Automated summary
Build an interactive web dashboard for technical indicator analysis using Plotly Dash or Streamlit.
Strengths
- Supports multiple dashboard types (single‑symbol, multi‑symbol, multi‑timeframe, scanner)
- Built‑in dark theme and selectors for symbol, exchange, and interval
- Real‑time auto‑refresh with dcc.Interval or st.rerun()
- Uses predefined templates to accelerate development
Limitations
- Requires prior setup of dependency rules
- Dashboards are not automatically deployed
- Limited to basic technical indicators without advanced customization
Use this skill when you need a visual, interactive technical analysis dashboard for one or more stock symbols with common indicators.
Avoid this skill if you need complex quantitative analysis or an automated trading system without a user interface.
Security analysis
SafeThe skill creates a local web dashboard for technical analysis using Plotly Dash or Streamlit. It involves file creation, running a local Python server, and loading environment variables from .env, but there are no instructions for data exfiltration, system destruction, or any dangerous operations. The Bash usage is limited to starting the application locally, which is standard and safe.
No concerns found
How this analysis is madeExamples
Build a single symbol dashboard for SBIN using Dash with EMA and RSI indicators.Create a multi-timeframe Streamlit dashboard for RELIANCE with 5m, 15m, 1h, and D timeframes.Create a scanner-dashboard type in Dash for a watchlist of 10+ NSE symbols.name: indicator-dashboard description: Build a web dashboard for technical indicator analysis using Plotly Dash or Streamlit. Supports single-symbol, multi-symbol, and multi-timeframe layouts with real-time refresh. argument-hint: "[type] [symbol]" allowed-tools: Read, Write, Edit, Bash, Glob, Grep, AskUserQuestion
Create a web dashboard for interactive technical analysis using Plotly Dash or Streamlit.
Arguments
Parse $ARGUMENTS as: type symbol
$0= dashboard type. Default: single- Dash types:
single,multi-symbol,multi-timeframe,scanner-dashboard - Streamlit types:
streamlit-single,streamlit-multi,streamlit-scanner
- Dash types:
$1= symbol (e.g., SBIN, RELIANCE). Default: SBIN
If no arguments, ask the user what kind of dashboard they want and whether they prefer Dash or Streamlit.
Instructions
- Read the indicator-expert rules, especially:
rules/dashboard-patterns.md— Dash app patternsrules/streamlit-patterns.md— Streamlit app patternsrules/plotting.md— Chart patternsrules/data-fetching.md— Data loading
- Create
dashboards/{dashboard_name}/directory (on-demand) - Create
app.pyindashboards/{dashboard_name}/ - Use the matching template from
rules/assets/
Dashboard Requirements
All dashboards must include:
- Dark theme: Dash uses
dbc.themes.DARKLY; Streamlit uses[theme] base = "dark"or CSS injection - Symbol input: Text input or dropdown for symbol selection
- Exchange selector: NSE, BSE, NFO, NSE_INDEX
- Interval selector: 1m, 5m, 15m, 1h, D
- Indicator selectors: Checkboxes/multiselect for overlay and subplot indicators
- Interactive chart: Plotly chart with
template="plotly_dark",xaxis_type="category" - Stats display: Key metrics (LTP, Change, Volume, indicator values)
- Auto-refresh: Dash uses
dcc.Interval; Streamlit usesst.rerun()withtime.sleep() - Load
.envfrom project root viafind_dotenv()
Dash Dashboard Types
single — Single Symbol Dashboard (Dash)
- One symbol with configurable indicators
- Overlays: EMA, SMA, Bollinger, Supertrend, Ichimoku (checkboxes)
- Subplots: RSI, MACD, Stochastic, Volume, ADX, OBV (checkboxes)
- Stats panel: LTP, day change, volume, selected indicator values
- Template:
rules/assets/dashboard_basic/app.py
multi-symbol — Multi-Symbol Watchlist (Dash)
- 4-6 symbols in a grid layout
- Each cell shows candlestick + one overlay indicator
- Bottom row: RSI comparison across all symbols
- Symbol list editable via input
multi-timeframe — MTF Analysis (Dash)
- 4-panel grid: 5m, 15m, 1h, D for same symbol
- Same indicators computed on each timeframe
- Confluence summary: "3/4 timeframes bullish"
- Template:
rules/assets/dashboard_multi/app.py
scanner-dashboard — Live Scanner (Dash)
- Watchlist of 10+ symbols
- Table showing: Symbol, LTP, RSI, EMA trend, Signal
- Color-coded rows (green=bullish, red=bearish)
- Click symbol to show detailed chart
- Auto-refresh every 30 seconds
Streamlit Dashboard Types
streamlit-single — Single Symbol Dashboard (Streamlit)
- Sidebar: symbol, exchange, interval, overlay/subplot multiselect
st.plotly_chart()for interactive chartsst.metric()for LTP, Change, RSI, EMA stats- Auto-refresh via checkbox +
st.rerun() - Template:
rules/assets/streamlit_basic/app.py
streamlit-multi — MTF Analysis (Streamlit)
- 2x2 grid via
st.columns(2)for 4 timeframes - Candlestick + EMA overlay per timeframe
- Confluence summary with
st.success()/st.error()/st.warning() st.metric()cards for each timeframe trend- Template:
rules/assets/streamlit_multi/app.py
streamlit-scanner — Scanner Dashboard (Streamlit)
- Sidebar: scan type selector, run button
st.progress()during scanst.dataframe()for results tablest.download_button()for CSV export
Running the Dashboard
After creating the app, provide instructions:
Dash:
cd dashboards/{dashboard_name}
python app.py
# Open http://127.0.0.1:8050 in browser
Streamlit:
cd dashboards/{dashboard_name}
streamlit run app.py
# Open http://localhost:8501 in browser
Example Usage
/indicator-dashboard single SBIN
/indicator-dashboard multi-timeframe RELIANCE
/indicator-dashboard scanner-dashboard
/indicator-dashboard streamlit-single SBIN
/indicator-dashboard streamlit-multi RELIANCE
/indicator-dashboard streamlit-scanner
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/technical-indicator-dashboard-mm78no68/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/technical-indicator-dashboard-mm78no68' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/technical-indicator-dashboard-mm78no68/download' --output '.skill-downloads/technical-indicator-dashboard-mm78no68/SKILL.md.part' && mv '.skill-downloads/technical-indicator-dashboard-mm78no68/SKILL.md.part' '.skill-downloads/technical-indicator-dashboard-mm78no68/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
Tools declared by the author (check availability in your agent): Read, Write, Edit, Bash, Glob, Grep, AskUserQuestion
No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/technical-indicator-dashboard-mm78no68/SKILL.md' && test -s '.skill-downloads/technical-indicator-dashboard-mm78no68/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/indicator-dashboard' && cp -R '.skill-downloads/technical-indicator-dashboard-mm78no68' '.claude/skills/indicator-dashboard'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/technical-indicator-dashboard-mm78no68'openskills read 'technical-indicator-dashboard-mm78no68'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/technical-indicator-dashboard-mm78no68/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
Create Interactive Maps with kepler.gl
Data & AI
Create interactive map visualizations and export standalone HTML using the keplergl Python package. Use to plot geospatial data from DataFrames, GeoJSON, or CSV.
Agent-Driven Parameter Optimization
Data & AI
Guide for optimizing configurable systems with a measurable objective, using bounded trials and evidence-based recommendations.
ICP Visitor Outreach
Data & AI
Identify signed-up users matching the ideal customer profile but stalled, and draft personalized outreach to re-engage them.