Tradingview MCP
atilaahmettaner/tradingview-mcp
AI Trading Intelligence — live prices, 30+ technical indicators, backtesting (6 strategies), walk-forward overfitting detection, trade logs, equity curves, licensed news sentiment (Marketaux), and…
Retrieve historical market capitalization data for any stock using Octagon MCP.
Install Historical Market Cap
$ npx skills add OctagonAI/skills --skill historical-market-cap -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install OctagonAI/skills historical-market-cap --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/OctagonAI/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/historical-market-cap .claude/skills/historical-market-cap && 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 "historical-market-cap" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/historical-market-cap into .claude/skills/historical-market-cap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "historical-market-cap", 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/OctagonAI/skills/tree/main/skills/historical-market-capType 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 OctagonAI/skills --skill historical-market-cap -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install OctagonAI/skills historical-market-cap --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/historical-market-cap .agents/skills/historical-market-cap && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "historical-market-cap" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/historical-market-cap into .agents/skills/historical-market-cap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "historical-market-cap", 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 OctagonAI/skills --skill historical-market-cap -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install OctagonAI/skills historical-market-cap --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/historical-market-cap .cursor/skills/historical-market-cap && 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 "historical-market-cap" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/historical-market-cap into .cursor/skills/historical-market-cap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "historical-market-cap", 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/OctagonAI/skills.git --path skills/historical-market-cap--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 OctagonAI/skills --skill historical-market-cap -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install OctagonAI/skills historical-market-cap --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/historical-market-cap .gemini/skills/historical-market-cap && 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 "historical-market-cap" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/historical-market-cap into .gemini/skills/historical-market-cap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "historical-market-cap", 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 OctagonAI/skills historical-market-capInstalls 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 OctagonAI/skills --skill historical-market-cap -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/historical-market-cap .github/skills/historical-market-cap && 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 "historical-market-cap" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/historical-market-cap into .github/skills/historical-market-cap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "historical-market-cap", 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 OctagonAI/skills --skill historical-market-cap -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install OctagonAI/skills historical-market-cap --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/historical-market-cap .opencode/skills/historical-market-cap && 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 "historical-market-cap" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/historical-market-cap into .opencode/skills/historical-market-cap/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "historical-market-cap", 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.
Facts
- Skill name
historical-market-cap- GitHub stars
- 127
- Token cost
- ~1.8k tokens
- SKILL.md length
- 580 words
- Files
- 5 (incl. references)
- Skills in repo
- 53
- Repo updated
- First seen
- Licence
- MIT
At a glance
Retrieve historical market capitalization data for any stock using Octagon MCP.
- Works in 4 steps: Identify Parameters → Execute Query via Octagon MCP → Expected Output → …
- Tracking market cap changes over time
- SKILL.md covers Prerequisites, Workflow, Example Queries and Understanding Market Cap History, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Historical Market Cap is an agent skill from OctagonAI/skills. Retrieve historical market capitalization data for any stock using Octagon MCP. Use when tracking market cap changes over time, analyzing valuation trends, identifying peak and trough valuations, and comparing historical size classifications.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `README.md`, `marketplace.json` and `references/interpreting-results.md`).
It sits in Business, Finance & HR, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: A collection of Claude skills for agentic financial research by Octagon. The licence is MIT.
When your agent uses it
- Tracking market cap changes over time
- Analyzing valuation trends
- Identifying peak and trough valuations
- Comparing historical size classifications
Example prompts
- “/historical-market-cap”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 51e938c. 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 (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Historical Market Cap loads about 1.8k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 580 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.
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 OctagonAI/skills at commit 51e938c, republished under its MIT licence (© OctagonAI). 580 words, ~1,788 tokens.
Download SKILL.mdSave it as
.claude/skills/historical-market-cap/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- historical-market-cap
- description
- Retrieve historical market capitalization data for any stock using Octagon MCP. Use when tracking market cap changes over time, analyzing valuation trends, identifying peak and trough valuations, and comparing historical size classifications.
Historical Market Cap
Retrieve historical market capitalization data over a specified date range using the Octagon MCP server.
Prerequisites
Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See references/mcp-setup.md for installation instructions.
Workflow
1. Identify Parameters
Determine your query parameters:
- Ticker: Stock symbol (e.g., AAPL, MSFT)
- Start Date: Beginning of date range
- End Date: End of date range
- Limit (optional): Maximum records to return
2. Execute Query via Octagon MCP
Use the octagon-agent tool with a natural language prompt:
Retrieve historical market capitalization data for <TICKER> from <START_DATE> to <END_DATE>, limited to <LIMIT> records.MCP Call Format:
{
"server": "octagon-mcp",
"toolName": "octagon-agent",
"arguments": {
"prompt": "Retrieve historical market capitalization data for AAPL from 2025-01-01 to 2025-04-30, limited to 1000 records."
