MCP server that wraps the Dagster GraphQL API — manage runs, assets, schedules, sensors, and backfills from any MCP client
Nothing to run from here
Stats Explorer is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
99/100
trust score
9
downloads a day
local
runs on your machine
798
downloads in 30d
What it does
Verified statistics for AI agents: guided tests, A/B tests, Tukey HSD, reports, optional Jev checks.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user stats-explorer-mcp '--' npx -y stats-explorer-mcp
# 2 — ask your agent for something these tools do
# choose_test, inspect_data_file, describe and 31 more
Stats Explorer costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publisher
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts Stats Explorer as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. Its scan result is on the Security tab — read the surface before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user stats-explorer-mcp '--' npx -y stats-explorer-mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it runs from its publisher’s own package: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it asks of you
Nothing is required, but it can take STATS_EXPLORER_ROOTS (Folders the tools may read data files from and write reports to (separated by : on macOS/Linux, ; on Windows). Defaults to the client's workspace roots, or the current directory.), STATS_EXPLORER_STREAM_MB (CSV/TSV/Parquet files above this size (MB) are streamed in parallel passes; any size works. Default 512.), STATS_EXPLORER_WORKERS (Parallel readers for large files. Default: all CPU cores but one (max 16).), STATS_EXPLORER_CACHE (Keep columns read from large files for fast follow-up questions: ask (default), on or off.), STATS_EXPLORER_CACHE_DIR (Folder for saved large-file columns. Default: the system temp folder.), STATS_EXPLORER_MAX_FILE_MB (Refuse data files larger than this (MB). Default: no limit.), STATS_EXPLORER_GUIDED (on (default): classify each request and follow the six-step workflow for hypothesis tests. off: expose the individual test tools directly.), STATS_EXPLORER_CONFIRM_SELECTIONS (on (default): the user confirms every column, chart type and report section before anything runs. off: run with the AI's choices.), STATS_EXPLORER_CONFIRM_VIA (auto (default): forms where the client supports them. chat: always ask in the conversation (recommended for Claude Code).), STATS_EXPLORER_HIDE_LINK (Set to 1 to remove the one-line Stats Explorer app link from results.), STATS_EXPLORER_JEV (Independent checks by Jev (TypeSafe): off (default), cooperate (the AI calls your Jev MCP server), enforce (Stats Explorer calls the Jev API) or orchestrated (signed decisions from an orchestrator).), STATS_EXPLORER_JEV_CHECKPOINTS (Comma list of plan, conclusions, data. Default plan,conclusions (data sends column names and text samples to Jev).), TYPESAFE_API_KEY (Enforce mode only: TypeSafe API key Stats Explorer uses to call Jev.), STATS_EXPLORER_APPROVAL_SECRET (Orchestrated mode only: shared secret (32+ characters) the orchestrator signs decisions with.), STATS_EXPLORER_AUDIT_LOG (Path of a JSON Lines file recording every Jev checkpoint decision.) if you want to set them.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
34 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Read from its source code. A server that builds its tool list when it starts can list more tools than this.
