MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Retrieve consensus price targets for any stock using Octagon MCP.
MITAuto-check passed
Install Price Target Consensus
$ npx skills add OctagonAI/skills --skill price-target-consensus -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install OctagonAI/skills price-target-consensus --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/price-target-consensus .claude/skills/price-target-consensus && 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 "price-target-consensus" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/price-target-consensus into .claude/skills/price-target-consensus/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "price-target-consensus", 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/price-target-consensusType 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 price-target-consensus -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install OctagonAI/skills price-target-consensus --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/price-target-consensus .agents/skills/price-target-consensus && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "price-target-consensus" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/price-target-consensus into .agents/skills/price-target-consensus/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "price-target-consensus", 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 price-target-consensus -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install OctagonAI/skills price-target-consensus --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/price-target-consensus .cursor/skills/price-target-consensus && 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 "price-target-consensus" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/price-target-consensus into .cursor/skills/price-target-consensus/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "price-target-consensus", 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/price-target-consensus--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 price-target-consensus -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install OctagonAI/skills price-target-consensus --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/price-target-consensus .gemini/skills/price-target-consensus && 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 "price-target-consensus" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/price-target-consensus into .gemini/skills/price-target-consensus/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "price-target-consensus", 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 price-target-consensusInstalls 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 price-target-consensus -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/price-target-consensus .github/skills/price-target-consensus && 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 "price-target-consensus" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/price-target-consensus into .github/skills/price-target-consensus/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "price-target-consensus", 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 price-target-consensus -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 price-target-consensus --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/price-target-consensus .opencode/skills/price-target-consensus && 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 "price-target-consensus" agent skill from https://github.com/OctagonAI/skills/tree/main/skills/price-target-consensus into .opencode/skills/price-target-consensus/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "price-target-consensus", 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
price-target-consensus- GitHub stars
- 127
- Token cost
- ~1.8k tokens
- SKILL.md length
- 567 words
- Files
- 5 (incl. references)
- Skills in repo
- 53
- Repo updated
- First seen
- Licence
- MIT
At a glance
Retrieve consensus price targets for any stock using Octagon MCP.
- Works in 4 steps: Identify the Stock → Execute Query via Octagon MCP → Expected Output → …
- You need the average
- SKILL.md covers Prerequisites, Workflow, Example Queries and Understanding the Metrics, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Price Target Consensus is an agent skill from OctagonAI/skills. Retrieve consensus price targets for any stock using Octagon MCP. Use when you need the average, median, high, and low analyst price targets to evaluate upside/downside potential and analyst agreement.
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 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
- You need the average
- Low analyst price targets to evaluate upside/downside potential and analyst agreement
Example prompts
- “/price-target-consensus”
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
Price Target Consensus loads about 1.8k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 567 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). 567 words, ~1,751 tokens.
Download SKILL.mdSave it as
.claude/skills/price-target-consensus/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- price-target-consensus
- description
- Retrieve consensus price targets for any stock using Octagon MCP. Use when you need the average, median, high, and low analyst price targets to evaluate upside/downside potential and analyst agreement.
Price Target Consensus
Retrieve consensus price target metrics including average, median, high, and low targets 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 the Stock
Determine the ticker symbol for the company you want to analyze (e.g., AAPL, MSFT, GOOGL).
2. Execute Query via Octagon MCP
Use the octagon-agent tool with a natural language prompt:
Retrieve consensus price targets for the stock symbol <TICKER>.MCP Call Format:
{
"server": "octagon-mcp",
"toolName": "octagon-agent",
"arguments": {
"prompt": "Retrieve consensus price targets for the stock symbol AAPL."
}
}3. Expected Output
The agent returns consensus price target data:
| Metric | Value |
|---|---|
| Consensus Target | $303.11 |
| Median Target | $315.00 |
| Target High | $350.00 |
| Target Low | $220.00 |
Data Sources: octagon-stock-data-agent
4. Interpret Results
See references/interpreting-results.md for guidance on:
- Understanding consensus vs. median
- Analyzing the target range
- Calculating upside/downside
- Evaluating analyst agreement
Example Queries
Basic Query:
Retrieve consensus price targets for the stock symbol AAPL.With Price Context:
What is the consensus price target for TSLA and how does it compare to current price?Range Focus:
What are the highest and lowest analyst price targets for NVDA?Comparison:
Compare consensus price targets for AAPL, MSFT, and GOOGL.Upside Analysis:
What upside does the consensus target imply for AMZN?Understanding the Metrics
Consensus Target
| Aspect | Description |
|---|---|
| Definition | Average of all analyst targets |
| Calculation | Sum of targets / Number of analysts |
| Use | General market expectation |
| Limitation | Skewed by outliers |
Median Target
| Aspect | Description |
|---|---|
| Definition | Middle value of all targets |
| Calculation | 50th percentile |
| Use | Central tendency, outlier-resistant |
| Advantage | Less affected by extremes |
Target High
| Aspect | Description |
|---|---|
| Definition | Most bullish analyst target |
| Represents | Best-case scenario |
| Use | Maximum upside potential |
| Caution | May be overly optimistic |
Target Low
| Aspect | Description |
|---|---|
| Definition | Most bearish analyst target |
| Represents | Worst-case scenario |
| Use | Downside risk assessment |
| Caution | May be overly pessimistic |
Calculating Potential
Upside/Downside Formulas
Consensus Upside = (Consensus Target - Current Price) / Current Price × 100%
Maximum Upside = (Target High - Current Price) / Current Price × 100%
Downside Risk = (Target Low - Current Price) / Current Price × 100%Example Calculations
If AAPL trades at $270.01:
| Metric | Target | Potential |
|---|---|---|
| Consensus | $303.11 | +12.3% upside |
| Median | $315.00 | +16.7% upside |
| High | $350.00 | +29.6% upside |
| Low | $220.00 | -18.5% downside |
Range Analysis
Spread Calculation
Range = Target High - Target Low
Spread % = Range / Consensus Target × 100%Interpreting Spread
| Spread % | Interpretation |
|---|---|
| <20% | Strong consensus |
| 20-40% | Normal range |
| 40-60% | Moderate disagreement |
| >60% | High uncertainty |
Example Range Analysis
From AAPL data:
- High: $350.00
- Low: $220.00
- Range: $130.00
- Consensus: $303.11
- Spread: 42.9%
Interpretation: Moderate disagreement among analysts, with significant difference between bulls and bears.
