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Runs three local analytical reports on record routing data: a quality audit, exact row deduplication, and a grouped numeric summary of accepted/rejected record counts.

Runs three local analytical reports on record routing data — a quality audit, exact row deduplication, and IQR-based outlier flagging — for up to 200 rows of accepted/rejected record counts.

Runs three local analytical reports on record routing data — a quality audit, exact row deduplication, and numeric interval filter — for up to 200 rows of accepted/rejected record counts.

Audits data quality and fills missing values for accepted_records and rejected_records fields in up to 200 record routing rows, producing two independent analytical reports.

Runs a record quality audit and IQR-based outlier detection on up to 200 rows of record routing data (accepted_records and rejected_records), returning two independent analytical reports.

Runs a record quality audit and a numeric interval filter on up to 200 rows of record routing data (accepted_records, rejected_records), returning 2 independent analytical reports.

Performs a two-part local analytical report on record routing data: a quality audit of accepted/rejected record counts and string normalisation of routing record fields, for up to 200 rows.

Runs three independent analytical reports (record quality audit, string normalisation, and base-100 index series) on supplied record routing data containing accepted and rejected record counts, for up to 200 rows.

Runs three bundled analytical reports on record routing data — quality audit, string normalisation, and category frequency analysis — for up to 200 rows of accepted/rejected record counts.

Runs three local analytical reports (quality audit, string normalisation, and duplicate key detection) on up to 200 record routing rows containing accepted and rejected record counts.

Runs three local analytical reports — record quality audit, string normalisation, and equal-width histogram — on up to 200 record routing rows containing accepted and rejected record counts.

Runs three local analytical reports on record routing data — a quality audit, string normalisation, and grouped numeric summary — for up to 200 rows of accepted/rejected record counts.

Runs three local analytical reports on record routing data — a quality audit, string normalisation, and IQR-based outlier detection — for up to 200 rows of accepted/rejected record counts.

Runs three local analytical reports on record routing data — quality audit, string normalisation, and keyed dataset comparison — for up to 200 rows of accepted/rejected record counts.

Runs three local analytical reports on record routing data — a record quality audit, string normalisation, and numeric distribution analysis — for up to 200 rows of accepted/rejected record counts.

Normalises strings and deduplicates exact rows in record routing datasets (accepted_records, rejected_records) across up to 200 rows, producing 2 analytical reports.

Normalises string fields and produces a grouped numeric summary report for up to 200 record routing rows (accepted_records, rejected_records), returning two analytical reports per call.

Computes a trailing mean time series and a date-by-category pivot table for accepted_records and rejected_records fields in supplied record routing data (up to 200 rows, two reports per call).

Computes a trailing mean time series and an equal-width histogram for accepted_records or rejected_records fields in record routing data, returning two independent statistical reports for up to 200 rows.

Computes trailing mean time-series analysis and fills missing values for accepted_records and rejected_records routing data, returning two analytical reports for up to 200 rows.

Computes trailing mean time series and IQR-based outlier flags for accepted_records and rejected_records fields in supplied record routing data (up to 200 rows, 2 reports per call).

Computes a trailing mean time series and a keyed dataset comparison for record routing data (accepted_records, rejected_records) across up to 200 rows, returning two independent analytical reports.

Chat with a tool-enabled AI agent that can perform tasks on your behalf via a paid API endpoint

Calculates adjacent-period changes in recoverable mining heat value given energy consumption, heat price per kWh, and recoverable percentage across multiple time periods.

Computes a base-100 indexed rebase report for recoverable mining heat value across multiple scenarios using supplied energy, heat price, and recovery rate assumptions.

Computes and benchmarks the correlation between recoverable mining heat value metrics across multiple data rows against supplied benchmark values.

Generates a distribution histogram of recoverable mining heat values from supplied energy, recovery, and heat price data across configurable bins.

Computes a statistical distribution summary for recoverable mining heat value across a batch of energy input rows using predefined mining assumptions.

Compares named scenarios for recoverable mining heat value, showing how different energy, heat price, and recovery rate assumptions affect the calculated outcome.

