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MAGA2010/data-master

Auditable data analysis from raw files to reproducible PDF reports. Codex skill and cross-platform CLI.

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View on GitHub Add to my skills

Verified commit d6622807d8a8ae3e04ab24f234ec850932348dbd

67GitHub stars

28stars in 7 days

11skills in this repository

21commits in 30 days

Heat

7.8

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Star growth, last 24 hoursNot available
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Commits, last 30 daysNot available
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Individual skills in this repository

data-master /
data-cleaner skills/data-cleaner
data-explorer skills/data-explorer
data-profiler skills/data-profiler
data-router skills/data-router
data-transformer skills/data-transformer
data-validator skills/data-validator
deep-analyzer skills/deep-analyzer
report-generator skills/report-generator
statistical-analyzer skills/statistical-analyzer
visualizer skills/visualizer

How it works

Choose a skill

data-master

When your agent uses it

Analyze datasets with preserved raw inputs, explicit cleaning decisions, reproducible figures and PDF reports. Full statistics for one tabular file; descriptive time-series analysis; integrity profiling for text, image, audio, geographic and mixed inputs. Use for data cleaning, EDA, group comparisons, statistical reports, 跑数据 or 数据分析 with a traceable evidence trail.

What it loads into context

Frontmatter

108 tokens

Always available to the agent

SKILL.md

5,298 tokens

When the skill is activated

Reference documents

84,863 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

Contract
Data integrity contract
Three-stage pipeline
Routing
Workflow (agent-side)
Nature-grade standards (default)
Default-FAIL contract (from my-mathmodel-agent)
File map
Related files
Source hierarchy
Red lines

Scripts executed separately

bin/data-master.cjs
data_master/__init__.py
data_master/__main__.py
data_master/cli.py
examples/column_traps/make_traps.py
examples/column_traps/outputs/cleaned/clean_traps_dirty.py
examples/date_number_ambiguity/make_fixture.py
examples/date_number_ambiguity/outputs/cleaned/clean_session_export.py
examples/geo/make_geo.py
examples/iris/make_dirty_iris.py
examples/iris/outputs/cleaned/clean_iris_dirty.py
examples/media/make_images.py
examples/repeated_measures/outputs/cleaned/clean_measurements.py
examples/survey/make_survey.py
examples/survey/outputs/cleaned/clean_survey_responses.py
examples/text/make_corpus.py
examples/timeseries/make_series.py
package.json
scripts/_md2pdf_browser.py
scripts/_md2pdf_fpdf.py
scripts/_md2pdf_reportlab.py
scripts/assemble_profile_report.py
scripts/assemble_report.py
scripts/chart_decision.py
scripts/check_deps.py
scripts/cleaning_plan.py
scripts/dm_types.py
scripts/effect_size.py
scripts/execute_cleaning.py
scripts/export_html.py
scripts/final_audit.py
scripts/package_skill.py
scripts/pipeline_state.py
scripts/profile_geospatial.py
scripts/profile_media.py
scripts/profile_tabular.py
scripts/profile_text.py
scripts/profile_timeseries.py
scripts/render_chart.py
scripts/route_input.py
scripts/run_pipeline.py
scripts/sensitivity_analysis.py
scripts/state_keeper.py
scripts/stats_descriptive.py
scripts/stats_inferential.py
scripts/timeseries_analysis.py
scripts/validate_clean.py
scripts/verify_artifacts.py
tests/npm/launcher.test.cjs
tests/test_date_number_ambiguity.py
tests/test_distribution.py
tests/test_html_export.py
tests/test_pipeline_integrity.py
tests/test_pipeline_invariants.py
tests/test_reliability.py
tests/test_sqlite_input.py
tests/test_string_hygiene.py
tests/test_survey_case.py
tests/test_timeseries_holds.py

Files

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Read SKILL.md


# data-master — universal data processing pipeline

Turn **any data** into a **cleaned, audited, Nature-grade report + charts** with one command. The agent reads the data, decides modality, picks methods, runs the pipeline, and writes evidence-backed outputs. The user never has to choose a statistical test or a chart type.

## Contract

Installed CLI: `data-master analyze <file> --out <dir>`; use
`data-master doctor --json` to diagnose the environment and
`data-master verify <dir> --json` to check saved artifact hashes.
`data-master demo --out <new-dir>` runs a synthetic example, and
`data-master skill install` installs this skill atomically from the npm or
Python distribution. Existing
`python scripts/run_pipeline.py` commands below remain supported; installing
the CLI alone does not register the skill until `skill install` is run.

View the complete source on GitHub

data-cleaner

When your agent uses it

Turn profile.json into a deterministic cleaning plan, execute only the planned operations, and prove byte-for-byte reproducibility. Additive-only.

What it loads into context

Frontmatter

46 tokens

Always available to the agent

SKILL.md

775 tokens

When the skill is activated

Reference documents

10,888 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Scripts executed separately

../../scripts/cleaning_plan.py
../../scripts/execute_cleaning.py
../../scripts/execute_cleaning.py
../../scripts/execute_cleaning.py
../../scripts/execute_cleaning.py
../../scripts/execute_cleaning.py
../../scripts/execute_cleaning.py
../../scripts/execute_cleaning.py
../../scripts/execute_cleaning.py
../../scripts/execute_cleaning.py
../../scripts/validate_clean.py

Files

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../../scripts/execute_cleaning.pyruns41,471 bytes
../../scripts/execute_cleaning.pyruns41,471 bytes
../../scripts/execute_cleaning.pyruns41,471 bytes
../../scripts/execute_cleaning.pyruns41,471 bytes
../../scripts/execute_cleaning.pyruns41,471 bytes
../../scripts/execute_cleaning.pyruns41,471 bytes
../../scripts/execute_cleaning.pyruns41,471 bytes
../../scripts/execute_cleaning.pyruns41,471 bytes
../../scripts/execute_cleaning.pyruns41,471 bytes
../../scripts/validate_clean.pyruns7,944 bytes
SKILL.mdcore3,090 bytes
SKILL.mdcore3,090 bytes
../data-explorer/SKILL.mddoc2,248 bytes
../data-profiler/SKILL.mddoc2,573 bytes
../statistical-analyzer/SKILL.mddoc3,426 bytes

Read SKILL.md


# data-cleaner

**Stage 1c of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

Whenever `profile.json` exists. Cleaning is never ad hoc: the plan is the gate,
and only `plan["auto_actions"]` may execute.