Consensus vs. Median
When to Use Each
| Scenario | Prefer |
|---|---|
| Normal distribution | Consensus (average) |
| Outliers present | Median |
| Skewed targets | Median |
| General expectation | Consensus |
Identifying Skew
| Condition | Indicates |
|---|---|
| Consensus > Median | Right skew (bullish outliers) |
| Consensus < Median | Left skew (bearish outliers) |
| Consensus ≈ Median | Symmetric distribution |
Show full SKILL.md (217 more words)Show less
Example
From AAPL data:
- Consensus: $303.11
- Median: $315.00
- Consensus < Median → Left skew (some bearish outliers pulling average down)
Bull vs. Bear Cases
Understanding Extremes
| Target | Represents |
|---|---|
| High | Bull case assumptions |
| Low | Bear case assumptions |
| Gap | Range of outcomes |
Scenario Analysis
| Scenario | Assumptions |
|---|---|
| Bull Case | Strong growth, expanding margins, favorable macro |
| Base Case | Consensus expectations |
| Bear Case | Challenges, competition, risks materialize |
Practical Applications
Investment Decision
| Finding | Consideration |
|---|---|
| Price < Low Target | Potential deep value or concerns |
| Price near Consensus | Fairly valued |
| Price > High Target | Potentially overvalued |
Risk Assessment
| Metric | Use For |
|---|---|
| Downside to Low | Worst-case loss |
| Upside to High | Best-case gain |
| Risk/Reward | Low upside / High downside |
Position Sizing
| Consensus View | Position Approach |
|---|---|
| Strong upside, tight range | Larger position |
| Moderate upside, wide range | Standard position |
| Limited upside, wide range | Smaller position |
Common Use Cases
Quick Valuation Check
Is AAPL fairly valued based on analyst targets?Upside Screening
Which tech stocks have the highest consensus upside?Risk Assessment
What's the downside risk to the lowest analyst target for TSLA?Sentiment Check
How wide is the range between bull and bear cases for NVDA?Analysis Tips
Compare to current price: Calculate actual upside/downside.
Use median when skewed: More reliable central tendency.
Analyze the range: Wide = uncertainty, tight = agreement.
Consider timing: Targets are typically 12-month forward.
Track changes: Rising consensus = improving sentiment.
Combine with fundamentals: Targets are opinions, verify with data.
Integration with Other Skills
| Skill | Combined Use |
|---|---|
| stock-quote | Current price for potential calculation |
| price-target-summary | Historical target trends |
| analyst-estimates | Earnings behind the targets |
| financial-metrics-analysis | Fundamental validation |
© 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/price-target-consensus 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
Price Target Consensus 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 |
|---|---|---|---|---|---|---|
| Price Target Consensus this skillOctagonAI/skills | 127 | — | ~1.8k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 63 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 4 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| MCP Developmentcoollabsio/coolify | 63k | 1 repos | ~949 | Automated safety check: Pass | MIT | |
| Analyze Logsactivepieces/activepieces | 25k | 1 repos | ~1.6k | Automated safety check: Pass | MIT |
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Questions about Price Target Consensus
What does Price Target Consensus do?
Retrieve consensus price targets for any stock using Octagon MCP. Price Target Consensus is an agent skill from OctagonAI/skills. Retrieve consensus price targets for any stock using Octagon MCP.
When should I use Price Target Consensus?
Price Target Consensus fits situations like: you need the average; low analyst price targets to evaluate upside/downside potential and analyst agreement.
How do I install Price Target Consensus in Claude Code?
Run `npx skills add OctagonAI/skills --skill price-target-consensus -a claude-code`. Or copy the skill folder (skills/price-target-consensus in OctagonAI/skills) into .claude/skills/price-target-consensus in your project. Claude Code loads it when a task matches its description.
How do I install Price Target Consensus in Codex?
Run `npx skills add OctagonAI/skills --skill price-target-consensus -a codex`. Or copy the skill folder (skills/price-target-consensus in OctagonAI/skills) into .agents/skills/price-target-consensus in your project. Codex loads it when a task matches its description.
Can I use Price Target Consensus 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 price-target-consensus -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/price-target-consensus, .gemini/skills/price-target-consensus, .github/skills/price-target-consensus and .opencode/skills/price-target-consensus in your project.
What does Price Target Consensus need to run?
SKILL.md names no scripts, command-line tools or credentials: Price Target Consensus is instructions for the agent only.
Does Price Target Consensus 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 Price Target Consensus 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 Price Target Consensus use?
Price Target Consensus 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 Price Target Consensus use?
About 1.8k tokens (SKILL.md is roughly 7k 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 Price Target Consensus?
Skills that share tags, products or a category with Price Target Consensus: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and MCP Development (coollabsio/coolify, 63k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Price Target Consensus?
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.