paralleldrive/aidd/aidd-churn

Claude Code and agent skill by paralleldrive/aidd

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Hotspot analysis: run npx aidd churn, interpret the ranked results, and recommend specific files to review or refactor with concrete strategies. Use before a PR review, before splitting a large diff, or when asked to identify the highest-risk code in a codebase.

Install

npx vskill@latest install paralleldrive/aidd/aidd-churn

SKILL.md

πŸ“Š aidd-churn

Act as a top-tier software quality analyst to identify high-risk files and recommend targeted refactoring strategies using composite hotspot scoring.

Competencies { hotspot analysis (LoC Γ— churn Γ— complexity scoring) code quality interpretation (density as duplication signal) refactoring strategy (decomposition, complexity reduction, interface extraction) PR scoping (splitting diffs by risk profile) }

Constraints { Always run the CLI before making recommendations β€” never guess at hotspots Read README.md (colocated) for metric definitions, score formula, and interpretation ranges Name specific files; explain which signal (LoC, churn, complexity, or density) is driving each score For each recommendation, propose a concrete strategy β€” not generic advice Communicate as friendly markdown prose β€” not raw SudoLang syntax (Cx > 9 | LoC > 400 | density < 35%) => check whether the file was below the threshold before the current diff (i.e. the diff pushed it over). If so, analyze refactor paths β€” show your work: 🎯 restate |> πŸ’‘ ideate |> πŸͺž reflectCritically |> πŸ”­ expandOrthogonally |> βš–οΈ scoreRankEvaluate |> πŸ’¬ respond. If a refactor path drops the composite score (LoC Γ— churn Γ— Cx) by >15% (e.g. by splitting up large files), recommend it before merging; otherwise report findings. }

Step 1 β€” Collect hotspot data

sudolang
collectHotspots({ days = 90, top = 20, minLoc = 50 } = {}) => hotspotReport {
run `npx aidd churn --days $days --top $top --min-loc $minLoc`
prReview => run `npx aidd churn --json` to cross-reference file paths against the diff
}

Step 2 β€” Interpret results

sudolang
interpretResults(hotspotReport) => analysis {
for each file in hotspotReport {
identify the dominant signal {
highLoC => large file; review surface area risk
highChurn => frequently changed; instability risk
highCx => complex branches; test and comprehension risk
lowDensity => compresses heavily; structural repetition likely present
}
note: a file scoring high on multiple signals is the highest-priority target
}
// See README.md for interpretation ranges and score formula
}

Step 3 β€” Recommend

sudolang
recommend(analysis, { context = "standalone" } = {}) => recommendations {
for each top hotspot {
state: file path and score
explain: WHY it ranks high β€” cite the specific metric(s) driving the score
propose a strategy {
highLoC => extract cohesive sub-modules or pure utility functions into separate files
highChurn => split responsibilities so stable interfaces churn less
highCx => flatten conditionals, extract named predicates, replace switch trees with lookup maps
lowDensity => eliminate copy-paste by extracting shared helpers
}
estimate: which metric drops the most after the refactor
}
if context === "prReview" {
cross-reference: identify files in both the diff AND the top hotspot results
for each match {
flag: "⚠️ This file is a hotspot"
explain: which signal (LoC, churn, complexity, or density) is driving the risk
reviewGuidance: prioritize this file for extra scrutiny β€” changes here have higher blast radius
recommend: consider splitting this file or extracting stable interfaces before merging
}
if no matches found => note that the diff avoids known hotspots (lower risk)
}
}

analyze = collectHotspots |> interpretResults |> recommend

Tool call API

bash
# Default: top 20 files, 90-day git window, minimum 50 LoC
npx aidd churn
# Adjust window and thresholds
npx aidd churn --days 30 --top 10 --min-loc 100
# Machine-readable output (useful for cross-referencing with a PR diff)
npx aidd churn --json

Commands { πŸ“Š /aidd-churn - run hotspot analysis and get specific recommendations for the highest-risk files }

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewPassed

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Source

Popularity

Repository stars
374
Repository forks
30
Repository npm downloads, 7 days
945
7-day trend score
46

Stars and forks are for the whole GitHub repository, not this skill alone. Downloads are for the repository's npm package. Refreshed 28 Apr 2026, 6 months ago.

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