## Inputs

View the complete source on GitHub

data-explorer

When your agent uses it

Descriptive analysis and EDA on cleaned data: distributions, missingness mechanism screen, correlations, categorical structure. No p-values for group comparisons.

What it loads into context

Frontmatter

48 tokens

Always available to the agent

SKILL.md

553 tokens

When the skill is activated

Reference documents

9,995 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Scripts executed separately

../../scripts/stats_descriptive.py

Files

../../SKILL.mddoc22,652 bytes
../../references/workflow-contract.mddoc12,227 bytes
../../scripts/stats_descriptive.pyruns10,700 bytes
../data-cleaner/SKILL.mddoc3,090 bytes
SKILL.mdcore2,248 bytes
SKILL.mdcore2,248 bytes
../statistical-analyzer/SKILL.mddoc3,426 bytes

Read SKILL.md


# data-explorer

**Stage 2a of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

After cleaning passes its gate. Run this before inferential statistics so the
test choice is informed by the actual distributions, not assumed.

## Inputs

View the complete source on GitHub

data-profiler

When your agent uses it

Profile a dataset without changing it: shape, dtypes, missingness, duplicates, distributions, anomalies, candidate group/target/time columns. Emits profile.json.

What it loads into context

Frontmatter

51 tokens

Always available to the agent

SKILL.md

629 tokens

When the skill is activated

Reference documents

9,803 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Scripts executed separately

../../scripts/profile_tabular.py

Files

../../SKILL.mddoc22,652 bytes
../../references/workflow-contract.mddoc12,227 bytes
../../scripts/profile_tabular.pyruns18,682 bytes
../data-cleaner/SKILL.mddoc3,090 bytes
SKILL.mdcore2,573 bytes
SKILL.mdcore2,573 bytes
../data-router/SKILL.mddoc2,586 bytes

Read SKILL.md


# data-profiler

**Stage 1b of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

After routing, before any cleaning plan exists. The profile is the only
artifact the rest of the pipeline is allowed to use to decide what a column *is*.

## Inputs

View the complete source on GitHub

data-router

When your agent uses it

Classify any input path into one of eight modalities and emit a route manifest. First stage of data-master; no statistics, no cleaning.

What it loads into context

Frontmatter

46 tokens

Always available to the agent

SKILL.md

633 tokens

When the skill is activated

Reference documents

9,665 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Scripts executed separately

../../scripts/check_deps.py

Files

../../SKILL.mddoc22,652 bytes
../../references/modality-routing.mddoc8,377 bytes
../../references/research-protocol.mddoc5,976 bytes
../../scripts/check_deps.pyruns5,907 bytes
../data-profiler/SKILL.mddoc2,573 bytes
SKILL.mdcore2,586 bytes
SKILL.mdcore2,586 bytes

Read SKILL.md


# data-router

**Stage 1a of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

The moment a user hands over a file, a folder, or a glob. Never skip it: a CSV
with a timestamp column and a free-text corpus are both `.csv`/`.txt`, and the wrong
branch produces a technically valid report about the wrong object.

## Inputs

View the complete source on GitHub

data-transformer

When your agent uses it

Derive analysis variables from cleaned data under an explicit, reversible transformation ledger. No silent recoding, no leakage.

What it loads into context

Frontmatter

42 tokens

Always available to the agent

SKILL.md

597 tokens

When the skill is activated

Reference documents

9,995 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Files

../../SKILL.mddoc22,652 bytes
../../references/workflow-contract.mddoc12,227 bytes
../data-cleaner/SKILL.mddoc3,090 bytes
SKILL.mdcore2,612 bytes
SKILL.mdcore2,612 bytes
SKILL.mdcore2,612 bytes
../statistical-analyzer/SKILL.mddoc3,426 bytes

Read SKILL.md


# data-transformer

**Stage 2b of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

When the analysis needs a derived variable (log transform, ratio, binning, unit
conversion, date parts) that is not already a column. Optional: a run with no derived
variables skips this skill and records that it did.

## Inputs

View the complete source on GitHub

data-validator

When your agent uses it

Run the 8 Nature statistical gates and the journal checklist against the finished artifacts. Writes audit/final_audit.md and refuses the label submission-ready on a mandatory FAIL.

What it loads into context

Frontmatter

52 tokens

Always available to the agent

SKILL.md

619 tokens

When the skill is activated

Reference documents

9,108 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Scripts executed separately

../../scripts/final_audit.py
../../scripts/sensitivity_analysis.py

Files

../../SKILL.mddoc22,652 bytes
../../references/journal-reporting-standards.mddoc8,248 bytes
../../references/nature-statistics-checklist.mddoc7,413 bytes
../../scripts/final_audit.pyruns41,457 bytes
../../scripts/sensitivity_analysis.pyruns24,729 bytes
SKILL.mdcore2,516 bytes
SKILL.mdcore2,516 bytes

Read SKILL.md


# data-validator

**Stage 3c of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

After the report exists. Also run it on a partially finished run to see which
gate is still open -- the audit reads artifacts, so missing ones surface as FAIL/N/A
rather than as an exception.

## Inputs

View the complete source on GitHub

deep-analyzer

When your agent uses it

Optional modelling stage: regression, mixed effects, clustering, dimensionality reduction, forecasting. Only runs when the design supports it; every model carries diagnostics.

What it loads into context

Frontmatter

49 tokens

Always available to the agent

SKILL.md

583 tokens

When the skill is activated

Reference documents

9,874 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Files

../../SKILL.mddoc22,652 bytes
../../references/workflow-contract.mddoc12,227 bytes
SKILL.mdcore2,522 bytes
SKILL.mdcore2,522 bytes
../statistical-analyzer/SKILL.mddoc3,426 bytes
../visualizer/SKILL.mddoc2,788 bytes

Read SKILL.md


# deep-analyzer

**Stage 2d of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

Only when the user asks for a model, or when a stated question cannot be answered
by a group comparison. Never the default: a model adds assumptions that must be
defended.