Generates a one-input sensitivity table showing how recoverable mining heat value changes as a single input variable (energy, recovery rate, or heat price) varies across supplied values.

Performs ordered trend analysis on recoverable mining heat value across a series of data rows, using supplied energy, recoverability, and heat price inputs.

Ranks multiple mining scenarios by recoverable heat value using supplied energy, recoverable percentage, and heat price inputs.

Computes a rolling mean of recoverable mining heat value across a time-series of energy rows, using supplied mining assumptions and a fixed formula.

Classifies recoverable mining heat value results as below, within, or above a defined threshold range, given energy, recovery rate, and heat price inputs.

Generates a two-dimensional scenario grid showing recoverable mining heat value across combinations of energy consumption, recoverable percentage, and heat price per kWh.

Compares a list of required recovery-check IDs against supplied check states to identify missing, conflicting, or unknown conditions that cannot be satisfied.

Validates declared byte counts and payload/receipt retention durations against caller-specified ceilings using exact integer comparison, without reading actual storage or measuring costs.

Groups recovery manifest IDs within payment, network, and revision frames to distinguish exact duplicates from conflicting result or deadline projections.

Compares two ordered lists of revision-pinned recovery manifest digests to identify reorderings and insertions of shared manifests, without any store mutation.

Inspects declared parent and retry links within recovery manifest metadata to detect missing manifests, cycles, and cross-frame links across payment or chain contexts.

Reports missing required-field declarations (payment, input, result, and original expiry) in recovery-manifest metadata without accessing secrets, signatures, or payloads.

Validates that TTL unit labels (seconds, milliseconds, hours) in payload and receipt fields of recovery manifests match the declared retention contract, without converting units or accessing storage.

Removes repeated and blank lines from text while preserving original order, optimized for recruiting workflows.

Replaces email addresses in recruiting-related text with a privacy marker to anonymize contact information.

Formats product features into concise, polished product copy tailored for recruiting workflows and audiences.

Truncates or condenses recruiting-related text into a short excerpt within a specified character limit.

Converts a recruiting-related job title or text string into a lowercase, URL-safe slug optimized for recruiting workflows.

Computes period-over-period (adjacent row) changes for recruitment funnel metrics—applications or interviews—across up to 200 dated records.

Computes daily sum time series for a chosen recruitment funnel metric (applications or interviews) from up to 200 typed records, grouped by date.

Pivots recruitment funnel data (applications or interviews) by date and source category, returning a cross-tabulated summary from up to 200 supplied records.

Detects duplicate row keys in recruitment funnel datasets (applications and interviews by source and date) and returns a data-quality report.

Removes exact duplicate rows from recruitment funnel datasets containing application and interview data, returning a deduplicated record set.

Fills missing numeric values (applications or interviews) in recruitment funnel records using zero, mean, or median imputation methods

Computes a grouped numeric summary (aggregated statistics) for recruitment funnel data — applications and interviews — grouped by source category, up to 200 records.

Compares two keyed datasets of recruitment funnel records (applications and interviews) row-by-row, producing data-quality and numeric difference analysis locally without external enrichment.

Computes a numeric distribution (e.g. histogram/percentile breakdown) for a specified field—applications or interviews—across up to 200 recruitment funnel records.

Filters recruitment funnel records (applications and interviews) to only return rows whose selected numeric field falls within a specified minimum–maximum range.

Sorts recruitment funnel records (applications and interviews) by a chosen numeric field, returning rows in ordered sequence for up to 200 typed records.

Audits up to 200 recruitment funnel records for data quality issues, detecting anomalies, nulls, and consistency problems across application and interview metrics.

Computes a weighted mean for a secondary measure (applications or interviews) across recruitment funnel data rows, supporting up to 200 typed records.

Computes the statistical correlation between applications and interviews across recruitment funnel records (up to 200 rows).

Searches publicly indexed Reddit discussions focused on consulting topics using live Bing web search results

Searches publicly indexed Reddit discussions about procurement topics using live Bing web search results

Searches publicly indexed Reddit discussions filtered to product team topics using live Bing web search results

Searches publicly indexed Reddit discussions relevant to research teams using live Bing web search results

Searches publicly indexed Reddit discussions focused on small business topics using live Bing web search results

Returns module and type entrypoints for the redis npm package, enabling integration and upgrade planning by inspecting registry metadata.

