{"slug":"data-3","title":"data","summary":"Data analysis and reference enrichment.","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-20T08:04:42.686264Z","repo":{"url":"https://github.com/notque/vexjoy-agent","stars":425,"forks":48,"license":"MIT","updatedAt":"2026-09-26T21:45:15Z"},"bodyHtml":"
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name: data\ndescription: \"Data analysis and reference enrichment.\"\nuser-invocable: true\nargument-hint: \"

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Data Skill

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Two modes. Match the request to a section.

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SignalMode
Analyze data, CSV, metrics, A/B test, trend, KPI, funnel, distributionA. Data Analysis
Enrich references, generate references, decompose skill, improve depthB. Reference Enrichment
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A. Data Analysis

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Every analysis starts with the decision it supports, works backward to evidence\nrequired, then touches the data. Analysis without a decision is arithmetic.

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Phase 1: FRAME

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Establish what decision this analysis supports.

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  1. Identify the decision, decision-maker, options, and default action if no analysis is done.
    2. \n
  2. If the user cannot articulate a decision, ask: \"What will you do differently based on this analysis?\" If exploratory, switch to Exploratory Mode (apply rigor gates, make no causal claims).
    3. \n
  3. Define evidence requirements: what evidence favors each option, minimum threshold for changing the default, deal-breakers.
    4. \n
  4. Save analysis-frame.md.
    5. \n
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Gate: Decision identified, options enumerated, evidence requirements saved.

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Phase 2: DEFINE

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Lock metric definitions before loading data. Defining after seeing data enables cherry-picking.

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For each metric: name, exact formula (numerator/denominator), population (included/excluded), time window, segments. For comparisons: define groups and verify fairness.

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Save metric-definitions.md. Definitions are locked once Phase 3 starts. If data reveals a definition is unworkable, return here, update, and document the change.

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Gate: All metrics defined with formulas and populations.

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Phase 3: EXTRACT

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Load data. Assess quality. No interpretation.

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  1. Detect tools: try import pandas; fall back to csv.DictReader + statistics.
    2. \n
  2. Profile: row count, column types, missing values, date range, distribution stats.
    3. \n
  3. Quality checks (load references/rigor-gates.md Gate 1):
    4. \n
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CheckMinimumIf failed
Sample fractionReport N of MWarn if <5% coverage
Time windowNo gaps >10%Adjust or note limitation
Segment size30+ per segmentMerge small segments or exclude
Missing rate<20% per critical columnImpute with disclosure or exclude
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  1. Save data-quality-report.md.
    2. \n
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Gate: Data loaded, quality assessed, failures documented as limitations.

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Phase 4: ANALYZE

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Compute metrics per Phase 2 definitions. Report confidence intervals, not point estimates.

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  1. Compute using exact formulas. Wilson score CI for proportions.
    2. \n
  2. Fairness gate (comparisons): same time window, same population, confounders documented, survivorship checked (load references/rigor-gates.md Gate 2).
    3. \n
  3. Multiple testing (6+ comparisons): apply Bonferroni (threshold = 0.05/N). Report all segments tested (Gate 3).
    4. \n
  4. Practical significance: report effect size alongside statistical significance. Base-rate context (\"from 2.1% to 2.3%\", not \"+10% lift\") (Gate 4).
    5. \n
  5. Save analysis-results.md.
    6. \n
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Gate: All metrics computed. Rigor gates applied.

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Phase 5: CONCLUDE

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Lead with insights. Return to the decision.

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  1. Headline finding: one sentence addressing the Phase 1 decision.
    2. \n
  2. Supporting evidence: primary metric with CI, secondary metrics, segment breakdowns.
    3. \n
  3. Limitations: wide CIs are the finding, not a formatting problem.
    4. \n
  4. Decision mapping: does evidence meet threshold? Deal-breakers triggered? Recommended action? Additional data needed?
    5. \n
  5. Save analysis-report.md (load references/output-templates.md for analysis-type templates).
    6. \n
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Gate: Report saved with headline, limitations, recommendation tied to decision.