## Inputs

View the complete source on GitHub

report-generator

When your agent uses it

Assemble report.md, executive_summary.md and report.pdf from the artifacts. Every number is registered against its source; the PDF is the primary deliverable and the Markdown is the citable source.

What it loads into context

Frontmatter

57 tokens

Always available to the agent

SKILL.md

602 tokens

When the skill is activated

Reference documents

8,076 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Scripts executed separately

../../scripts/assemble_report.py

Files

../../SKILL.mddoc22,652 bytes
../../references/report-template-catalog.mddoc5,625 bytes
../../scripts/assemble_report.pyruns95,409 bytes
../data-validator/SKILL.mddoc2,516 bytes
SKILL.mdcore2,517 bytes
SKILL.mdcore2,517 bytes
../visualizer/SKILL.mddoc2,788 bytes

Read SKILL.md


# report-generator

**Stage 3b of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

After figures render. Never before the statistical gates have artifacts to read --
a report written first is a report that has to be edited to fit the numbers.

## Inputs

View the complete source on GitHub

statistical-analyzer

When your agent uses it

Choose and run the inferential test from the data structure, with effect sizes, CIs, multiplicity correction and explicit rejected alternatives. Refuses to report a p-value it cannot estimate.

What it loads into context

Frontmatter

57 tokens

Always available to the agent

SKILL.md

825 tokens

When the skill is activated

Reference documents

8,326 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Scripts executed separately

../../scripts/stats_inferential.py

Files

../../SKILL.mddoc22,652 bytes
../../references/nature-statistics-checklist.mddoc7,413 bytes
../../scripts/stats_inferential.pyruns81,514 bytes
../data-explorer/SKILL.mddoc2,248 bytes
SKILL.mdcore3,426 bytes
SKILL.mdcore3,426 bytes
../visualizer/SKILL.mddoc2,788 bytes

Read SKILL.md


# statistical-analyzer

**Stage 2c of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

When the data has a comparison or association structure. If there is no natural
grouping variable, run descriptive analysis only and say so -- inventing a grouping
variable is worse than reporting none.

## Inputs

View the complete source on GitHub

visualizer

When your agent uses it

Decide and render one figure per argument: chart type chosen against alternatives, title states the finding, caption defines the encoding, body gives fact -> interpretation -> implication.

What it loads into context

Frontmatter

51 tokens

Always available to the agent

SKILL.md

654 tokens

When the skill is activated

Reference documents

8,521 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

When to invoke
Inputs
Outputs
Commands
Gate (default FAIL)
Failure modes seen in real runs
Do not
Upstream / downstream

Scripts executed separately

../../scripts/chart_decision.py
../../scripts/render_chart.py

Files

../../SKILL.mddoc22,652 bytes
../../references/chart-decision-catalog.mddoc6,921 bytes
../../scripts/chart_decision.pyruns22,301 bytes
../../scripts/render_chart.pyruns22,265 bytes
../report-generator/SKILL.mddoc2,517 bytes
../statistical-analyzer/SKILL.mddoc3,426 bytes
SKILL.mdcore2,788 bytes
SKILL.mdcore2,788 bytes

Read SKILL.md


# visualizer

**Stage 3a of the data-master pipeline.** The router in `../../SKILL.md` owns the
end-to-end run; this skill owns one stage and one gate. Read the parent contract before
changing anything here: the invariants in `../../SKILL.md` (additive-only, types decided
once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

## When to invoke

After statistics exist. The decision layer runs even when no figure is produced --
"no chart says anything a table does not" is itself a recorded decision.

## Inputs

View the complete source on GitHub

Skills included

data-master
data-cleaner
data-explorer
data-profiler
data-router
data-transformer
data-validator
deep-analyzer
report-generator
statistical-analyzer
visualizer

Repository facts

License: MIT
Created: Oct 17, 2026
Last pushed: Nov 2, 2026
Forks: 1
Open issues: 0
Project activity: Active

Before you install

Network or broad shell

Automated signals, not a guarantee

Checked Nov 4, 2026

Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.

Show 56 notes

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11 broad permissionsin 1 file: SKILL.md:1
7 dangerous shell commandsin 1 file: tests/npm/launcher.test.cjs:109
153 network referencesin 18 files: references/journal-reporting-standards.json:8 docs/LAUNCH_KIT.md:33 references/research-protocol.md:43 and 15 more
1 obfuscation signalin 1 file: examples/media/make_images.py:33
No instruction overrides
79 executable scriptsin 59 files: scripts/execute_cleaning.py:1 scripts/assemble_report.py:1 scripts/chart_decision.py:1 and 56 more
19 references to secretsin 9 files: data_master/cli.py:29 scripts/execute_cleaning.py:497 scripts/run_pipeline.py:120 and 6 more
No requests to vote in Hot

Show all 270 signals by file

`.github/ISSUE_TEMPLATE/config.yml`

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contact_links:   - name: Security report     url: https://github.com/MAGA2010/data-master/security/policy

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- name: Security report     url: https://github.com/MAGA2010/data-master/security/policy     about: Report vulnerabilities and accidental data disclosure privately.

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url: https://github.com/MAGA2010/data-master/security/policy

`CHANGELOG.md`

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[0.2.0]: https://github.com/MAGA2010/data-master/releases/tag/v0.2.0

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[0.3.0]: https://github.com/MAGA2010/data-master/releases/tag/v0.3.0

`CITATION.cff`

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- family-names: Wang     given-names: S. repository-code: "https://github.com/MAGA2010/data-master"

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given-names: S. repository-code: "https://github.com/MAGA2010/data-master" url: "https://github.com/MAGA2010/data-master"

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repository-code: "https://github.com/MAGA2010/data-master"

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url: "https://github.com/MAGA2010/data-master"

`EVALUATION.md`

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CI results are public in [GitHub Actions](https://github.com/MAGA2010/data-master/actions).

