Data and analytics AI use cases from hot AI repos

Forecast future candlesticks from one price history

Load a pre-trained Kronos model and get a forecast of future OHLCV candles from your own historical data.

Kronos
Data and analytics
Intermediate

View prompt

Using the Kronos repo, load NeoQuasar/Kronos-small with its tokenizer, forecast the next 120 candles for <CSV path with open, high, low, close columns and timestamps>, and return the pred_df table. Verify that the forecast index matches the y_timestamp values you passed in and that the lookback is no more than 512 rows.

Build a KPI dashboard from warehouse SQL with an agent

Have a coding agent turn your SQL queries into a validated, live-previewed YAML dashboard.

View prompt

Using the dbt-charts repo, install the CLI, run `dct skills intro`, and write a KPI board for the metrics described in <placeholder> as YAML under charts/. Run `dct validate` on the file and report any errors. Verify that the board renders in `dct serve`.

Measure shelf attention from a fixed store camera video

Run the gaze pipeline on a fixed-camera clip to rank which shelf zones draw attention.

View prompt

Using the repo escudero/brandpose, run scripts/gaze_sim_v2.py on the fixed-camera video at <video path> with --max-seconds 60, using the flags in the README. Report the number of estimated gazes, shoppers and zones with attention, and the output folder. Verify that outputs/v2 contains results and that the attention figures are labeled as model estimates, not ground truth.

Check delayed IHSG quotes and valuation from a terminal

Install Idx and pull delayed prices, P/E and dividend yield for several IDX tickers in one command.

Idx
Data and analytics
Intermediate

View prompt

Use the idx command from the fzn0x/idx repo to fetch quotes for <tickers such as BBCA,BBRI,TLKM> by running `idx -o json stocks quote <tickers>`. Return price, change percent, P/E and dividend yield for each ticker as a short list. Verify that the response includes a last trade timestamp and state that the data is delayed.

Run a full lakehouse platform on one machine

Start the console, SQL, notebooks and sample Retail lakehouse on a laptop with Python only.

View prompt

Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric). Install it from the README Install section, run `olcf quickstart`, and report the console address and whether the sample Retail lakehouse loads. Verify by running one SQL query against the sample lakehouse and returning the row count.

Ask an AI to explain your sleep and training trends

Sync your Zepp records locally, pick the dates you care about, and get a Markdown file to discuss with your AI.

View prompt

Using the ZeppBridge MCP server in <path to your MCP client config>, query my sleep and workout records for <date range> and summarise the changes in resting heart rate, HRV and training load. Expected output: a short written summary with the dates used. Verify that any date without samples is reported as missing, not treated as zero.

Ask an agent which skills are worth installing

Give an AI agent the ranking guide so it can answer questions about top skills from current data.

View prompt

Read https://linkly.ai/skills/llms.txt, which describes the LinklyAI best-skills rankings. Answer this question: <your question>. Return the top three skills with their Worth-Installing Score, Cov value and per-platform counts. Verify one number against the matching CSV in data/ from the LinklyAI/best-skills repo.

Sort a batch of texts into labels in one call

Label short texts with plain HTTP, no key or signup, up to a thousand per request.

View prompt

Using the classifier.dev API from the classifier-dev repo, label each text in <input file> as bug, feature or praise by calling https://classifier.dev over plain HTTP with no key. Write the results to <output file> as CSV with the original text and the label. Verify that the number of output rows equals the number of input rows.

Find support tickets by meaning, not keywords

Count and group tickets that describe a problem, using a plain-language condition in SQL.

View prompt

Use the jev4pg repository to set up the workspace, import the CSV at <path-to-csv> as a dataset named tickets, and run a SQL query that counts rows where SEMANTIC(body, 'The ticket describes an unresolved billing problem.') is true, grouped by product. Return the grouped counts and confirm that the decision states and any uncertain rows are reported before the counts are treated as final.

Forecast many assets in one batch run

Use predict_batch to forecast several equal-length price series at once and keep results in input order.

Kronos
Data and analytics
Intermediate

View prompt

In the Kronos repo, prepare equal-length candlestick DataFrames for <list of instrument CSV paths>, call predict_batch with pred_len=<number of steps>, and write one forecast CSV per instrument. Verify that the number of returned DataFrames equals the number of input series and that each series has the same lookback length.

Check dashboard YAML for errors before merging

Run strict validation on board files in a pull request, with no warehouse needed.

View prompt

In the dbt-charts repo, run `dct validate --strict` on the boards changed in <placeholder> and list every error and warning with its file path. Verify that the command exits cleanly once the issues are fixed.

Render the full promo video with zones, depth and soundtrack

Rebuild the demo pipeline: extraction, zones, depth, render and synthesized audio.

