Creating Financial Models
Chen-zexi/open-ptc-agent
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
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…
$ npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence dcf --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "dcf" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf into .claude/skills/dcf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dcf", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcfType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence dcf --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf .agents/skills/dcf && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dcf" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf into .agents/skills/dcf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dcf", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence dcf --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf .cursor/skills/dcf && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "dcf" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf into .cursor/skills/dcf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dcf", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agentii-ai/agentii-investment-intelligence.git --path plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence dcf --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf .gemini/skills/dcf && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "dcf" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf into .gemini/skills/dcf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dcf", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agentii-ai/agentii-investment-intelligence dcfInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf .github/skills/dcf && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "dcf" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf into .github/skills/dcf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dcf", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentii-ai/agentii-investment-intelligence --skill dcf -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agentii-ai/agentii-investment-intelligence dcf --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf .opencode/skills/dcf && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "dcf" agent skill from https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf into .opencode/skills/dcf/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dcf", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
dcfDCF 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. 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.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 86980e1. It shows what the files ask for, not the result of running them.
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.
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.
Hosts in commands or code, which the agent is likely to contact:
agentii.aiFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
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.
.claude/skills/dcf/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.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).
| Parameter | Default | Notes |
|---|---|---|
| lookback_years | 3 | Historical data window |
| include_peers | false | Whether to surface a peer comparison block |
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.
See contracts/retrieval.md for the canonical decision tree; skill-specific retrieval detail is in references/methodology.md.
Default: 12 fiscal quarters (max 20). Financial modeling: trailing 12 quarters (3 fiscal years) for long-range projection inputs.
See frontmatter allowed_tools.
Step-by-step execution detail is in references/methodology.md.
Inputs → Build → Validate → Output → Next
search_xbrl_facts (Income Statement, Balance Sheet, Cash Flow) + get_company_financials + get_realtime_quote for current price.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).hardcoded_count == 0 for tagged cells per ## Validation Gates; verify projection horizon ≥ 5 years, terminal growth < risk-free proxy.## Output File. (Optional) render an executive-summary .pptx via Bash+python-pptx; convert .xlsx → PDF via LibreOffice.agentii.md; hand off to a downstream pitch/review skill if requested.references/cost-of-capital-methodology.md. If failed: If components uncited: refuse delivery, list missing citations.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).Per-tool failure modes and fallback actions are tabulated in references/tool-fallbacks.md.
Write the final deliverable to {ticker}/{YYYY-MM-DD_HHMM}_dcf_{affix}.md .
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:
[FACT] / [DEDUCTED] / [VIEW] per contracts/snapshot-synthesis.md./v/ citations are PRIMARY (immediately after each fact); the bottom Citations section is a non-duplicative roll-up index.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.
contracts/memory-load.md.key_metrics, conclusions, facts_count, deducted_count, views_count, citation_count) per contracts/output-frontmatter-schema.md.[FACT]/[DEDUCTED]/[VIEW] — see contracts/snapshot-synthesis.md.sessions/{YYYY-MM-DD}/ and update sessions/INDEX.md per contracts/session-format.md.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.
| Failure Mode | Detection | Action | User-Facing Message |
|---|---|---|---|
| Missing data | Data API returns empty result set | Widen date range and retry once | "No data available for {ticker} in requested window." |
| Partial data | Data API returns <80% expected records | Proceed with coverage gaps section | "Analysis based on partial data; see Coverage Gaps section." |
| Sector mismatch | Peer sector != target sector | Filter out mismatched peers | "Removed {n} peer(s) due to sector mismatch." |
| Insufficient history | Ticker <3 years on public markets | Downgrade to limited-history profile | "Limited historical data; analysis adjusted accordingly." |
| MCP unreachable | Preflight probe fails | Halt 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
SKILL.md and 9 other files (references) in plugins/vertical-plugins/models-and-pitches/skills/agentii/dcf of agentii-ai/agentii-investment-intelligence.