`README.md`

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[![CI](https://github.com/MAGA2010/data-master/actions/workflows/ci.yml/badge.svg)](https://github.com/MAGA2010/data-master/actions/workflows/ci.yml)

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[![Release](https://img.shields.io/github/v/release/MAGA2010/data-master)](https://github.com/MAGA2010/data-master/releases)

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[![GitHub stars](https://img.shields.io/github/stars/MAGA2010/data-master?style=flat)](https://github.com/MAGA2010/data-master/stargazers)

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[![Python](https://img.shields.io/badge/python-3.11%20%7C%203.12%20%7C%203.13-3776AB)](https://www.python.org/)

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[![License: MIT](https://img.shields.io/badge/license-MIT-2EA44F)](LICENSE)

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[![Codex Skill](https://img.shields.io/badge/Codex-skill-111827)](SKILL.md)

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If the verified demo is useful, [star the repository](https://github.com/MAGA2010/data-master) so other researchers and analysts can find it.

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git clone https://github.com/MAGA2010/data-master.git

`README.zh-CN.md`

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如果这个可核验 Demo 对你有帮助，欢迎[为仓库点 Star](https://github.com/MAGA2010/data-master)，让更多研究者和数据分析者发现它。

`SKILL.md`

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Bash(python *), Bash(py *), PowerShell(python *), PowerShell(py *), WebSearch, WebFetch

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Bash(python *), Bash(py *), PowerShell(python *), PowerShell(py *), WebSearch, WebFetch

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Bash(python *), Bash(py *), PowerShell(python *), PowerShell(py *), WebSearch, WebFetch

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Bash(python *), Bash(py *), PowerShell(python *), PowerShell(py *), WebSearch, WebFetch

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Bash(python *), Bash(py *), PowerShell(python *), PowerShell(py *), WebSearch, WebFetch

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Bash(python *), Bash(py *), PowerShell(python *), PowerShell(py *), WebSearch, WebFetch

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Bash(python *), Bash(py *), PowerShell(python *), PowerShell(py *), WebSearch, WebFetch

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Bash(python *), Bash(py *), PowerShell(python *), PowerShell(py *), WebSearch, WebFetch

`bin/data-master.cjs`

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```
#!/usr/bin/env node
```

`data_master/init.py`

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`data_master/main.py`

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`data_master/cli.py`

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```

`docs/LAUNCH_KIT.md`

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> npm archive, `.skill` archive, and checksums. > > Repository: https://github.com/MAGA2010/data-master

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> > Repository: https://github.com/MAGA2010/data-master >

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> Repository: https://github.com/MAGA2010/data-master

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> The repository has a 24-second demo, five reproducible case studies, a public > evaluation dashboard, and release artifacts for the CLI and skill package: > https://github.com/MAGA2010/data-master

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> evaluation dashboard, and release artifacts for the CLI and skill package: > https://github.com/MAGA2010/data-master >

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> https://github.com/MAGA2010/data-master

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> release artifacts. > > Explore it or contribute a failure case: https://github.com/MAGA2010/data-master

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> > Explore it or contribute a failure case: https://github.com/MAGA2010/data-master >

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> Explore it or contribute a failure case: https://github.com/MAGA2010/data-master

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> I built data-master: an open-source Codex skill + CLI that turns messy datasets > into auditable PDF reports. Reproducible cleaning, unit-aware stats, sensitivity > checks, and evidence you can verify. v0.3.0: https://github.com/MAGA2010/

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> checks, and evidence you can verify. v0.3.0: https://github.com/MAGA2010/data-master

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- Repository: https://github.com/MAGA2010/data-master

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- Release: https://github.com/MAGA2010/data-master/releases/tag/v0.3.0

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- Date-or-number benchmark: https://github.com/MAGA2010/data-master/blob/main/docs/case-studies/identifier-traps.md#benchmark-date-or-number

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- Synthetic survey: https://github.com/MAGA2010/data-master/blob/main/docs/case-studies/survey-data.md

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- Evaluation: https://github.com/MAGA2010/data-master/blob/main/EVALUATION.md

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- Identifier traps: https://github.com/MAGA2010/data-master/blob/main/docs/case-studies/identifier-traps.md

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- Repeated rows: https://github.com/MAGA2010/data-master/blob/main/docs/case-studies/unit-aware-comparison.md

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- Audit refusal: https://github.com/MAGA2010/data-master/blob/main/docs/case-studies/audit-refusal.md

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- Good first issues: https://github.com/MAGA2010/data-master/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22

`examples/column_traps/make_traps.py`

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`examples/column_traps/outputs/cleaned/clean_traps_dirty.py`

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#!/usr/bin/env python
```

`examples/date_number_ambiguity/make_fixture.py`

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`examples/date_number_ambiguity/outputs/audit/final_audit.json`

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"label": "Nature Portfolio",     "reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",     "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

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"reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",     "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",     "checklist": "Nature Portfolio reporting s

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"url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

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"checklist": "Nature Portfolio reporting standards (design checklist AUTHOR_INPUT_NEEDED)",     "checklist_url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",     "design": "generic",

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"checklist_url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

`examples/date_number_ambiguity/outputs/cleaned/clean_session_export.py`

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#!/usr/bin/env python
```

`examples/geo/make_geo.py`

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`examples/iris/make_dirty_iris.py`

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#!/usr/bin/env python
```

`examples/iris/outputs/audit/final_audit.json`

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"label": "Nature Portfolio",     "reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",     "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 111

"reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",     "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",     "checklist": "Nature Portfolio reporting s

Network accessline 112

"url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 113

"checklist": "Nature Portfolio reporting standards (design checklist AUTHOR_INPUT_NEEDED)",     "checklist_url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",     "design": "generic",

Network accessline 114

"checklist_url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

`examples/iris/outputs/cleaned/clean_iris_dirty.py`

Executable scriptline 1
```
#!/usr/bin/env python
```

`examples/media/make_images.py`

Executable scriptline 1
```
Executable file
```

Possible obfuscationline 33

(OUT / "corrupt.jpg").write_bytes(b"\xff\xd8\xff\xe0not-really-a-jpeg")

`examples/repeated_measures/outputs/audit/final_audit.json`

Network accessline 106

"label": "Nature Portfolio",     "reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",     "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 107

"reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",     "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",     "checklist": "Nature Portfolio reporting s

Network accessline 108

"url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 109

"checklist": "Nature Portfolio reporting standards (design checklist AUTHOR_INPUT_NEEDED)",     "checklist_url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",     "design": "generic",