View prompt

Using the repo escudero/brandpose, follow docs/experiments/2026-10-14-promo-v2.md to run the promo pipeline in scripts/promo/ on the ACTi demo video. Report the output video path and the stage that failed, if any. Verify that the zone ranking in the rendered report matches the zones in config/zones_acti.json.

Screen about 950 IDX stocks by valuation and sector

Filter the IDX universe with one expression, for example low P/E financial stocks.

Idx
Data and analytics
Intermediate

View prompt

Use the idx tool from the fzn0x/idx repo to run `idx -o json stocks screen -f "<filter>"` where <filter> is a rule such as pe 15,sector=financial. Return the matching tickers with their P/E and sector as a table. Verify that the count of results matches the length of the returned data array.

Find what depends on a table and who owns it

Use OLCF Intelligence to follow table lineage and see the confidence and gaps for each edge.

View prompt

Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric). Run `olcf intelligence` against the local installation, and for <table name> list its upstream and downstream assets with the confidence level of each edge. Verify that every edge has a confidence value and that gaps are listed with reasons, not guessed.

Turn an AI coaching reply into a plan on your watch

Paste an AI-written week of workouts into Zepp Bridge, adjust each session, and send it to a compatible watch.

View prompt

Using the ZeppBridge repository, draft a seven-day running plan for <athlete goal> based on my records from <date range>, in the format ZeppBridge accepts. Expected output: a plan file ready to paste into the app. Verify that rest and sleep suggestions are kept as notes only, since they are not sent to the watch.

Install the ranking data as a skill for an agent

Install the Best Skills repo as a skill so an agent keeps the rankings as standing context.

View prompt

Use the best-skills skill installed from https://github.com/LinklyAI/best-skills. Name the ranking list that answers this question: <your question>, then give the CSV file path and its top three rows. Verify that the file path exists in the repo's data/ folder.

Re-check low-confidence labels with a stronger model

Use the smart tier so answers under 0.7 confidence get a second opinion.

View prompt

Using the classifier-dev repo at classifier.dev, classify each item in <input file> with the smart tier for single-label work. Output a table of item, label, confidence and whether it was escalated. Verify that every escalated row has a label that was returned by the service.

Run semantic analysis on existing PostgreSQL tables in place

Attach a table you already run without copying its rows, then query it with JEV from the workspace.

View prompt

Use the jev4pg repository to attach the existing table <schema>.<table> to the workspace for tenant demo without copying rows, using the external PostgreSQL setup. Report the grants that were applied and confirm that a test SELECT through the workspace returns only rows visible to the runtime login.

Fine-tune Kronos on your own market dataset

Adapt the Kronos tokenizer and predictor to your own candlestick data using the Qlib-based finetuning pipeline.

View prompt

In the Kronos repo, edit finetune/config.py for my local Qlib data at <qlib data path>, run the preprocessing script, then run both torchrun training scripts with <number of GPUs> GPUs. Verify that train_data.pkl, val_data.pkl and test_data.pkl exist in dataset_path before starting training.

Find which dashboards break when a column changes

Use dct impact to list the boards that reference a column before you rename it.

View prompt

In the dbt-charts repo, run `dct impact` for the column <placeholder> and list every board that references it. Verify the list against `dct search` results for the same name.

Rebuild the store in Blender and render a shopper's view

Calibrate the camera to marked corners, model the fixtures in Blender and render one shopper's view.

View prompt

Using the repo escudero/brandpose, follow docs/experiments/2026-10-18-blender-pov.md to calibrate the camera with scripts/blender/calib.py, then render the shopper's view with scripts/blender/pov.py for <person ID>. Report the output video path and the calibration result. Verify with scripts/blender/overlay.py that the fixtures match the image before accepting the render.

Let an AI agent pull IDX stock briefs over MCP

Expose Idx as MCP tools so an agent can answer questions about IHSG stocks with live tool calls.

View prompt

Connect to the idx MCP server from the fzn0x/idx repo and call idx_brief for <ticker such as BBCA>. Summarise the quote, verdict, returns against IHSG, top holders and headlines in plain language. Verify that the answer quotes the last trade time from the tool result and says the data is delayed.

Let Claude Code browse the catalog and run read-only SQL

Connect Claude Code to the OLCF MCP endpoint to explore tables, follow lineage and query safely.

View prompt

Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric) through its MCP endpoint. List the catalog assets in the sample Retail lakehouse, then run one read-only SQL count on <table name> and return the result. Verify that the query was rejected if you ask for any write statement.

Give a local AI agent read-only access to your records

Install the headless tools and connect an MCP client so it can query your local Zepp Bridge database.

View prompt

Using the zeppbridge-mcp server configured in <path to MCP config>, list my workouts for <date range> and report total distance and training load. Expected output: a table of workouts with dates. Verify that the server only reads the local database and that no workouts outside the range are returned.