Returns published artifact integrity metadata (checksums, signatures, tarball info) for a given version of the redis npm package to support integration and upgrade planning.

Returns runtime and platform constraints metadata for the redis npm package from the registry, supporting integration and upgrade planning.

Fetches and summarizes a given HTTPS web page URL, returning a condensed text summary

Generates a hash/snippet fingerprint of a web page at a given URL for later change detection

Fetches a web page and returns both extracted text and a summary in a single bundled response

Verifies whether a payment rail's referral program actually pays out by checking code binding, activation thresholds, credit amounts, vesting, clawback terms, and on-ledger traceability.

Returns a blunt, LLM-generated decision procedure for evaluating affiliate and referral programs—covering what they actually pay agents, terms audits, and payout reliability scoring.

Validates a refund imports CSV file and reports blank/empty rows, up to 1000 rows

Validates that all category values in a refund CSV file belong to a declared allowlist, returning a compliance report for up to 1000 rows.

Computes per-column statistics for a refund-import CSV table, validating up to 1000 rows against a declared schema contract.

Validates a CSV table of refund records against a declared contract, checking up to 1000 rows for structural and data integrity issues.

Validates a CSV file of refund import data against a declared column contract, reporting structure and value conformance for up to 1000 rows.

Validates a refund transactions CSV for exact row duplicates, returning a structured duplicate-detection report for up to 1000 rows.

Validates the column headers and data contract of a refund imports CSV file against declared constraints, returning a contract check report.

Validates a refund CSV against a declared schema contract and returns a report of all invalid rows, up to 1000 rows.

Validates a refund imports CSV table (up to 1000 rows) and returns a located error report identifying cell-level data quality issues.

Validates numeric cell values in a refund-import CSV against configurable minimum/maximum bounds, returning a range-check compliance report.

Validates that every record key in a refund imports CSV table is unique, returning a uniqueness report for up to 1000 rows.

Validates and normalizes a CSV table of refund records (up to 1000 rows) into trimmed, typed data with configurable delimiter, numeric bounds, and allowed categories.

Validates a CSV of refund records and exports only the rows that pass all defined validity checks, up to 1000 rows.

Validates a refund-import CSV for whitespace anomalies in cell content, checking up to 1000 rows with configurable delimiter, cell length, and category constraints.

Runs two analytical reports on supplied registry snapshot data: adjacent row-to-row change detection and a date-by-category pivot table, for up to 200 rows.

Analyzes registry snapshot data to detect adjacent row changes and fill missing values in registered_entities or changed_entities fields, returning two analytical reports for up to 200 rows.

Analyzes registry snapshot records to compute adjacent row changes and flag IQR-based outliers for registered_entities and changed_entities fields, producing two local analytical reports per call.

Analyzes registry snapshot data (registered_entities, changed_entities) with two local analytical reports: adjacent row-to-row changes and keyed dataset comparison, for up to 200 rows.

Computes adjacent-row changes and secondary-measure weighted mean for registry snapshot data (registered_entities, changed_entities) across up to 200 rows, returning two analytical reports.

Generates two analytical reports (Base-100 indexed series and equal-width histogram) from supplied registry snapshot data covering registered and changed entities.

Computes base-100 indexed time series and fills missing values for registry snapshot data (registered_entities, changed_entities) across company groups.

Computes Base-100 indexed time series and IQR-based outlier flags for registry snapshot data (registered_entities, changed_entities) across up to 200 rows in two independent analytical reports.

Computes a Base-100 index series and a two-measure correlation report for supplied registry snapshot data (registered_entities, changed_entities) across up to 200 rows.

Analyzes registry snapshot data to produce category frequency distributions and a base-100 indexed series for registered and changed entity counts across company groups.

Analyzes registry snapshot data (registered_entities, changed_entities) by computing category frequency distributions and a date-by-category pivot table, returning two analytical reports for up to 200 rows.

Analyzes supplied registry snapshot records to produce category frequency counts and an equal-width histogram for registered or changed entity metrics.