Recommends which test tool to run for a research question, with the assumptions to check. Call this first when unsure.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| goal | string | yes | ab_test: compare variants of an experiment (A/B/n) against a control; plan_ab_test: sample size for one. |
| paired | boolean | no | Same subjects measured twice / matched pairs. |
| has_covariate | boolean | no | A numeric variable to adjust for. |
| multiple_outcomes | boolean | no | Several numeric outcomes analysed together. |
| known_population_sd | boolean | no | — |
| outliers_or_ranks | boolean | no | Data are ranks, ordinal, or have strong outliers. |
| outcome_is_numeric | boolean | no | A/B tests: the metric is a number per visitor (revenue, time) rather than converted yes/no. |
Lists the columns of a local CSV/TSV/JSON file with their types, missing values, ranges and category values, plus a 5-row preview. Use before running tests on a file so you pass correct column and group names.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| file | string | yes | — |
n, mean, SD, SE, median, quartiles, range, skewness, excess kurtosis, distribution shape and a t-based CI for the mean. Inline values, or a file column optionally split by group_column.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| values | array | no | — |
| name | string | no | — |
| file | string | no | — |
| column | string | no | — |
| group_column | string | no | — |
| confidence | number | no | Confidence level for intervals. Default 0.95. |
One-sample, paired, or independent two-sample t-test (Welch by default; set equal_variances true for Student). Returns t, df, p, CI, Cohen's d and assumption warnings. Data inline (sample1/sample2) or from a file: one_sample uses column; paired uses column + column2; independent uses column +…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| type | string | yes | — |
| sample1 | array | no | — |
| sample2 | array | no | — |
| mu0 | number | no | Hypothesised mean for one_sample. |
| equal_variances | boolean | no | false (default) = Welch's t-test; true = Student's pooled t-test. |
| file | string | no | — |
| column | string | no | — |
| column2 | string | no | — |
| group_column | string | no | — |
| group1 | string | no | Name of the first group (label in results; or category value in group_column). |
| group2 | string | no | — |
| alternative | string | no | two-sided (default), greater (first > second / sample > value), or less. |
| alpha | number | no | Significance level. Default 0.05. |
| confidence | number | no | Confidence level for intervals. Default 0.95. |
One-proportion z-test (successes, n, p0) or one-sample mean z-test with a known population SD (values or sample_mean + n, population_sd, mu0).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| type | string | yes | — |
| successes | integer | no | — |
| n | number | no | — |
| p0 | number | no | — |
| values | array | no | — |
| sample_mean | number | no | — |
| population_sd | number | no | — |
| mu0 | number | no | — |
| alternative | string | no | two-sided (default), greater (first > second / sample > value), or less. |
| alpha | number | no | Significance level. Default 0.05. |
| confidence | number | no | Confidence level for intervals. Default 0.95. |
One-way ANOVA across 2+ groups with eta²/omega², group summaries and, for 3+ groups, a Tukey HSD post-hoc test that highlights which groups differ (letters, best group). Optional Bonferroni-adjusted pairwise Welch t-tests. Inline groups [{name, values}] or file + column + group_column.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| groups | array | no | — |
| file | string | no | — |
| column | string | no | — |
| group_column | string | no | — |
| groups_to_include | array | no | Subset of group_column categories to compare. |
| pairwise | boolean | no | Also run Bonferroni-adjusted Welch t-tests between every pair. |
| post_hoc | string | no | Tukey HSD post-hoc test for 3+ groups (default): adjusted p-values, simultaneous intervals and the groups that differ. |
| alpha | number | no | Significance level. Default 0.05. |
| confidence | number | no | Confidence level for intervals. Default 0.95. |
A/B or A/B/n experiment readout, each variant against a control. metric "conversion" (converted yes/no: two-proportion z-test, Fisher's exact when counts are small) or "continuous" (revenue, time, order value: Welch t-test). Returns per-variant rates/means with intervals, absolute and relative…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| metric | string | yes | conversion: a yes/no outcome per visitor. continuous: a numeric value per visitor (revenue, time on page). |
| variants | array | no | Inline per-variant numbers, control first unless control is given. |
| file | string | no | — |
| variant_column | string | no | Column with the experiment arm (A, B, control, treatment…). |
| outcome_column | string | no | Conversion indicator (1/0, yes/no, true/false) or the numeric metric. |
| success_value | no | Value of outcome_column that means converted, if not obvious. | |
| variants_to_include | array | no | — |
| control | string | no | Name of the control / baseline variant. Default: the first variant. |