}
}3. Expected Output
The agent returns daily market cap values:
| Date | Market Cap (USD) |
|---|---|
| 2025-04-30 | $3.17 trillion |
| 2025-02-25 | $3.70 trillion (High) |
| 2025-04-08 | $2.57 trillion (Low) |
| ... | ... |
Summary Statistics:
- Highest: $3.70 trillion on 2025-02-25
- Lowest: $2.57 trillion on 2025-04-08
- Most Recent: $3.17 trillion on 2025-04-30
Data Sources: octagon-stock-data-agent
4. Interpret Results
See references/interpreting-results.md for guidance on:
- Analyzing market cap trends
- Calculating growth rates
- Identifying peaks and troughs
- Understanding volatility
Example Queries
Standard Date Range:
Retrieve historical market capitalization data for AAPL from 2025-01-01 to 2025-04-30, limited to 1000 records.Full Year:
Get historical market cap for MSFT for the entire year 2024.Quarterly Analysis:
Show TSLA's market cap history for Q1 2025.Multi-Year Trend:
Retrieve market cap history for NVDA from 2020 to 2025.Peak Analysis:
When did AAPL reach its highest market cap in 2024?Understanding Market Cap History
What the Data Shows
| Metric | Description |
|---|---|
| Daily Market Cap | End-of-day value |
| Date Series | Trading days only |
| Calculation | Price × Shares Outstanding |
| Adjustments | Split-adjusted shares |
Key Statistics
| Statistic | Purpose |
|---|---|
| Maximum | Peak valuation |
| Minimum | Trough valuation |
| Average | Typical valuation |
| Range | Volatility indicator |
Trend Analysis
Calculating Changes
| Metric | Formula |
|---|---|
| Absolute Change | End Cap - Start Cap |
| Percentage Change | (End - Start) / Start × 100% |
| CAGR | (End/Start)^(1/years) - 1 |
Example Calculation
From the AAPL data:
- High: $3.70T (Feb 25)
- Low: $2.57T (Apr 8)
- Range: $1.13T
- Peak-to-Trough: -30.5%
Trend Patterns
| Pattern | Characteristics |
|---|---|
| Uptrend | Higher highs, higher lows |
| Downtrend | Lower highs, lower lows |
| Consolidation | Range-bound |
| V-Recovery | Sharp decline, sharp recovery |
| Rounded Top | Gradual peak formation |
Period Analysis
Daily Analysis
| Use Case | Focus |
|---|---|
| Trading | Short-term moves |
| Volatility | Day-to-day changes |
| Events | Catalyst impact |
Weekly/Monthly Analysis
| Use Case | Focus |
|---|---|
| Trends | Direction over time |
| Comparisons | Period-over-period |
| Smoothing | Reduce noise |
Annual Analysis
| Use Case | Focus |
|---|---|
| Growth | Long-term trajectory |
| Milestones | Major achievements |
| CAGR | Compound growth |
Volatility Assessment
Measuring Volatility
| Metric | Calculation |
|---|---|
| Range | High - Low |
| Range % | (High - Low) / Average |
| Daily Moves | Average daily change |
| Standard Deviation | Price dispersion |
Show full SKILL.md (238 more words)Show less
Volatility Interpretation
| Range % | Volatility |
|---|---|
| <20% | Low |
| 20-40% | Moderate |
| 40-60% | High |
| >60% | Very High |
Example
From AAPL data:
- High: $3.70T
- Low: $2.57T
- Range: $1.13T
- Range %: ~35%
- Interpretation: Moderate-high volatility
Peak and Trough Analysis
Identifying Peaks
| Signal | Description |
|---|---|
| All-time High | Highest ever |
| Period High | Highest in range |
| Local Peak | Temporary high |
Identifying Troughs
| Signal | Description |
|---|---|
| All-time Low | Lowest ever |
| Period Low | Lowest in range |
| Local Trough | Temporary low |
Peak-to-Trough Metrics
| Metric | Purpose |
|---|---|
| Drawdown % | Decline from peak |
| Recovery Time | Days to recover |
| Drawdown Duration | Peak to trough time |
Size Classification Over Time
Tracking Category Changes
| If Market Cap... | Classification |
|---|---|
| >$200B | Mega-cap |
| $10B-$200B | Large-cap |
| $2B-$10B | Mid-cap |
| $300M-$2B | Small-cap |
Milestone Analysis
| Milestone | Significance |
|---|---|
| First $1T | Historic achievement |
| Crossed $2T | Elite status |
| Crossed $3T | World's most valuable |
Comparative Analysis
Same Company Over Time
| Comparison | Purpose |
|---|---|
| YoY | Year-over-year growth |
| QoQ | Quarterly momentum |
| MoM | Monthly trends |
Multiple Companies
| Comparison | Purpose |
|---|---|
| Relative Size | Market position |
| Relative Growth | Performance comparison |
| Correlation | Movement similarity |
Common Use Cases
Trend Analysis
How has AAPL's market cap changed over the past year?Peak Finding
When did TSLA reach its highest market cap?Drawdown Analysis
What was NVDA's biggest decline from peak in 2024?Milestone Tracking
When did MSFT first cross $3 trillion market cap?Comparison
Compare the market cap growth of AAPL and MSFT over 5 years.Analysis Tips
Use appropriate timeframes: Match analysis to investment horizon.