Network accessline 110

"checklist_url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

`examples/repeated_measures/outputs/cleaned/clean_measurements.py`

Executable scriptline 1
```
#!/usr/bin/env python
```

`examples/survey/make_survey.py`

Executable scriptline 1
```
Executable file
```

`examples/survey/outputs/audit/final_audit.json`

Network accessline 108

"label": "Nature Portfolio",     "reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",     "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 109

"reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",     "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",     "checklist": "STROBE",

Network accessline 110

"url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 111

"checklist": "STROBE",     "checklist_url": "https://www.strobe-statement.org/",     "design": "observational",

Network accessline 112

"checklist_url": "https://www.strobe-statement.org/",

`examples/survey/outputs/cleaned/clean_survey_responses.py`

Executable scriptline 1
```
#!/usr/bin/env python
```

`examples/text/make_corpus.py`

Executable scriptline 1
```
Executable file
```

`examples/timeseries/make_series.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`package.json`

Executable scriptline 1
```
Executable file
```

Network accessline 6

"publishConfig": {"access": "public"},   "license": "MIT",   "homepage": "https://github.com/MAGA2010/data-master#readme",

Network accessline 7

"license": "MIT",   "homepage": "https://github.com/MAGA2010/data-master#readme",   "bugs": {"url": "https://github.com/MAGA2010/data-master/issues"},

Network accessline 8

"homepage": "https://github.com/MAGA2010/data-master#readme",

Network accessline 9

"bugs": {"url": "https://github.com/MAGA2010/data-master/issues"},

Network accessline 10

"keywords": ["codex-skill", "agent-skills", "data-analysis", "reproducible-research", "statistics"],   "repository": {"type": "git", "url": "https://github.com/MAGA2010/data-master.git"},   "engines": {"node": ">=20"},

Network accessline 11

"repository": {"type": "git", "url": "https://github.com/MAGA2010/data-master.git"},

`pyproject.toml`

Network accessline 21

[project.urls] Repository = "https://github.com/MAGA2010/data-master"

Network accessline 22

Repository = "https://github.com/MAGA2010/data-master"

Network accessline 23

Issues = "https://github.com/MAGA2010/data-master/issues"

Network accessline 24

Changelog = "https://github.com/MAGA2010/data-master/blob/main/CHANGELOG.md"

Network accessline 25

Documentation = "https://github.com/MAGA2010/data-master#readme"

`references/journal-reporting-standards.json`

Network accessline 8

"checklist": "CONSORT 2025",       "fallback_checklist": "CONSORT 2010",       "url": "https://www.consort-statement.org/",

Network accessline 9

"fallback_checklist": "CONSORT 2010",       "url": "https://www.consort-statement.org/",       "detect": [

Network accessline 10

"url": "https://www.consort-statement.org/",

Network accessline 31

"label": "Observational study",       "checklist": "STROBE",       "url": "https://www.strobe-statement.org/",

Network accessline 32

"checklist": "STROBE",       "url": "https://www.strobe-statement.org/",       "detect": [

Network accessline 33

"url": "https://www.strobe-statement.org/",

Network accessline 50

"label": "Systematic review / meta-analysis",       "checklist": "PRISMA 2020",       "url": "https://www.prisma-statement.org/",

Network accessline 51

"checklist": "PRISMA 2020",       "url": "https://www.prisma-statement.org/",       "detect": [

Network accessline 52

"url": "https://www.prisma-statement.org/",

Network accessline 70

"label": "Meta-analysis of observational studies",       "checklist": "MOOSE",       "url": "https://doi.org/10.1001/jama.283.15.2008",

Network accessline 71

"checklist": "MOOSE",       "url": "https://doi.org/10.1001/jama.283.15.2008",       "detect": [

Network accessline 72

"url": "https://doi.org/10.1001/jama.283.15.2008",

Network accessline 88

"label": "Diagnostic accuracy study",       "checklist": "STARD 2015",       "url": "https://www.equator-network.org/reporting-guidelines/stard/",

Network accessline 89

"checklist": "STARD 2015",       "url": "https://www.equator-network.org/reporting-guidelines/stard/",       "detect": [

Network accessline 90

"url": "https://www.equator-network.org/reporting-guidelines/stard/",

Network accessline 106

"label": "Animal / preclinical study",       "checklist": "ARRIVE 2.0",       "url": "https://arriveguidelines.org/",

Network accessline 107

"checklist": "ARRIVE 2.0",       "url": "https://arriveguidelines.org/",       "detect": [

Network accessline 108
```
"url": "https://arriveguidelines.org/",
```

Network accessline 125

"label": "Quality improvement study",       "checklist": "SQUIRE 2.0",       "url": "https://www.squire-statement.org/",

Network accessline 126

"checklist": "SQUIRE 2.0",       "url": "https://www.squire-statement.org/",       "detect": [

Network accessline 127

"url": "https://www.squire-statement.org/",

Network accessline 142

"label": "Case report",       "checklist": "CARE",       "url": "https://www.care-statement.org/",

Network accessline 143

"checklist": "CARE",       "url": "https://www.care-statement.org/",       "detect": [

Network accessline 144

"url": "https://www.care-statement.org/",

Network accessline 158

"label": "Analytical / measurement study",       "checklist": "Nature Portfolio reporting standards",       "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 159

"checklist": "Nature Portfolio reporting standards",       "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",       "detect": [

Network accessline 160

"url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 177

"label": "Design not declared",       "checklist": "Nature Portfolio reporting standards (design checklist AUTHOR_INPUT_NEEDED)",       "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 178

"checklist": "Nature Portfolio reporting standards (design checklist AUTHOR_INPUT_NEEDED)",       "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",       "detect": [],

Network accessline 179

"url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 191

"label": "Nature Portfolio",       "reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",       "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 192

"reporting_standards": "Nature Portfolio reporting standards + the 8 data-master statistical gates",       "url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",       "statistics_guide": "https://www.natur

Network accessline 193

"url": "https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards",

Network accessline 194

"statistics_guide": "https://www.nature.com/nature-portfolio/editorial-policies/statistics",