| expected_split | object | no | Planned traffic share per variant, e.g. {"A": 50, "B": 50}. Default: equal split. |
| correction | string | no | Multiple-comparison adjustment when 2+ variants are compared with the control. |
| mde | number | no | Smallest lift that matters to the business, as an absolute difference (0.005 = 0.5 percentage points); each lift is judged against it. |
| segment_column | string | no | Optional: also break the result down by this column (device, country…). Exploratory. |
| bayesian | boolean | no | — |
| prior | object | no | Beta prior for conversion rates. Default Beta(1, 1). |
| srm_alpha | number | no | Threshold for the sample ratio mismatch check. Default 0.001. |
| post_hoc | string | no | Tukey HSD between all variants (continuous metric, 3+ variants). |
| alternative | string | no | two-sided (default), greater (first > second / sample > value), or less. |
| alpha | number | no | Significance level. Default 0.05. |
| confidence | number | no | Confidence level for intervals. Default 0.95. |
Plan an A/B test: visitors needed per variant to detect the smallest lift that matters (mde) at a given power, and/or the smallest lift detectable with a given n_per_variant; days needed from daily_traffic. Conversion: baseline_rate. Continuous: baseline_sd (and baseline_mean for a relative mde).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| metric | string | yes | — |
| baseline_rate | number | no | — |
| baseline_mean | number | no | — |
| baseline_sd | number | no | — |
| mde | number | no | Smallest lift that matters: absolute (0.005 = +0.5 percentage points) or relative with mde_type "relative" (0.1 = +10%). |
| mde_type | string | no | — |
| n_per_variant | integer | no | — |
| variants | integer | no | — |
| adjust_for_variants | boolean | no | Split α across the comparisons with the control (Bonferroni) when there are 3+ variants. |
| power | number | no | — |
| daily_traffic | number | no | — |
| alternative | string | no | two-sided (default), greater (first > second / sample > value), or less. |
| alpha | number | no | Significance level. Default 0.05. |
goodness_of_fit: observed counts vs expected (proportions or counts; uniform if omitted), inline or by counting a file column. independence: contingency table inline or cross-tabulated from two categorical file columns; returns Cramér's V and expected counts. No continuity correction.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| type | string | yes | — |
| observed | array | no | — |
| expected | array | no | — |
| categories | array | no | — |
| expected_by_category | object | no | For file mode: expected proportion or count per category value. |
| table | array | no | — |
| row_labels | array | no | — |
| col_labels | array | no | — |
| file | string | no | — |
| column | string | no | — |
| row_column | string | no | — |
| col_column | string | no | — |
| alpha | number | no | Significance level. Default 0.05. |
Exact test of association for a 2×2 table; use instead of chi_square when expected counts are small. Inline table [[a,b],[c,d]] or two binary file columns (row_column, col_column).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| table | array | no | — |
| row_labels | array | no | — |
| col_labels | array | no | — |
| file | string | no | — |
| row_column | string | no | — |
| col_column | string | no | — |
| alternative | string | no | two-sided (default), greater (first > second / sample > value), or less. |
| alpha | number | no | Significance level. Default 0.05. |
Exact test of a success count against a hypothesised probability, with a Wilson CI.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| successes | integer | yes | — |
| trials | integer | yes | — |
| p0 | number | no | — |
| alternative | string | no | two-sided (default), greater (first > second / sample > value), or less. |
| alpha | number | no | Significance level. Default 0.05. |
| confidence | number | no | Confidence level for intervals. Default 0.95. |
Pearson (linear) or Spearman (rank) correlation with p-value, Fisher CI (Pearson) and least-squares line. Inline x/y or file + x_column + y_column (rows missing either are dropped).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| x | array | no | — |
| y | array | no | — |
| x_name | string | no | — |
| y_name | string | no | — |
| file | string | no | — |
| x_column | string | no | — |
| y_column | string | no | — |
| method | string | no | — |
| alternative | string | no | two-sided (default), greater (first > second / sample > value), or less. |
| alpha | number | no | Significance level. Default 0.05. |
| confidence | number | no | Confidence level for intervals. Default 0.95. |
Compares group means on an outcome after adjusting for one numeric covariate. Data from file or rows (array of objects).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| file | string | no | — |
| rows | array | no | — |
| group_column | string | yes | — |
| outcome_column | string | yes | — |
| covariate_column | string | yes | — |
| alpha | number | no | Significance level. Default 0.05. |
Compares groups on 2+ numeric outcomes jointly (Wilks' lambda with Rao's F). Data from file or rows.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| file | string | no | — |
| rows | array | no | — |
