl. references)
Skills in repo
79
Repo updated
First seen
Licence
Apache-2.0

At a glance

DCF valuation model, discounted cash flow, intrinsic value, WACC calculation, terminal value, free cash flow projection, equity value per share, DCF sensitivity analysis, unlevered free cash flow…

  • Works in 5 steps: Inputs: resolved ticker +… → Build: write a self-contained Python… → Validate: run LibreOffice recalc; audit… → …
  • Tasks that involve Financial modeling
  • SKILL.md covers Preflight, Triggers, Defaults and Methodology, plus 8 more sections
  • Reaches agentii.ai

What it does

Dcf is an agent skill from agentii-ai/agentii-investment-intelligence. DCF valuation model, discounted cash flow, intrinsic value, WACC calculation, terminal value, free cash flow projection, equity value per share, DCF sensitivity analysis, unlevered free cash flow, present value calculation, build a DCF

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `references/cost-of-capital-methodology.md`, `references/formula-sheet.md` and `references/institutional-defaults.md`).

It sits in Business, Finance & HR, covering Financial modeling. The repository describes itself as: Claude-type skills for institutional equity research — 25 AI agent skills with SEC filings, XBRL financials, earnings calendars, DCF/comps/LBO models, and PPT generation. Powered… The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Financial modeling

Example prompts

  • “/dcf”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Inputs: resolved ticker + search_xbrl_facts (Income Statement, Balance Sheet, Cash Flow) + get_company_financials + get_realtime_quote for…
  2. Build: write a self-contained Python script using openpyxl that creates the DCF workbook (projections, WACC, terminal value, sensitivity…
  3. Validate: run LibreOffice recalc; audit hardcoded_count == 0 for tagged cells per ## Validation Gates; verify projection horizon ≥ 5…
  4. Output: write the artifact path per ## Output File. (Optional) render an executive-summary .pptx via Bash+python-pptx; convert .xlsx → PDF…
  5. Next: append to agentii.md; hand off to a downstream pitch/review skill if requested.

What it can do on your machine

Read from SKILL.md and the folder at commit 86980e1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • agentii.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dcf loads about 2.1k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 60 tokens; SKILL.md has 876 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~60
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~10k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from agentii-ai/agentii-investment-intelligence at commit 86980e1, republished under its Apache-2.0 licence (© agentii-ai). 876 words, ~2,087 tokens.

Download SKILL.mdSave it as .claude/skills/dcf/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
dcf
description
DCF valuation model, discounted cash flow, intrinsic value, WACC calculation, terminal value, free cash flow projection, equity value per share, DCF sensitivity analysis, unlevered free cash flow, present value calculation, build a DCF
multi_ticker_semantics
target_with_optional_peers
temporal_scope.default_quarters
4
temporal_scope.max_quarters
12
temporal_scope.description
Typical lookback: 4 quarters, max: 12
retrieval_scope
unstructured_document_search
min_tool_diversity
5

Preflight

Run canonical pre-flight per contracts/preflight.md.

Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md — carry the _run_id from your first tool result and name yourself (and your parent, if you were spawned).

Triggers

  • analyze dcf model
  • run dcf model analysis
  • produce dcf model report
  • dcf model breakdown
  • dcf model deep dive
  • build a dcf model
  • assess dcf model
  • quantify dcf model
  • compare dcf model across peers
  • review dcf model for
  • generate dcf model on
  • dcf model for investment decision

Defaults

ParameterDefaultNotes
lookback_years3Historical data window
include_peersfalseWhether to surface a peer comparison block

Methodology

Retrieval Scope

This skill performs unstructured document search at scale across SEC filings and earnings call transcripts (10-K, 10-Q, 8-K). The three-layer agent-use-ready retrieval protocol (Document Discovery → Page Map → Deep Read) applies to all unstructured document search at scale.

Retrieval Strategy

See contracts/retrieval.md for the canonical decision tree; skill-specific retrieval detail is in references/methodology.md.

Temporal Scope

Default: 12 fiscal quarters (max 20). Financial modeling: trailing 12 quarters (3 fiscal years) for long-range projection inputs.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

Step-by-step execution detail is in references/methodology.md.

