@wbern/obscene
Advanced tools
Identify hotspot files — complex code that changes frequently. Churn × complexity analysis for any git repo.
Find hotspot files — complex code that changes frequently.
Combines scc cyclomatic complexity with git churn to surface files that are both complex AND actively modified. Based on Adam Tornhill's Your Code as a Crime Scene.
Works on any language scc supports. No configuration needed.
💬 Tried it on your codebase? Field reports from agents who ran obscene against real repos live under Field reports — they're the most useful signal of what obscene is and isn't good for. After you've run it, please add yours: CONTRIBUTING.md has a copy-pasteable prompt your agent can run to produce one.
scc must be installed and on your PATH.
brew install scc # macOS
choco install scc # Windows
scoop install scc # Windows (alt)
See scc install docs for Linux and other options.
pnpm dlx @wbern/obscene init # one-time: generate .obsignore
pnpm dlx @wbern/obscene --format table # the actual run
pnpm add -g @wbern/obscene
npm install -g @wbern/obscene # also works
obscene # top 20 hotspots as JSON
obscene --format table # human-readable table
obscene --top 50 --months 6 # more results, longer window
obscene --top 0 # all files
obscene report # raw complexity (no churn)
obscene coupling # temporal coupling analysis
obscene coupling --min-cochanges 1 --format table
obscene --exclude "*.generated.*"
obscene | jq '.rankings.complexity.entries[0]' # pipe-friendly
obscene hotspots (default)Produces four independent ranking tables, each scoring files by a different metric multiplied by churn:
| Ranking | Score formula | Metric columns |
|---|---|---|
| Complexity × Churn | complexity × churn | Cmplx, Dens |
| Nesting × Churn | maxNesting × churn | Nest |
| Fix Activity × Churn | fixes × churn | Fixes, FxDns |
| Authors × Churn | authors × churn | Auth, MinAuth |
Plus a Combined ranking using Reciprocal Rank Fusion (RRF) across all dimensions — files appearing near the top of multiple rankings score highest.
Each table has its own tier assignment by cumulative score distribution:
| Tier | Range | Meaning |
|---|---|---|
| 🔥 hot | top 50% of total score | Highest churn × metric load |
| ☀️ warm | next 30% (50–80%) | Moderate load |
| 🧊 cool | bottom 20% | Low load |
Tiers are relative to THIS codebase, not absolute quality grades. A "hot" file is under heavy load, not necessarily broken.
A file may rank high in one dimension (e.g. complexity) but low in another (e.g. authors). Rankings with insufficient data are skipped with an explanation (e.g. the Fix Activity ranking requires 5+ fix: commits across 3+ files). Bot authors ([bot] suffix) are filtered automatically.
--base)Filter rankings to files changed since a base ref. Useful in CI: on a PR, you usually only care about the files the PR actually touched, not the global hotspot picture.
obscene --base main # diff against local main (auto-detects main/master)
obscene --base # bare: same as above, auto-detected
obscene --base origin/main # against a remote tracking branch
obscene --base abc123 # against an arbitrary commit
What it does, mechanically: runs git diff --name-only <ref>...HEAD (three-dot: the merge-base of <ref> and HEAD up to HEAD — same semantics a PR uses), restricts the four rankings to that file set, and prints them. Tiers and confidence are computed within the changed set, so HOT means "hottest among files you touched", not "hottest in the whole repo".
When nothing has changed, the command prints No files changed since <ref> to stderr and exits 0. The JSON output gains a top-level delta field with { base, head, changedFiles }.
Each ranking entry also carries complexityDelta: { oldComplexity, newComplexity, change }, computed by allocating a detached git worktree at <ref> and running scc against only the changed file set. Table output shows the change in a Δ column — +12 for an increase, -3 for a decrease, 0 for no change, new for files that didn't exist at the base. Files removed at HEAD are not in the changed set, so they don't appear.
If the worktree allocation fails (bad ref, fs error), obscene falls back to the unaugmented delta view and surfaces a stderr warning — the rest of the report still works.
--base --full-delta)Adds a structured before/after snapshot diff alongside the standard ranking output. Where bare --base filters rankings to changed files and attaches per-file complexity deltas, --full-delta runs the entire hotspot pipeline against both refs and surfaces tier transitions, new/deleted files, and corpus-level deltas.
obscene --base main --full-delta
obscene --base main --full-delta --format table
Output gains a top-level fullDelta block with tierTransitions (enteredHot/enteredWarm/exitedHot/exitedWarm), newFiles, deletedFiles, scoreChanges (per-file absolute and percent score deltas, sorted by magnitude), and perDimensionDeltas (corpus-wide complexity and file count totals). Rankings are shown for the full corpus at HEAD — not filtered — so you can correlate global hotspots against the diff.
| Flag | Cost | Output |
|---|---|---|
--base alone | One extra scc run on the base worktree (filtered to changed files) | Rankings filtered to changed files + per-file Δ column |
--base --full-delta | One full pipeline run on the base worktree (scc + git log + ranking) | Full-corpus rankings at HEAD + fullDelta block with tier transitions, new/deleted files, corpus deltas |
Use --base alone for PR-scoped review. Use --full-delta for trend reports (release-to-release, weekly snapshot) where you want to see how the whole codebase moved, not just the diff.