Network accessline 215

"label": "Science (AAAS)",       "reporting_standards": "Science Journals editorial policies: statistics, data and code availability",       "url": "https://www.science.org/content/page/science-journals-editorial-policies",

Network accessline 216

"reporting_standards": "Science Journals editorial policies: statistics, data and code availability",       "url": "https://www.science.org/content/page/science-journals-editorial-policies",       "statistics_guide": "https://www.science.or

Network accessline 217

"url": "https://www.science.org/content/page/science-journals-editorial-policies",

Network accessline 218

"statistics_guide": "https://www.science.org/content/page/science-journals-editorial-policies",

Network accessline 238

"label": "The Lancet",       "reporting_standards": "The Lancet statistical reporting + EQUATOR checklist for the study design",       "url": "https://www.thelancet.com/information-for-authors",

Network accessline 239

"reporting_standards": "The Lancet statistical reporting + EQUATOR checklist for the study design",       "url": "https://www.thelancet.com/information-for-authors",       "statistics_guide": "https://www.thelancet.com/information-for-autho

Network accessline 240

"url": "https://www.thelancet.com/information-for-authors",

Network accessline 241

"statistics_guide": "https://www.thelancet.com/information-for-authors",

Network accessline 263

"label": "New England Journal of Medicine",       "reporting_standards": "NEJM statistical reporting standards + EQUATOR checklist for the study design",       "url": "https://www.nejm.org/author-center/new-manuscripts",

Network accessline 264

"reporting_standards": "NEJM statistical reporting standards + EQUATOR checklist for the study design",       "url": "https://www.nejm.org/author-center/new-manuscripts",       "statistics_guide": "https://www.nejm.org/author-center/new-man

Network accessline 265

"url": "https://www.nejm.org/author-center/new-manuscripts",

Network accessline 266

"statistics_guide": "https://www.nejm.org/author-center/new-manuscripts",

`references/journal-reporting-standards.md`

Network accessline 45

- Nature Portfolio reporting standards -- <https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards>

Network accessline 45

- Nature Portfolio reporting standards -- <https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards>

Network accessline 46

- Nature Portfolio statistics -- <https://www.nature.com/nature-portfolio/editorial-policies/statistics>

Network accessline 46

- Nature Portfolio statistics -- <https://www.nature.com/nature-portfolio/editorial-policies/statistics>

Network accessline 47

- NEJM author center -- <https://www.nejm.org/author-center/new-manuscripts>

Network accessline 47

- NEJM author center -- <https://www.nejm.org/author-center/new-manuscripts>

Network accessline 48

- Science Journals editorial policies -- <https://www.science.org/content/page/science-journals-editorial-policies>

Network accessline 48

- Science Journals editorial policies -- <https://www.science.org/content/page/science-journals-editorial-policies>

Network accessline 49

- The Lancet information for authors -- <https://www.thelancet.com/information-for-authors>

Network accessline 49

- The Lancet information for authors -- <https://www.thelancet.com/information-for-authors>

Network accessline 50

- EQUATOR Network (index of all checklists) -- <https://www.equator-network.org/>

Network accessline 50

- EQUATOR Network (index of all checklists) -- <https://www.equator-network.org/>

`references/nature-statistics-checklist.md`

Network accessline 44

Method basis: [National Academies missing-data guidance](https://www.ncbi.nlm.nih.gov/books/NBK209904/pdf/Bookshelf_NBK209904.pdf).

Network accessline 44

Method basis: [National Academies missing-data guidance](https://www.ncbi.nlm.nih.gov/books/NBK209904/pdf/Bookshelf_NBK209904.pdf).

Network accessline 44

Method basis: [National Academies missing-data guidance](https://www.ncbi.nlm.nih.gov/books/NBK209904/pdf/Bookshelf_NBK209904.pdf).

`references/research-protocol.md`

Network accessline 43

"extension": ".fasta",   "candidate_libraries": [     {"name": "Bio", "installed": true, "version": "1.81", "source": "https://biopython.org/"},

Network accessline 43

"extension": ".fasta",   "candidate_libraries": [     {"name": "Bio", "installed": true, "version": "1.81", "source": "https://biopython.org/"},

Network accessline 44

"candidate_libraries": [     {"name": "Bio", "installed": true, "version": "1.81", "source": "https://biopython.org/"},     {"name": "pyfaidx", "installed": true, "version": "0.7", "source": "https://github.com/mdshw5/pyfaidx"}

Network accessline 44

"candidate_libraries": [     {"name": "Bio", "installed": true, "version": "1.81", "source": "https://biopython.org/"},     {"name": "pyfaidx", "installed": true, "version": "0.7", "source": "https://github.com/mdshw5/pyfaidx"}

Network accessline 45

{"name": "Bio", "installed": true, "version": "1.81", "source": "https://biopython.org/"},

Network accessline 45

{"name": "Bio", "installed": true, "version": "1.81", "source": "https://biopython.org/"},

Network accessline 46

{"name": "pyfaidx", "installed": true, "version": "0.7", "source": "https://github.com/mdshw5/pyfaidx"}

Network accessline 46

{"name": "pyfaidx", "installed": true, "version": "0.7", "source": "https://github.com/mdshw5/pyfaidx"}

Network accessline 51

],   "web_sources": [     "https://en.wikipedia.org/wiki/FASTA_format",

Network accessline 51

],   "web_sources": [     "https://en.wikipedia.org/wiki/FASTA_format",

Network accessline 52

"web_sources": [     "https://en.wikipedia.org/wiki/FASTA_format",     "https://biopython.org/wiki/SeqIO"

Network accessline 52

"web_sources": [     "https://en.wikipedia.org/wiki/FASTA_format",     "https://biopython.org/wiki/SeqIO"

Network accessline 53

"https://en.wikipedia.org/wiki/FASTA_format",

Network accessline 53

"https://en.wikipedia.org/wiki/FASTA_format",

Network accessline 54
```
"https://biopython.org/wiki/SeqIO"
```
Network accessline 54
```
"https://biopython.org/wiki/SeqIO"
```

`scripts/_md2pdf_browser.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```
Access to secretsline 309
```
[Credential-related source omitted]
```

`scripts/_md2pdf_fpdf.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/_md2pdf_reportlab.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/assemble_profile_report.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/assemble_report.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```
Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/chart_decision.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```
Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/check_deps.py`