| group_column | string | yes | — |
| outcome_columns | array | yes | — |
| alpha | number | no | Significance level. Default 0.05. |
Analytics-style breakdown: count, sum, mean, median, min, max or SD of a numeric column for each category of another column (e.g. total revenue by region, average order value by segment, top 10 products by profit, rows per status), sorted, top N. Without value_column it counts rows per category.…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| file | string | no | — |
| group_column | string | yes | — |
| value_column | string | no | — |
| stats | array | no | Which numbers to show (default count, sum, mean, median; just count without value_column). |
| sort_by | string | no | — |
| descending | boolean | no | — |
| top | integer | no | — |
CI for a mean (t distribution; values or file + column) or a proportion (Wilson; successes + n).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| type | string | yes | — |
| values | array | no | — |
| file | string | no | — |
| column | string | no | — |
| successes | integer | no | — |
| n | integer | no | — |
| confidence | number | no | Confidence level for intervals. Default 0.95. |
Two-sample t-test power. Give two of effect_size (Cohen's d), n_per_group, power; the third is solved. Normal approximation.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| effect_size | number | no | — |
| n_per_group | integer | no | — |
| power | number | no | — |
| alpha | number | no | Significance level. Default 0.05. |
| alternative | string | no | — |
Before you present findings (or put them in a report), pass the sentences of your answer that state a result. Each claim is checked against the computed results: Stats Explorer checks that every number appears in the results, that "significant" / "not significant" matches the p-value and α, and…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| claims | array | yes | Your findings, one sentence per entry, exactly as you will present them. |
| result_ids | array | no | Results the claims are about (default: the current analysis). |
| question | string | no | The user's question, if no analysis was started. |
Records the answer to a JEV CHECK NEEDED or APPROVAL NEEDED response. Cooperate mode: pass the packet_id and results (one per question id, with the probability Jev gave that the statement is TRUE, or its verdict label), and the name of the Jev tool you used. Orchestrated mode: only the…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| packet_id | string | yes | — |
| results | array | no | — |
| jev_tool | string | no | Name of the Jev tool you called. |
| model | string | no | Jev model reported by the tool, if any. |
| override_reason | string | no | Only when the user explicitly chose to go ahead without (or despite) the check: their reason. |
| approval | string | no | Orchestrated mode: the signed approval token from the orchestrator. |
Call this first for every new question. Read the user's request and choose mode: "hypothesis_test" when they want to know whether a difference, effect or relationship is real / significant / caused by something, or want to test a claim; "explore" when they want to describe, summarise, visualise,…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| question | string | yes | The user's request, in their own words. |
| mode | string | yes | "explore" for describing, comparing, ranking, summarising, charting or finding patterns (most analytics questions); "hypothesis_test" when asked whether a difference or relationship is significant / r |
| file | string | no | The data file the user named or attached to the chat (CSV, TSV, Excel or Parquet): pass its path or name, never its contents. Large files are fine. |
Step 1 of a hypothesis test. Fix the test, the data selection and H₀/H₁ BEFORE looking at results. test_arguments are the same fields the test takes (e.g. t_test: {type, file, column, group_column, group1, group2, equal_variances, mu0}; anova: {file, column, group_column}; ab_test: {metric, file,…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| test | string | yes | — |
| test_arguments | object | yes | Fields for the chosen test (file, columns, groups, type, method, ...). Not alpha/alternative. |
| h0 | string | no | Null hypothesis in plain words (a sensible default is proposed if omitted). |
| h1 | string | no | — |
| alternative | string | no | Decide from the question, never from the data. |
| alpha | number | no | — |
| confidence | number | no | — |
| confirmation_token | string | no | Only after the user has confirmed the proposed choices: the confirmation_token from the previous response. |
Step 2. Checks shape (skew, kurtosis, small samples), group sizes, outliers, expected counts, ANCOVA slope equality and missing values for the test fixed in step 1. If an assumption fails, revising the test now (state_hypotheses again) is principled.
No parameter schema on file.
Step 3. Standard deviation, IQR, coefficient of variation, outliers and variance ratio for the data in the plan.
No parameter schema on file.
Step 5. Runs exactly the test, columns and hypotheses the user confirmed in step 1 and returns the statistic, p-value, effect size, CI and verdict. Requires steps 1–4.
No parameter schema on file.