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Error Handling (Data Analysis)

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ErrorRecovery
No decision contextAsk \"What will you do differently?\" Switch to Exploratory if none.
Parse failureTry utf-8, latin-1, utf-8-sig. Detect delimiter. Max 3 attempts.
Insufficient segment data (<30)Merge small segments, remove segmentation, or accept with disclosure.
Metrics changed after seeing dataReturn to Phase 2, document changes. Max 2 revisions.
Wide CI on primary metricState: \"Data does not support a confident decision.\" Suggest more data.
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B. Reference Enrichment

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Enrich agent/skill reference files from Level 0-2 to Level 3+, or decompose bloated body files by extracting domain content into references.

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Phase 0: DECOMPOSE (when --decompose or \"extract references\")

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Extract domain-heavy content from a bloated SKILL.md into reference files.

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  1. Run python3 scripts/detect-decomposition-targets.py --skill {name} (or --agent).
    2. \n
  2. If no extractable blocks, report \"nothing to decompose\" and stop.
    3. \n
  3. Snapshot: cp {path} /tmp/decomp-before-{name}.md.
    4. \n
  4. For each block: create reference file, remove from body (MOVE, not copy), add loading table entry.
    5. \n
  5. Retain in body: frontmatter, overview, phase workflow, loading table, error handling.
    6. \n
  6. Validate: python3 scripts/validate-decomposition.py --before /tmp/decomp-before-{name}.md --after {path} --refs {refs_dir}/.
    7. \n
  7. If fails: restore from snapshot. If passes: python3 scripts/validate-references.py --skill {name}.
    8. \n
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Load references/decomposition-prompt.md for the autonomous decomposition prompts.

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Gate: Validation passes. Body reduced. All extracted content in references.

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Phase 1: DISCOVER

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  1. Run python3 scripts/gap-analyzer.py --agent {name} (or --skill).
    2. \n
  2. Read the component's .md and existing references. Map coverage.
    3. \n
  3. Compare stated domains against covered domains. Output gap report.
    4. \n
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Gate: At least one gap identified. If Level 3 already, stop.

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Phase 2: RESEARCH

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For each gap: identify version-specific patterns, failure modes with detection commands (grep -rn \"pattern\"), error-fix mappings, project conventions. Dispatch up to 5 parallel research agents per sub-domain.

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Gate: Each gap has 10+ concrete findings (version numbers, function names, grep patterns). Generic advice does not count.

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Phase 3: COMPILE

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Create one reference file per sub-domain (max 500 lines) following references/reference-file-template.md. Include: overview, pattern table with version ranges, failure mode table with detection commands, error-fix mappings.

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Do-pairing rule: every failure mode needs a \"Do instead\" counterpart. No bare negative blocks.

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Validate: python3 scripts/validate-references.py --agent {name} and --check-do-framing. Both must exit 0. Then run condense on each file.

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Gate: Each file 80-500 lines. Both validations pass.

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Phase 4: VALIDATE

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Tier 1: python3 scripts/audit-reference-depth.py --agent {name} --json. Level must be 3.\nTier 2: Apply references/quality-rubric.md. For each pattern: detection command present? Would a reviewer using only this file produce Level 3 output?

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Gate: Both tiers pass. Max 2 loops per gap before flagging for manual review.

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Phase 5: INTEGRATE

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  1. Add/update loading table in the component body.
    2. \n
  2. Validate: python3 scripts/validate-references.py --agent {name} and python3 -m pytest scripts/tests/test_reference_loading.py -k {name} -v.
    3. \n
  3. Stage changes.
    4. \n
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Gate: Validation passes. Report level change (was N, now M) and new file list.

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Error Handling (Reference Enrichment)

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ErrorRecovery
Gap analyzer failsCheck both agents/ and skills/ directories.
Phase 2 gate fails (<10 findings)Domain may be narrow. Flag for manual enrichment.
Phase 4 still below Level 3Files too generic. Target Phase 2 at weakest section.
Decomposition validation failsRestore from snapshot. Check for partial extractions.
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Deep References

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All references are >100 lines of domain-specific content. Load as directed by sections above.

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SignalReferenceLines
Phase 3-4: statistical gates, sample adequacy, fairnessreferences/rigor-gates.md378
Phase 5: report templates (A/B, trend, distribution, cohort)references/output-templates.md489
Failure mode recognition (p-hacking, survivorship, Simpson's)references/preferred-patterns.md240
Classifying reference depth Level 0-3references/quality-rubric.md173
Writing new reference filesreferences/reference-file-template.md166
Running headless decompositionreferences/decomposition-prompt.md205
Running headless enrichmentreferences/enrichment-prompt.md117
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