Deliverable Chain

Inputs → Build → Validate → Output → Next

  1. Inputs: resolved ticker + search_xbrl_facts (Income Statement, Balance Sheet, Cash Flow) + get_company_financials + get_realtime_quote for current price.
  2. Build: write a self-contained Python script using openpyxl that creates the DCF workbook (projections, WACC, terminal value, sensitivity tables) per ## Output Structure. Execute via Bash: python3 script.py. Verify the .xlsx exists. If import openpyxl fails, fall back to .md summary with data_availability: degraded (see contracts/office-tooling.md).
  3. Validate: run LibreOffice recalc; audit hardcoded_count == 0 for tagged cells per ## Validation Gates; verify projection horizon ≥ 5 years, terminal growth < risk-free proxy.
  4. Output: write the artifact path per ## Output File. (Optional) render an executive-summary .pptx via Bash+python-pptx; convert .xlsx → PDF via LibreOffice.
  5. Next: append to agentii.md; hand off to a downstream pitch/review skill if requested.

Validation Gates

  1. projection horizon: ≥ 5 years (10 years for secular-trends analysis). If failed: If < 5 years: refuse delivery, report actual horizon.
  2. terminal growth rate: < risk-free rate proxy (current 10Y UST). If failed: If terminal_g ≥ rf: flag in assumptions section, note conservatism violation.
  3. WACC components: WACC = (E/V × Ke) + (D/V × Kd × (1-T) with all components cited to source data. For risk-free rate selection, ERP triangulation, beta de-levering/re-levering, industry betas, and cost of debt estimation, consult references/cost-of-capital-methodology.md. If failed: If components uncited: refuse delivery, list missing citations.
  4. hardcoded_count: == 0 for all cells tagged projection|margin|discount_factor|pv|sensitivity per xlsx_audit output. If failed: If hardcoded_count > 0: per the hardcode gate, refuse delivery. Bounce back to analytical-subagent ONCE with audit report.
  5. **calculation arc cross-validation **: cross-statement balancing verified against gold.xbrl_calculations weights — the DCF free-cash-flow projection and income statement structure MUST align with the filer's reported concept hierarchy. Call get_statement_structure(accession_number) (resolve the accession_number first). Flag discrepancies ≥1% as audit findings. If failed: If material discrepancy (≥1%): flag in audit findings, refuse delivery for discrepancies ≥5%. Tool-diversity is also tracked here: distinct MCP tools used MUST be ≥ min_tool_diversity (5); below that, flag as depth-insufficient in Coverage Gaps (a quality signal, not a delivery blocker).

Tool Fallbacks

Per-tool failure modes and fallback actions are tabulated in references/tool-fallbacks.md.

Show full SKILL.md (353 more words)Show less

Output File

Write the final deliverable to {ticker}/{YYYY-MM-DD_HHMM}_dcf_{affix}.md .

Output Structure

The deliverable is a structured markdown report written to the path in ## Output File. Full section-by-section template (headings, tables, and field definitions) lives in references/output-structure.md. Required elements:

  1. Executive Summary — headline conclusions (≤200 words).
  2. Core analysis sections — per this skill's methodology and analyst modes.
  3. Data classification — tag findings [FACT] / [DEDUCTED] / [VIEW] per contracts/snapshot-synthesis.md.
  4. Coverage Gaps & Citations — inline /v/ citations are PRIMARY (immediately after each fact); the bottom Citations section is a non-duplicative roll-up index.
  5. Output frontmatter — emit the FR-090 structured block per contracts/output-frontmatter-schema.md.

Citations & memory: follow contracts/citation-and-memory.md — ≥1 citation per 200 words; every material fact, table row, and metric is immediately followed by its inline clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link; a bottom Citations section provides a non-duplicative roll-up index; the closing TUI reply includes a compact Key Citations list (headline 5–10 facts) of clickable /v/ URLs; and append the run to agentii.md per contracts/agentii-md-schema.md.

Memory & Snapshot

  • Memory load (pre-flight): load prior workspace context for the ticker before retrieval — see contracts/memory-load.md.
  • Structured output frontmatter: emit the FR-090 block (key_metrics, conclusions, facts_count, deducted_count, views_count, citation_count) per contracts/output-frontmatter-schema.md.
  • Snapshot synthesis: after writing the deliverable, update the two-tier snapshot and classify findings as [FACT]/[DEDUCTED]/[VIEW] — see contracts/snapshot-synthesis.md.
  • Session archival: record the run under sessions/{YYYY-MM-DD}/ and update sessions/INDEX.md per contracts/session-format.md.

