All Services

Calculates the gap between a mining electricity cost target and actual electricity charges based on supplied energy usage and rate inputs.

Classifies mining electricity charge values (energy_kwh × usd_per_kwh) as below, within, or above a user-defined threshold range across a batch of rows.

Computes benchmark correlation for mining energy consumption (kWh) given power draw, operating hours, and uptime percentage across multiple mining rigs or scenarios.

Calculates energy consumption per BTC mined (energy intensity) for up to 200 rows of mining scenario inputs in a single batch call

Analyzes ordered trends in Bitcoin mining energy intensity (kWh per BTC) across a sequence of up to 200 data points, returning a bounded trend analysis report.

Classifies mining energy intensity (kWh per BTC) data rows as below, within, or above user-defined lower and upper thresholds

Ranks rows of mining facility IT energy consumption and PUE values against each other, returning a bounded record ranking report.

Calculates the target gap for a mining facility's total energy consumption given IT load (kWh) and Power Usage Effectiveness (PUE), returning a bounded report for the specified target.

Rebases a series of mining facility PUE-adjusted overhead energy readings to a base-100 index, using supplied IT load (kWh) and PUE values.

Computes the Pearson/benchmark correlation between mining facility IT energy consumption and PUE-driven overhead energy against a set of benchmark values.

Computes a statistical distribution summary of total facility energy consumption (IT + overhead) derived from supplied PUE and IT kWh values across multiple mining operation rows.

Performs an ordered trend analysis on mining facility overhead energy data, using IT power consumption and PUE values across multiple time rows to reveal directional patterns.

Ranks a list of mining facility records by overhead energy efficiency using PUE and IT energy consumption inputs, returning a bounded ranking report.

Classifies mining facility overhead energy consumption (total kWh including PUE overhead) into threshold bands (below lower, within range, above upper) for a batch of PUE/IT-kWh row inputs.

Computes a weighted mean of total mining facility energy (IT load × PUE) across multiple scenario rows using supplied weights.

Computes the statistical correlation between mining fleet power consumption (kW) and a set of benchmark values supplied by the caller.

Computes a bounded statistical distribution summary of total power consumption (kW) for a multi-row mining fleet given unit counts and per-unit wattage.

Computes local finite-change elasticity for mining fleet power consumption given baseline fleet size and per-unit wattage, returning a bounded sensitivity report.

Calculates the power target gap for a mining fleet given a target value and fleet configuration (units and watts per unit).

Computes adjacent period changes in spare coverage ratios for a mining fleet, given sequences of spare and operating unit counts.

Calculates spare-unit coverage ratios for multiple mining fleet configurations in a single batch request using supplied assumptions.

Computes the local finite-change elasticity of mining fleet spare coverage ratio with respect to a chosen input variable, given user-supplied baseline assumptions.

Ranks a list of mining fleet records by their spare coverage ratio (spare units divided by operating units) and returns a bounded ranking report.

Computes a rolling (trailing) mean of spare coverage ratios across ordered observations of mining fleet spare and operating unit counts.

Calculates the target gap for mining fleet spare coverage given spare units, operating units, and a numeric target threshold.

Generates a two-input scenario grid showing spare unit coverage ratios across combinations of spare_units and operating_units for mining fleet analysis

Computes a weighted mean of spare-unit coverage ratios across multiple mining fleet scenarios using user-supplied spare and operating unit counts with custom weights.

Computes adjacent period-over-period changes in mining hardware purchase budget totals (units, unit cost, shipping) from user-supplied assumptions, returning a bounded delta report.

Computes a distribution summary (spread, totals, percentiles) for a multi-row mining hardware purchase budget given units, unit price, and shipping cost per row.

Applies interquartile range (IQR) outlier detection to a batch of mining hardware purchase budget rows, flagging anomalous entries based on units, unit price, and shipping cost.

Computes the local finite-change elasticity of a mining hardware purchase budget with respect to a specified input field (units, unit price, or shipping cost) given a baseline scenario and a step size.

Analyzes ordered trend patterns in mining hardware purchase budgets across multiple row entries, computing totals and trend metrics from units, unit prices, and shipping costs.

