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MAGA2010MAGA2010/data-master/skills/deep-analyzer/SKILL.md

deep-analyzer

Skill78 starsChanged 23 days agoMITView on GitHub

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

1 other project uses a version of this.

Adopted by

Most recently taken up by

  • Other versions1 project uses a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 1

    1. dayvisonassis/sdd-skills★ 3same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository10 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Only when the user asks for a model, or when a stated question cannot be answered by a group comparison.

    line 17
  2. Distinctive:

    Every coefficient has an estimate, a CI and the unit it applies to.

    line 42
  3. Distinctive:

    do not run a model because it is available.

    line 59
  4. Distinctive:

    do not report a coefficient without its scale and CI.

    line 60
  5. Distinctive:

    do not drop the diagnostics to keep the table short.

    line 61

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
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

  • cleaned data
  • a declared model specification: outcome, predictors, random effects, family, link

Outputs

  • analysis/model.json: coefficients with CIs, fit statistics, diagnostics, n, unit_of_analysis, rule_source
  • analysis/model_residuals.png when diagnostics are visual

Commands

# v1 ships no model-fitting script: a deep-analysis run is an explicit,
# supervised step that must leave analysis/model.json in place.
python -c "import json; d=json.load(open('outputs/analysis/model.json',encoding='utf-8')); assert d.get('rule_source'); print('model artifact OK')"

Gate (default FAIL)

Every coefficient has an estimate, a CI and the unit it applies to. Every model reports at least one diagnostic appropriate to its family (residual vs fitted, overdispersion, VIF, convergence). A model that did not converge is reported as such, never as a fit.

Failure modes seen in real runs

  • Pseudo-replication. Rows that share a subject are modelled as independent, so every standard error is too small. Random effects are required when the design is nested.
  • A converged-looking fit with separation. Logistic regression on a rare outcome can produce enormous coefficients; the diagnostic must be printed, not skipped.
  • Comparing models on different n. AIC across different complete-case sets is not a comparison.

Do not

  • do not run a model because it is available;
  • do not report a coefficient without its scale and CI;
  • do not drop the diagnostics to keep the table short.

Upstream / downstream

  • previous: ../statistical-analyzer/SKILL.md
  • next: ../visualizer/SKILL.md
  • contract: ../../references/workflow-contract.md

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