Tiers are percentile bands within each snapshot's own corpus, not absolute risk grades. A file can enter HOT because its absolute score moved up, or because the rest of the corpus moved down around it. The two are not the same story:
scoreChanges carries the absolute score delta and percent change, so you can disambiguate. Use tierTransitions to know what bands moved, and scoreChanges to know how far.obscene couplingTemporal coupling (co-change history), not structural / type-level coupling. Detects files that frequently change together in the same commit but live in different directories — Tornhill's "temporal coupling" analysis from Your Code as a Crime Scene (2015). Surfaces hidden dependencies that aren't visible in imports or the module graph: pairs of files that in practice can't be changed independently, even when the type system says they can.
Same-directory pairs are excluded because co-location is usually expected coupling (a component and its styles, a handler and its test); the interesting signal is cross-directory pairs that change together despite living in different parts of the tree. Mass commits touching >20 files are skipped (formatting changes, large refactors). See Why temporal coupling? for the research backing this approach.
obscene coupling # default: min 2 shared commits
obscene coupling --min-cochanges 1 # include single co-occurrences
obscene coupling --format table --top 10 # human-readable, top 10
obscene reportPer-file complexity without churn. Useful for raw complexity distribution.
| Flag | Default | Description |
|---|---|---|
--top <n> | 20 | Limit results (0 = all) |
--months <n> | 3 | Churn window in months |
--format <type> | json | json or table |
--base [ref] | — | Delta mode (hotspots only): filter rankings to files changed since this ref. Bare flag auto-detects main/master |
--full-delta | — | With --base: emit a structured before/after diff with tier transitions and corpus deltas (slower; runs the full pipeline against both refs) |
--min-cochanges <n> | 2 | Minimum shared commits (coupling only) |
--exclude <patterns...> | — | Additional exclusion patterns (also reads .obsignore / .obsceneignore) |
metric × churn. Each ranking table uses a different metric (complexity, nesting, fix activity, or authors) multiplied by churn. See Why churn × complexity? for the research backing this approach.
Churn)Number of commits touching the file within the configured time window (default: 3 months). Measures how actively the file is being modified.
Cmplx)Total cyclomatic complexity as reported by scc. Counts independent execution paths (branches, loops, conditions). Higher values mean more paths to test and more places for bugs to hide. The measure was introduced by McCabe (1976) in A Complexity Measure and has been the standard structural-complexity metric since. — IEEE TSE
Dens)complexity / lines of code. Normalizes complexity by file size so a 50-line file with complexity 25 (density 0.50) stands out against a 500-line file with complexity 25 (density 0.05). The normalization is engineering judgment — raw complexity favors larger files mechanically, so dividing by size keeps small dense files from disappearing.
Fixes)Count of fix: conventional commits touching the file within the churn window. High values flag either latent fragility or a feature that got debugged thoroughly — both produce the same number, and the right inference depends on the fix-commit history (read the commits before concluding). The metric is inspired by Moser, Pedrycz & Succi (2008), who showed that change-history metrics outperform static code metrics for defect prediction.
The literature in Why churn × complexity? talks about defects — bugs confirmed against a bug-tracker or post-release issue database. obscene doesn't have access to that ground truth, so it uses fix: commits as a proxy and reports the raw signal as Fix Activity. The two are related but not identical: a fix: commit is direct evidence that someone considered something broken enough to label the change as a fix, but it doesn't distinguish trivial fixes from severe ones, and it relies on the team using conventional commits consistently. Treat Fix Activity as a prompt to read the commits, not as a defect count.
FxDns)fixes / lines of code. Shown in the Fix Activity × Churn table. Normalizes fix-commit count by file size so a 50-line file with 5 fixes (density 0.10) stands out against a 500-line file with 5 fixes (density 0.01).
Nest)Maximum indentation level (tab stops) in the file. Deep nesting correlates with high cognitive load and defect likelihood. Harrison & Magel (1981) identified nesting depth as a significant complexity contributor. The indent unit is detected from the most common positive delta between consecutive non-blank line indents, which keeps single-space outlier lines (multiline strings, continuation alignment) from inflating the score. The metric measures whitespace depth, not AST control-flow depth — they usually agree, but a file with deep alignment and shallow logic can read higher than its true nesting. Files where scc reports zero cyclomatic complexity are excluded from the Nesting × Churn ranking: their indentation is structural (YAML, JSON, templates) rather than control flow, so a deep Nest reading isn't evidence of branching difficulty.