Find skills gaining installs and social attention

Compare the 7-day trending list with the social buzz list to see which skills are rising and talked about.

View prompt

Using the LinklyAI/best-skills repo, compare rankings/trending-7d.csv and rankings/social-buzz.csv from the newest dated folder under data/. List skills that appear in both files, with their Weekly Δ% and their X, HN, Bluesky and GitHub counts. Verify each skill name appears exactly as written in both CSVs.

Send a structured bug report when a label looks wrong

Let an agent file a feedback.now report when classification breaks or misbehaves.

View prompt

Using the feedback.now endpoints at https://classifier.dev described in the classifier-dev repo, file one report for <describe the wrong label> with the input text and the returned label as evidence. Output the report id and the receipt status. Verify that the receipt shows the report was accepted or marked as a duplicate.

Query semantic conditions directly from psql with native functions

Call jev_native functions from any PostgreSQL client against tables you already own.

View prompt

Use the jev4pg repository to install the jev_native extension on a PostgreSQL 17 server and grant access to a SQL login named <login> for the table <schema>.<table>. Return a grouped query over jev_native.scan and confirm that the result includes decision states and uncertainty for each row.

Export a dashboard as HTML, PDF, or PNG

Render a board to a static file that you can send to people who do not run the CLI.

View prompt

In the dbt-charts repo, render the board at <placeholder> to PDF with `dct render <placeholder> --format pdf` and report the output file path. Verify that the file opens and shows the expected charts.

Find who holds more than 1% of an IDX stock

Import KSEI's monthly holder register and list every holder above 1% for one ticker.

Idx
Data and analytics
Intermediate

View prompt

Use the idx tool from the fzn0x/idx repo to run `idx ownership import --latest`, then `idx ownership ticker <ticker>` for <ticker such as BBCA>. Return the holders above 1% with their stake. Verify that the output names the month the register covers.

Run a Fabric notebook on your own Spark hardware

Send a Fabric notebook to your own Spark workers with olcf.run_remote() and read OneLake directly.

View prompt

Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric) and its Fabric integration guide. Attach the olcf_fabric wheel to a Fabric Environment, call olcf.run_remote() on <notebook name>, and report the returned status and exit value. Verify that the target OneLake table has the expected row count after the run.

Back up and export your full health history

Take verified database snapshots and export your records as JSON, CSV, GPX or FIT for safekeeping.

View prompt

Using the ZeppBridge repository, explain how to take a snapshot of my local health database and verify it. Expected output: a short checklist with the steps and where the snapshot is stored. Verify that the checklist mentions previewing differences before restoring.

Compare skill installs across China and global registries

Use the top installs file to see which skills lead on skills.sh, ClawHub and Tencent SkillHub.

View prompt

From rankings/top-installs.csv in the LinklyAI/best-skills repo, list the top five skills by SkillHub CN installs and the top five by skills.sh installs, and report any skill that appears in both lists. Verify that you treat empty platform cells as not listed, not as zero.

Run SQL against your warehouse from the terminal

Use dct query to test a metric query, or run a named board query, before you build a chart.

View prompt

In the dbt-charts repo, run `dct query <placeholder> 'SELECT …'` with the SQL for the metric <placeholder> and print the result rows. Verify that the row count and totals match a known figure from your source system.

Trace top broker buying and selling in an IDX stock

See which brokers lead buying and selling in one ticker and whether a bandar phase is showing.

Idx
Data and analytics
Intermediate

View prompt

Use the idx tool from the fzn0x/idx repo to run `idx broker <ticker>` and `idx bandar detect <ticker> --period 1w` for <ticker such as BBCA>. Return the top buyers, top sellers and the accumulation or distribution read. Verify that the output reports the session date and states whether the phase is accumulation or distribution.

Build a Spark cluster from your own machines

Enroll several machines as workers and run Spark jobs on the combined Standalone cluster.

View prompt

Use the open-lakehouse-compute-fabric repo (jddevtime/open-lakehouse-compute-fabric). Install the Spark runtime with `olcf runtime install --with-jdk`, enroll <number of machines> workers, and report the cluster members. Verify that a test job runs and that the worker list shows each machine as enrolled.

Run scheduled record syncs on a home server

Use the headless Docker image to keep your local library current without a desktop session.

View prompt

Using the ZeppBridge repository, read the Docker guide and list the files needed to run a headless sync on <server name>. Expected output: a checklist of files and where to place them. Verify that the checklist treats auth.json and the token as secrets to keep off shared storage.

Shortlist verified official agent skills

Pull the Official 100 list to pick skills published by verified publishers on each registry.

View prompt

Open rankings/official-100.csv in the LinklyAI/best-skills repo from the newest dated folder under data/. List the first five skills with their vendor and Verified by value. Verify that every row you list has a non-empty Verified by value.