Final Summary (TUI)

End the closing chat reply with a compact Key Citations list (headline 5–10 facts), each a clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link, so the user can cmd+click straight to the exact SEC page. See contracts/citation-and-memory.md.

Error Handling

Failure ModeDetectionActionUser-Facing Message
Missing dataData API returns empty result setWiden date range and retry once"No data available for {ticker} in requested window."
Partial dataData API returns <80% expected recordsProceed with coverage gaps section"Analysis based on partial data; see Coverage Gaps section."
Sector mismatchPeer sector != target sectorFilter out mismatched peers"Removed {n} peer(s) due to sector mismatch."
Insufficient historyTicker <3 years on public marketsDowngrade to limited-history profile"Limited historical data; analysis adjusted accordingly."
MCP unreachablePreflight probe failsHalt with actionable error"agentii data plane unreachable; check connection."

© agentii-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (references) in plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf of agentii-ai/agentii-investment-intelligence.

  • SKILL.md
  • references/cost-of-capital-methodology.md
  • references/formula-sheet.md
  • references/institutional-defaults.md
  • references/methodology.md
  • references/modes.md
  • references/output-structure.md
  • references/tool-fallbacks.md
  • references/validation-checklist.md
  • references/wsp-methodology.md

Open the folder on GitHubat commit 86980e1

Compare with similar skills

Dcf next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dcf compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Dcf this skillagentii-ai/agentii-investment-intelligence207—~2.1kAutomated safety check: PassApache-2.0
Creating Financial ModelsChen-zexi/open-ptc-agent7294 repos~1.3kAutomated safety check: PassMIT
Equity Researcherlzwme/finance-quant-skills4351 repos~4.9kAutomated safety check: PassNone
Equity ResearchrollingSirius/equity-research-skill452—~1.5kAutomated safety check: PassMIT
SaaS Metrics Coachrongxinzy/RongxinAI1542 repos~1.3kAutomated safety check: PassMIT
Startup Financial Modelingnicepkg/auto-company19212 repos~2.8kAutomated safety check: PassNone

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Questions about Dcf

What does Dcf do?

DCF valuation model, discounted cash flow, intrinsic value, WACC calculation, terminal value, free cash flow projection, equity value per share, DCF sensitivity analysis, unlevered free cash flow…. Dcf is an agent skill from agentii-ai/agentii-investment-intelligence.

When should I use Dcf?

Dcf fits situations like: tasks that involve Financial modeling.

How do I install Dcf in Claude Code?

Run `npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a claude-code`. Or copy the skill folder (plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf in agentii-ai/agentii-investment-intelligence) into .claude/skills/dcf in your project. Claude Code loads it when a task matches its description.

How do I install Dcf in Codex?

Run `npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a codex`. Or copy the skill folder (plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf in agentii-ai/agentii-investment-intelligence) into .agents/skills/dcf in your project. Codex loads it when a task matches its description.

Can I use Dcf in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dcf, .gemini/skills/dcf, .github/skills/dcf and .opencode/skills/dcf in your project.

What does Dcf need to run?

SKILL.md names no scripts, command-line tools or credentials: Dcf is instructions for the agent only. Our summary lists: Python 3.

Does Dcf access the network?

SKILL.md names 1 domain. In commands or code: agentii.ai; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Dcf safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Dcf use?

Dcf is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dcf use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8k tokens, read only when the agent opens those files.

What are the alternatives to Dcf?

Skills that share tags, products or a category with Dcf: Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars), Equity Researcher (lzwme/finance-quant-skills, 435 stars), Equity Research (rollingSirius/equity-research-skill, 452 stars) and SaaS Metrics Coach (rongxinzy/RongxinAI, 154 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dcf?

agentii-ai (a GitHub user) maintains it in agentii-ai/agentii-investment-intelligence, which has 207 GitHub stars. The repository holds 79 skills in this directory. The repository was last updated on September 29, 2026.

Source: agentii-ai/agentii-investment-intelligence on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.