Executable scriptline 1
```
Executable file
```
Executable scriptline 1
```
Executable file
```

`scripts/cleaning_plan.py`

Executable scriptline 1
```
Executable file
```
Executable scriptline 1
```
Executable file
```

`scripts/dm_types.py`

Executable scriptline 1
```
Executable file
```

`scripts/effect_size.py`

Executable scriptline 1
```
Executable file
```

`scripts/execute_cleaning.py`

Executable scriptline 1
```
#!/usr/bin/env python
```
Executable scriptline 1
```
#!/usr/bin/env python
```
Executable scriptline 1
```
Executable file; content unavailable
```
Executable scriptline 1
```
Executable file; content unavailable
```
Executable scriptline 1
```
Executable file; content unavailable
```
Executable scriptline 1
```
Executable file; content unavailable
```
Executable scriptline 1
```
Executable file; content unavailable
```
Executable scriptline 1
```
Executable file; content unavailable
```
Executable scriptline 1
```
Executable file; content unavailable
```
Executable scriptline 1
```
Executable file; content unavailable
```
Access to secretsline 497
```
[Credential-related source omitted]
```
Access to secretsline 497
```
[Credential-related source omitted]
```
Access to secretsline 639
```
[Credential-related source omitted]
```
Access to secretsline 639
```
[Credential-related source omitted]
```

`scripts/export_html.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/final_audit.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```
Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/package_skill.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/pipeline_state.py`

Executable scriptline 1
```
Executable file
```

`scripts/profile_geospatial.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/profile_media.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/profile_tabular.py`

Executable scriptline 1
```
Executable file
```
Executable scriptline 1
```
Executable file
```

`scripts/profile_text.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/profile_timeseries.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/render_chart.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```
Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/route_input.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/run_pipeline.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```
Access to secretsline 120
```
[Credential-related source omitted]
```
Access to secretsline 610
```
[Credential-related source omitted]
```
Access to secretsline 1174
```
[Credential-related source omitted]
```

`scripts/sensitivity_analysis.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```
Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/state_keeper.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/stats_descriptive.py`

Executable scriptline 1
```
Executable file
```
Executable scriptline 1
```
Executable file
```

`scripts/stats_inferential.py`

Executable scriptline 1
```
#!/usr/bin/env python
```
Executable scriptline 1
```
#!/usr/bin/env python
```

`scripts/timeseries_analysis.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`scripts/validate_clean.py`

Executable scriptline 1
```
Executable file
```
Executable scriptline 1
```
Executable file
```

`scripts/verify_artifacts.py`

Executable scriptline 1
```
#!/usr/bin/env python3
```

`skills/statistical-analyzer/SKILL.md`

Network accessline 59

Method basis: [lme4 model-comparison guidance](https://lme4.github.io/lme4/articles/lmer.pdf).

Network accessline 59

Method basis: [lme4 model-comparison guidance](https://lme4.github.io/lme4/articles/lmer.pdf).

Network accessline 59

Method basis: [lme4 model-comparison guidance](https://lme4.github.io/lme4/articles/lmer.pdf).

Network accessline 59

Method basis: [lme4 model-comparison guidance](https://lme4.github.io/lme4/articles/lmer.pdf).

Network accessline 59

Method basis: [lme4 model-comparison guidance](https://lme4.github.io/lme4/articles/lmer.pdf).

Network accessline 59

Method basis: [lme4 model-comparison guidance](https://lme4.github.io/lme4/articles/lmer.pdf).

`tests/npm/launcher.test.cjs`

Executable scriptline 1
```
Executable file
```

Dangerous shell operationline 109

assert.match(exec('--version'), /0\.3\.0/);

Dangerous shell operationline 111

exec('skill', 'install', '--target', skill);

Dangerous shell operationline 113

assert.equal(JSON.parse(exec('doctor', '--json')).healthy, true);

Dangerous shell operationline 115
```
exec('demo', '--out', out);
```

Dangerous shell operationline 116

assert.equal(JSON.parse(exec('verify', out, '--json')).valid, true);

Dangerous shell operationline 117

exec('resume', path.join(out, 'state.json'));

Dangerous shell operationline 118

assert.equal(JSON.parse(exec('verify', out, '--json')).valid, true);

`tests/test_date_number_ambiguity.py`

Executable scriptline 1
```
Executable file
```

`tests/test_distribution.py`

Executable scriptline 1
```
Executable file
```
Access to secretsline 50
```
[Credential-related source omitted]
```

`tests/test_html_export.py`

Executable scriptline 1
```
Executable file
```
Access to secretsline 40
```
[Credential-related source omitted]
```
Access to secretsline 57
```
[Credential-related source omitted]
```

`tests/test_pipeline_integrity.py`

Executable scriptline 1
```
Executable file
```
Access to secretsline 27
```
[Credential-related source omitted]
```

`tests/test_pipeline_invariants.py`

Executable scriptline 1
```
Executable file
```

`tests/test_reliability.py`

Executable scriptline 1
```
Executable file
```

`tests/test_sqlite_input.py`

Executable scriptline 1
```
Executable file
```

`tests/test_string_hygiene.py`

Executable scriptline 1
```
Executable file
```
Access to secretsline 74
```
[Credential-related source omitted]
```

`tests/test_survey_case.py`

Executable scriptline 1
```
Executable file
```

`tests/test_timeseries_holds.py`

Executable scriptline 1
```
Executable file
```
Access to secretsline 47
```
[Credential-related source omitted]
```
Access to secretsline 55
```
[Credential-related source omitted]
```

How the signals fit together

Facts from a static read of the files at the pinned commit. They describe what the code does, not why.

Some files could not be read; the facts below still hold.