Identify catalysts: Major moves often have drivers.
Consider splits: Ensure data is split-adjusted.
Watch for milestones: Round numbers are psychologically important.
Calculate drawdowns: Understand downside risk.
Compare to benchmarks: Market cap vs. index performance.
Integration with Other Skills
| Skill | Combined Use |
|---|---|
| company-market-cap | Current vs. historical |
| stock-performance | Price driving cap changes |
| income-statement | Earnings supporting cap |
| financial-metrics-analysis | Valuation evolution |
© OctagonAI, MIT. 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 4 other files (references) in skills/historical-market-cap of OctagonAI/skills.
- SKILL.md
- README.md
- marketplace.json
- references/interpreting-results.md
- references/mcp-setup.md
Open the folder on GitHubat commit 51e938c
Compare with similar skills
Historical Market Cap 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 |
|---|---|---|---|---|---|---|
| Historical Market Cap this skillOctagonAI/skills | 127 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Tradingview MCPatilaahmettaner/tradingview-mcp | 5k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Okx Cex Marketdex-original/okx-agent-trade-kit | 110 | 1 repos | ~2.7k | Automated safety check: Pass | MIT | |
| Polymarket Tennislivetennisapi/livetennisapi-mcp | 152 | — | ~3k | Automated safety check: Pass | MIT | |
| Okx Sentiment Trackerdex-original/okx-agent-trade-kit | 110 | 1 repos | ~3.8k | Automated safety check: Pass | MIT | |
| Odoo Agency Fleet Reviewerpipe-org/mcp-odoo | 421 | — | ~699 | Automated safety check: Pass | MIT |
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Questions about Historical Market Cap
What does Historical Market Cap do?
Retrieve historical market capitalization data for any stock using Octagon MCP. Historical Market Cap is an agent skill from OctagonAI/skills. Retrieve historical market capitalization data for any stock using Octagon MCP.
When should I use Historical Market Cap?
Historical Market Cap fits situations like: tracking market cap changes over time; analyzing valuation trends; identifying peak and trough valuations; comparing historical size classifications.
How do I install Historical Market Cap in Claude Code?
Run `npx skills add OctagonAI/skills --skill historical-market-cap -a claude-code`. Or copy the skill folder (skills/historical-market-cap in OctagonAI/skills) into .claude/skills/historical-market-cap in your project. Claude Code loads it when a task matches its description.
How do I install Historical Market Cap in Codex?
Run `npx skills add OctagonAI/skills --skill historical-market-cap -a codex`. Or copy the skill folder (skills/historical-market-cap in OctagonAI/skills) into .agents/skills/historical-market-cap in your project. Codex loads it when a task matches its description.
Can I use Historical Market Cap 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 OctagonAI/skills --skill historical-market-cap -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/historical-market-cap, .gemini/skills/historical-market-cap, .github/skills/historical-market-cap and .opencode/skills/historical-market-cap in your project.
What does Historical Market Cap need to run?
SKILL.md names no scripts, command-line tools or credentials: Historical Market Cap is instructions for the agent only.
Does Historical Market Cap access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Historical Market Cap 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 Historical Market Cap use?
Historical Market Cap is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Historical Market Cap use?
About 1.8k tokens (SKILL.md is roughly 7.2k 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 2.4k tokens, read only when the agent opens those files.
What are the alternatives to Historical Market Cap?
Skills that share tags, products or a category with Historical Market Cap: Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars), Okx Cex Market (dex-original/okx-agent-trade-kit, 110 stars), Polymarket Tennis (livetennisapi/livetennisapi-mcp, 152 stars) and Okx Sentiment Tracker (dex-original/okx-agent-trade-kit, 110 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Historical Market Cap?
OctagonAI (a GitHub organization) maintains it in OctagonAI/skills, which has 127 GitHub stars. The repository holds 53 skills in this directory. The repository was last updated on June 5, 2026.
Source: OctagonAI/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.