Step 6 (or standalone in an exploration). For rare-event questions (fraud, defects, diagnoses, churn): what share of positive flags is real (PPV). Give prevalence, sensitivity, specificity (0–1), or file + condition_column + test_column (0/1 columns). If the question is not about rare events, use…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| prevalence | number | no | — |
| sensitivity | number | no | — |
| specificity | number | no | — |
| file | string | no | — |
| condition_column | string | no | — |
| test_column | string | no | — |
| confirmation_token | string | no | Only after the user has confirmed the proposed choices: the confirmation_token from the previous response. |
Only steps 4 (power) and 6 (base rates) can be skipped, with a short reason that will appear in the report.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| step | yes | — | |
| reason | string | yes | — |
Shows the current analysis, which of the six steps are done, and the saved results (for the report).
No parameter schema on file.
Creates a chart for the columns the user asked to see: histogram (column), box_plot (column, optional group_column), scatter (x_column, y_column), bar (x_column categories, optional y_column mean), line (x_column, y_column), pie (x_column categories, optional y_column sum). The user confirms the…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| chart | string | yes | — |
| file | string | yes | — |
| column | string | no | — |
| x_column | string | no | — |
| y_column | string | no | — |
| group_column | string | no | — |
| bins | integer | no | — |
| title | string | no | — |
| save_to | string | no | — |
| overwrite | boolean | no | — |
| confirmation_token | string | no | Only after the user has confirmed the proposed choices: the confirmation_token from the previous response. |
Builds a report from results created in this session, only when the user asks for one. include = the result ids the user wants (default: the current analysis). The user confirms the sections. A hypothesis-test write-up (question, hypotheses, how it was tested, findings, verdict, base rates)…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| include | array | no | — |
| title | string | no | — |
| format | string | no | — |
| save_to | string | no | — |
| overwrite | boolean | no | — |
| confirmation_token | string | no | Only after the user has confirmed the proposed choices: the confirmation_token from the previous response. |
For a tool that answered STILL RUNNING: waits up to wait_seconds for the background job, then returns its result (the same result the tool would have returned) or its progress so far.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| job_id | string | yes | — |
| wait_seconds | number | no | — |
Stops a background job started by a tool that answered STILL RUNNING.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| job_id | string | yes | — |
Only when a tool answered LARGE FILE, and only after asking the user: record their choice for that file. choice "full" reads every row (exact results, can take a long time, runs in the background); "sample" reads about 1% of the rows in blocks spread across the file (quick, approximate).…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| file | string | yes | — |
| choice | string | yes | — |
| save_columns | boolean | no | — |
Lists or deletes the columns kept on disk from large files (see large_file_plan save_columns). action "list" or "clear" (all, or one file).
| Parameter | Type | Required | Notes |
|---|---|---|---|
| action | string | yes | — |
| file | string | no | — |
For open analytics questions such as "what drives revenue?", "what is related to churn time?" or "which factors matter for score?": ranks every other column (or the ones given) by how strongly it relates to a numeric target column. Pearson r for numeric columns, correlation ratio η plus the…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| file | string | yes | The data file the user named or attached (path or name; never its contents). |
| target | string | yes | The numeric outcome to explain, e.g. Revenue. |
| columns | array | no | Columns to consider (default: all others). |
| top | integer | no | — |
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install Stats Explorer or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
—
Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATrust score 99 / 100
Release 1.5.1 · scanned 28 Oct 2026
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score99
Findings0 threat · 0 capability
Capabilityminimal
Coveragesource
Verificationrepo
Surface digest99375579a17720a24cbc447587df264a37bc9760430533fcb4a777eedaed4d30
Score vectorthreat 100 → client 99
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | minimal | capability blast radius (minimal) — client exposure if the model is manipulated | −0 |
| verification-discount | repo | publisher verification (public source) — no provenance, but the source is public and inspectable | −1 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tool surfacenot available
Implementation sourceread
Package metadataread
Live transportnot available
No tools were enumerated, so prompt-injection, capability and toxic-flow analysis had no tool surface to inspect. To grade a package’s real runtime tools, scan the running server: --command "npx -y <package>".
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. minimal is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| No per-tool rows for this release — see the note below. | ||
No tool surface was enumerated, so there is nothing to attribute a capability to.
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
Integrityfirst-seen
Surface digest99375579a17720a2
Scanned2026-10-28
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Always latest — the install command names no version, so it installs the newest release when it runs; the current release on file is 1.5.1. Pin a release below and the command asks for exactly that one.
1.5.1
undated
latestNo release note was published with this version.
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