Open the folder on GitHubat commit 86980e1
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Dcf this skillagentii-ai/agentii-investment-intelligence | 207 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Creating Financial ModelsChen-zexi/open-ptc-agent | 729 | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Equity Researcherlzwme/finance-quant-skills | 435 | 1 repos | ~4.9k | Automated safety check: Pass | None | |
| Equity ResearchrollingSirius/equity-research-skill | 452 | — | ~1.5k | Automated safety check: Pass | MIT | |
| SaaS Metrics Coachrongxinzy/RongxinAI | 154 | 2 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Startup Financial Modelingnicepkg/auto-company | 192 | 12 repos | ~2.8k | Automated safety check: Pass | None |
Chen-zexi/open-ptc-agent
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
lzwme/finance-quant-skills
机构级投研报告生成技能。覆盖中国A股、港股、美股上市公司。输出模式:1)投资速览:3-5页,含公司概览、核心财务指标、估值倍数、投资逻辑与风险因素;2)深度研报:≥25页,含行业分析、产业链图谱、三表模型、DCF估值、情景分析与敏感性测试。触发条件:1)直接请求:分析/看看/研究/调研/介绍一下 + 公司名或股票;2)投资询问:你怎么看/能不能买/值得投吗/帮我看看/扒一扒 +…
rollingSirius/equity-research-skill
撰写机构级个股投资研究报告(二级市场深度研究)。Use whenever the user wants to research, analyze, or value a specific publicly-traded stock — e.g.
rongxinzy/RongxinAI
SaaS financial health advisor. An agent skill from rongxinzy/RongxinAI.
nicepkg/auto-company
This skill should be used when the user asks to "create financial projections", "build a financial model", "forecast revenue", "calculate burn rate", "estimate runway", "model cash flow", or…
FunnyKun/stock-value-analyzer
基于邱国鹭《投资中最简单的事》方法论的股票价值分析器(v2.0 双层架构)。通过"三好原则"(好行业、好公司、好价格)系统评估一只股票是否值得投资。v2.0 在原定性框架之上注入一套可量化、可复现的硬模型层——反向 DCF 反解市场隐含增速、情景概率加权估值、EPV 盈利能力价值、分行业估值路由、杜邦三/五因子分解、ROIC vs WACC、Piotroski F-Score、Beneish…
agentii-ai/agentii-investment-intelligence
The research-domain clarification skill — find underspecified items in a thesis spec.md (prose wrongif, universe rows without rationale, missing budget/expiry/pins, ambiguous pillars), ask the human…
agentii-ai/agentii-investment-intelligence
Scaffold and amend the L1 Investment Constitution — [ALLCAPS] placeholder bootstrap, SemVer bump rules, Sync Impact Report, MINOR/MAJOR re-examination dispatch after the gate-5 budget confirm.
agentii-ai/agentii-investment-intelligence
Execute research tasks — checklist soft gate, phase dispatch with explicit thesisdir, budget enforcement (halt + approval card on overrun), skillpin recording (versionhash content-hashed per skill…
agentii-ai/agentii-investment-intelligence
Decompose the plan into research tasks — one task per ticker × skill × mode, grouped by pillar, [P]-marked by the different-files-and-no-incomplete-deps rule, with source-refs.
agentii-ai/agentii-investment-intelligence
Adversarial verification of research theses — cross-run/cross-thesis contradiction via the entity index, pre-mortem (Klarman/Kahneman: assume the loss already happened, reverse the path), inversion…
agentii-ai/agentii-investment-intelligence
Chart pattern recognition, price action patterns, candlestick signal bars, pullback bar counting H1/H2/H3/H4, trend channels, micro channels, trading ranges, breakouts, major trend reversals 5-step…
Categories
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.
Dcf fits situations like: tasks that involve Financial modeling.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Dcf is instructions for the agent only. Our summary lists: Python 3.
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.
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.
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.
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.
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.
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.