Ranks multiple mining hardware purchase options by total budget cost (units × unit price + shipping), returning a bounded ranking report for up to 200 rows.

Computes a rolling mean of total mining hardware purchase costs (units × unit price + shipping) across a sequence of purchase rows with a configurable window size.

Calculates the gap between a budget target and the total cost of a mining hardware purchase (units × unit price + shipping), returning a bounded report.

Classifies a total mining hardware purchase budget as within or outside user-defined lower/upper thresholds, based on supplied unit costs and shipping figures.

Computes a weighted mean of total mining hardware purchase costs (unit price + shipping) across multiple purchase rows using user-supplied weights.

Records a single upvote for a specific feature request on the Mining Incidents MSHA database platform

Calculates net capital requirements for cryptocurrency mining operations across multiple hardware configurations in a single batch request

Computes benchmark correlation for mining net capital requirements given hardware, installation, and rebate cost inputs across multiple scenarios.

Computes the local finite-change elasticity of mining net capital requirement with respect to a chosen input variable (hardware, installation, or rebates cost)

Analyzes ordered trends in mining net capital requirements across multiple hardware/installation/rebate cost scenarios.

Ranks multiple mining operation records by net capital requirement, computed from hardware, installation, and rebate inputs.

Computes a rolling mean of mining net capital requirements across multiple periods given hardware, installation, and rebate cost inputs.

Calculates the target gap for mining net capital requirements given hardware, installation, and rebate assumptions

Classifies mining net capital requirement values as below, within, or above a user-defined threshold range based on supplied hardware, installation, and rebate cost assumptions.

Computes a weighted result mean for mining net capital requirement across multiple hardware/installation/rebate scenarios using user-supplied assumptions.

Generates a base-100 rebase report for mining operating cash contribution across multiple periods, normalizing all values relative to a chosen baseline row.

Computes the Pearson or similar correlation between mining operating cash contribution (revenue minus electricity, maintenance, and other costs) and a user-supplied benchmark series across up to 200 data rows.

Computes the local finite-change elasticity of mining operating cash contribution (revenue minus costs) with respect to a single input variable, given user-supplied baseline assumptions and a step size.

Analyzes ordered trends in mining operating cash contribution (revenue minus electricity, maintenance, and other costs) across a time series of up to 200 data rows.

Calculates the gap between actual mining operating cash contribution and a specified target, given revenue, electricity, maintenance, and other cost inputs.

Generates a two-input scenario grid showing mining operating cash contribution across combinations of two chosen financial variables (revenue, electricity, maintenance, or other costs).

Calculates adjacent period changes in mining operating cash reserve given daily cost and reserve days inputs across multiple periods.

Computes a Base-100 indexed report for mining operations' cash reserve requirements, normalizing multiple cost/duration scenarios against a chosen baseline row.

Computes the Pearson (or similar) correlation between mining operating cash reserve values and a set of numeric benchmarks across multiple row inputs.

Computes the local finite-change elasticity of a mining operation's cash reserve with respect to a chosen input variable (daily cost or reserve days), given user-supplied baseline assumptions.

Performs an ordered trend analysis on a series of mining operating cash reserve data points, returning a bounded report based on supplied daily cost and reserve day assumptions.

Ranks a list of mining operations by their operating cash reserve (daily cost × reserve days) and returns a bounded ranking report.

Computes a sequential (waterfall) attribution report showing how changes in daily cost and reserve days individually drive the difference in a mining operation's required cash reserve.

Calculates the target gap for a mining operation's cash reserve based on daily operating costs and the number of days of reserve required.

Computes a weighted arithmetic mean of mining operating cash reserve values (daily cost × reserve days) across multiple mining operation profiles.

Calculates the gap between a mining operation's current contribution margin and a specified target, using supplied revenue and contribution figures.

Computes benchmark correlation between mining operating cost per terahash and user-supplied benchmark values across multiple data rows.

Calculates the gap between a target value and the computed operating cost per unit of hash capacity, given daily mining costs and active terahash rate.

Validates the header/schema contract of a mining payout CSV file (up to 1000 rows), checking column names, data types, value ranges, and category membership.

Validates numeric values in a mining payout CSV file to ensure all figures fall within declared minimum and maximum bounds.