Signal confidence: 192 facts

Network access in prose of documentationCHANGELOG.md:116Low confidence
Network access in prose of documentationCHANGELOG.md:117Low confidence
Network access in prose of documentationEVALUATION.md:22Low confidence
Network access in prose of documentationREADME.md:5Low confidence
Network access in prose of documentationREADME.md:6Low confidence
Network access in prose of documentationREADME.md:7Low confidence
Network access in prose of documentationREADME.md:8Low confidence
Network access in prose of documentationREADME.md:9Low confidence
Network access in prose of documentationREADME.md:10Low confidence
Network access in prose of documentationREADME.md:35Low confidence
Network access in code of documentationREADME.md:145Low confidence
Network access in prose of documentationREADME.zh-CN.md:25Low confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Broad permissions in the frontmatter of the SKILL.mdSKILL.md:1High confidence
Executable script in code of a file that runsbin/data-master.cjs:1High confidence
Executable script in prose of a file that runsdata_master/__init__.py:1Low confidence
Executable script in code of a file that runsdata_master/__main__.py:1High confidence
Executable script in prose of a file that runsdata_master/cli.py:1Low confidence
Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuedata_master/cli.py:29Low confidence
Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuedata_master/cli.py:45Low confidence
Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuedata_master/cli.py:64Low confidence
Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuedata_master/cli.py:120Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:33Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:34Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:35Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:59Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:60Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:61Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:80Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:81Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:82Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:88Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:90Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:103Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:104Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:105Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:106Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:107Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:108Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:109Low confidence
Network access in prose of documentationdocs/LAUNCH_KIT.md:110Low confidence

and 142 more

Works with

Agent compatibility, from the files in this repository
Agent	Status	Evidence	Install to
Claude Code	Compatible	Standard SKILL.md SKILL.md	`.claude/skills/<skill-name>/`
Codex	Compatible	Standard SKILL.md SKILL.md	`.agents/skills/<skill-name>/`
Cursor	Compatible	Standard SKILL.md SKILL.md	`.cursor/skills/<skill-name>/`
Gemini CLI	Compatible	Standard SKILL.md SKILL.md	`.gemini/skills/<skill-name>/`
GitHub Copilot	Compatible	Standard SKILL.md SKILL.md	`.github/skills/<skill-name>/`
OpenCode	Compatible	Standard SKILL.md SKILL.md	`.opencode/skills/<skill-name>/`

Native The repository ships a manifest, folder or frontmatter field for this agent.
Compatible A standard SKILL.md that this agent can read.
Unknown No evidence either way.

Derived automatically from paths, manifests and frontmatter. Not tested on each agent.

MAGA2010/data-master

History

SKILL.md

skills/data-cleaner/SKILL.md

skills/data-explorer/SKILL.md

skills/data-profiler/SKILL.md

skills/data-router/SKILL.md

skills/data-transformer/SKILL.md

skills/data-validator/SKILL.md

skills/deep-analyzer/SKILL.md

skills/report-generator/SKILL.md

skills/statistical-analyzer/SKILL.md

skills/visualizer/SKILL.md

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Individual skills in this repository

How it works

data-master

Frontmatter

SKILL.md

Reference documents

The playbook

Scripts executed separately

Files

data-cleaner

Frontmatter

SKILL.md

Reference documents

The playbook

Scripts executed separately

Files

data-explorer

Frontmatter

SKILL.md

Reference documents

The playbook

Scripts executed separately

Files

data-profiler

Frontmatter

SKILL.md

Reference documents

The playbook

Scripts executed separately

Files

data-router

Frontmatter

SKILL.md

Reference documents

The playbook

Scripts executed separately

Files

data-transformer

Frontmatter

SKILL.md

Reference documents

The playbook

Files

data-validator

Frontmatter

SKILL.md

Reference documents

The playbook

Scripts executed separately

Files

deep-analyzer

Frontmatter

SKILL.md

Reference documents

The playbook

Files

report-generator

Frontmatter

SKILL.md

Reference documents

The playbook

Scripts executed separately

Files

statistical-analyzer

`SKILL.md`

`skills/data-cleaner/SKILL.md`

`skills/data-explorer/SKILL.md`

`skills/data-profiler/SKILL.md`

`skills/data-router/SKILL.md`

`skills/data-transformer/SKILL.md`

`skills/data-validator/SKILL.md`

`skills/deep-analyzer/SKILL.md`

`skills/report-generator/SKILL.md`

`skills/statistical-analyzer/SKILL.md`

`skills/visualizer/SKILL.md`

`.github/ISSUE_TEMPLATE/config.yml`

`CHANGELOG.md`

`CITATION.cff`

`EVALUATION.md`

`README.md`

`README.zh-CN.md`

`SKILL.md`

`bin/data-master.cjs`

`data_master/init.py`

`data_master/main.py`

`data_master/cli.py`

`docs/LAUNCH_KIT.md`

`examples/column_traps/make_traps.py`

`examples/column_traps/outputs/cleaned/clean_traps_dirty.py`

`examples/date_number_ambiguity/make_fixture.py`

`examples/date_number_ambiguity/outputs/audit/final_audit.json`

`examples/date_number_ambiguity/outputs/cleaned/clean_session_export.py`

`examples/geo/make_geo.py`

`examples/iris/make_dirty_iris.py`

`examples/iris/outputs/audit/final_audit.json`

`examples/iris/outputs/cleaned/clean_iris_dirty.py`

`examples/media/make_images.py`

`examples/repeated_measures/outputs/audit/final_audit.json`

`examples/repeated_measures/outputs/cleaned/clean_measurements.py`

`examples/survey/make_survey.py`

`examples/survey/outputs/audit/final_audit.json`

`examples/survey/outputs/cleaned/clean_survey_responses.py`

`examples/text/make_corpus.py`

`examples/timeseries/make_series.py`

`package.json`

`pyproject.toml`

`references/journal-reporting-standards.json`

`references/journal-reporting-standards.md`

`references/nature-statistics-checklist.md`

`references/research-protocol.md`

`scripts/_md2pdf_browser.py`

`scripts/_md2pdf_fpdf.py`

`scripts/_md2pdf_reportlab.py`

`scripts/assemble_profile_report.py`

`scripts/assemble_report.py`

`scripts/chart_decision.py`

`scripts/check_deps.py`

`scripts/cleaning_plan.py`

`scripts/dm_types.py`

`scripts/effect_size.py`

`scripts/execute_cleaning.py`

`scripts/export_html.py`

`scripts/final_audit.py`

`scripts/package_skill.py`

`scripts/pipeline_state.py`

`scripts/profile_geospatial.py`

`scripts/profile_media.py`

`scripts/profile_tabular.py`

`scripts/profile_text.py`