Validates and audits whitespace issues in a mining payout CSV file (up to 1000 rows), checking cell formatting, delimiters, and categorical values.

Computes benchmark correlation statistics for mining payout threshold waiting times given supplied mining assumptions across multiple rows and benchmark values.

Computes a rolling mean of the estimated waiting time to reach a Bitcoin mining payout threshold across multiple data rows, given user-supplied mining assumptions.

Classifies how long a miner must wait to reach a payout threshold given current balance and daily earnings, and assigns the result to a categorical band.

Computes a weighted mean of estimated waiting times until miners reach their payout threshold across multiple mining scenarios.

Computes a Base-100 rebased index of transaction fee burden relative to mining payouts across a supplied series of BTC fee/payout row pairs

Computes the local finite-change elasticity of mining transaction fee burden relative to a small percentage change in either fee or payout amount, given user-supplied baseline BTC values.

Ranks a batch of mining payout records by the ratio of transaction fee to payout amount (fee burden), using supplied BTC figures.

Computes a rolling mean of the transaction fee share relative to mining payouts over a supplied time-series of BTC fee and payout pairs.

Calculates the percentage burden of a Bitcoin transaction fee relative to a mining payout amount for a single input pair.

Calculates the target gap between a mining payout amount in BTC and the transaction fee burden, returning a bounded report based on supplied mining assumptions.

Generates a two-dimensional scenario grid showing how mining payout transaction fee burden varies across combinations of fee_btc and payout_btc values

Computes a weighted mean of the transaction fee burden (fee as a fraction of payout) across multiple mining payout records using supplied BTC fee and payout amounts.

Validates a CSV of mining pool metrics and identifies blank/empty rows in the dataset

Validates cell lengths in a CSV table of mining pool metrics, auditing up to 1000 rows for length compliance against configurable limits.

Validates a CSV table of mining pool metrics (up to 1000 rows) against a declared contract, checking structure, cell lengths, numeric ranges, and category values.

Validates a CSV of mining pool metrics against a declared column contract and projects allowed columns, supporting up to 1000 rows with quoted cells.

Validates CSV record key uniqueness for mining pool metric data, checking up to 1000 rows for duplicate observation IDs.

Validates and normalizes a CSV of mining pool metrics (hashrate, rewards, pool IDs) with trimmed typed conversion, enforcing declared schema contracts up to 1000 rows.

Validates a CSV table of mining pool metrics for whitespace issues, checking up to 1000 rows with support for quoted cells and configurable constraints.

Computes a base-100 rebased index of mining power capacity headroom (capacity minus committed load) across multiple time-series or site rows, normalised to a chosen baseline row.

Computes and returns a distribution summary report for mining power capacity headroom across an array of site-level capacity and committed-power observations.

Analyzes ordered trend patterns in mining power capacity headroom (available vs. committed kW) across a series of data points, returning a bounded trend analysis report.

Calculates the target gap (headroom) between total mining power capacity and committed load, given user-supplied capacity and committed kilowatt values against a numeric target.

Classifies whether mining power capacity headroom values fall within a specified lower/upper threshold band, using supplied capacity and committed power figures.

Generates a base-100 indexed rebase report of mining revenue across a series of hashprice and hashrate scenarios, normalizing all values against a chosen baseline row.

Identifies IQR-based statistical outliers in mining revenue estimates calculated from user-supplied hashrate and hashprice inputs.

Ranks a list of mining revenue scenarios by revenue output given user-supplied hashrate (TH/s) and hashprice (USD per PH/day) assumptions.

Computes how a miner's availability-adjusted BTC reward changes between adjacent time periods given full-time BTC reward and uptime percentage inputs.

Calculates availability-adjusted mining rewards for up to 200 rows of fulltime BTC reward and uptime percentage inputs in a single batch call.

Computes a distribution summary of mining rewards adjusted for miner availability percentage across a batch of mining scenario rows.

Computes IQR-based outlier flags for mining rewards adjusted by miner availability percentage across a batch of data rows.

Computes the local finite-change elasticity of mining reward (adjusted for availability) with respect to a specified input field, given a baseline scenario and a percentage step size.