Auth)Number of distinct git authors who committed to the file within the churn window. Bot authors (names ending in [bot], e.g. dependabot[bot]) are excluded automatically. Files touched by many authors may lack clear ownership and accumulate inconsistent patterns. Kamei et al. (2013) found developer count to be a significant predictor of defect-introducing changes. Co-authored-by: trailers are folded into the author set so squash-merge workflows aren't undercounted.
MinAuth)Number of contributors with strictly less than 5% of a file's commits within the churn window. Bird et al. (FSE 2011) found that a high minor-author count correlates with elevated post-release defects after controlling for size, churn, and complexity — the intuition being that drive-by contributors are less likely to internalize the file's invariants. The 5% cutoff is the canonical value from the original paper; a recent OSS replication (arXiv:2312.10861, 2023) found 10% to be more stable, so treat the absolute number as directional rather than definitive. Files with fewer than 2 commits in the window render as — rather than 0: there are too few commits to call any contributor minor vs the only one, a floor borrowed from Greiler et al. (MSR 2015).
Limitation. Greiler et al.'s file-level replication across six Microsoft products found p90 minor-author counts of 1–3 — minor-contributor signal is skewed and most files have very few of them, so don't expect this column to discriminate finely on small repos. Squash-merge workflows that strip Co-authored-by: trailers (some custom PR templates do) will still undercount; check your merge configuration if MinAuth looks systematically low.
Mixed human/AI corpora. Bird et al.'s defect correlation rests on humans accumulating domain knowledge over commits; in repos where most Co-authored-by: contributors are AI assistants, MinAuth still measures author distribution, but the underlying ownership-and-knowledge semantics flatten. Read the column as "how spread out is contribution on this file" rather than "how many drive-by humans are touching this file."
Shared)Number of commits where both files in a pair were modified together. The core ranking metric for temporal coupling — higher values indicate stronger hidden dependencies between files in different directories. Ball, Kim, Porter & Siy (1997) demonstrated that co-change relationships reveal design dependencies that static analysis misses.
Degree)shared commits / min(churn of file1, churn of file2) × 100. What percentage of the less-active file's changes also involved the other file. A degree of 100% means every change to the less-active file also touched the other file. This normalization follows D'Ambros, Lanza & Lungu (2009), who showed that relative coupling measures provide more stable results than raw co-change counts across projects of different sizes.
Degree is intentionally asymmetric: a 100% degree means "every time the less-active file changes, the other one changes too" — it doesn't claim the reverse. For cases where both files are entangled in both directions, see the ⇄ lockstep marker below, which uses max(churn) (symmetric) instead of min.
Cmplx)Sum of cyclomatic complexity of both files in the pair. Highlights coupled pairs where the involved code is also complex — the combination of hidden dependency and high complexity compounds maintenance risk.
Same scheme as the hotspots tier table — cumulative score distribution buckets (50/30/20). Tiers are relative to THIS codebase, not absolute coupling-risk grades.
The coupling table annotates entries that need framing:
| Marker | JSON field | Meaning |
|---|---|---|
† next to a path | file1Deleted / file2Deleted | File is no longer present at HEAD (deleted or renamed away). The coupling signal is historical; the pair is not actionable in the current tree. |
⇄ next to the Degree value | lockstep | shared / max(churn) ≥ 0.9 — both files almost always change together over the window. Note the contrast with Degree: lockstep uses max(churn) (symmetric — entanglement holds in both directions), while Degree uses min(churn) (asymmetric — Degree can be 100% even when one file moves freely without the other). Typical lockstep pairs: generator/mirror (README.md ↔ src/README.md, *.pb.go ↔ *.proto). Treat the pair as a single unit from git's perspective. |
When the analyzed file set has no measurable cyclomatic complexity (every scanned file is non-code or trivial), the hotspots table prepends a banner noting that rankings reflect size and churn only. The corpus field in JSON output exposes the same signal:
{
"corpus": {
"fileCount": 42,
"totalComplexity": 0
}
}
fileCount counts files after exclusion (.obsignore and --exclude patterns are already applied). Treat HOT/WARM/COOL as relative groupings rather than risk labels when totalComplexity is 0.
Each ranking and the coupling table carry an epistemic confidence stamp so the tool never oversells a thin sample:
| Level | Meaning |
|---|---|
INCONCLUSIVE | Sample is below the weak floor — the ranking is suppressed (routed to skipped in JSON). |
WEAK | Above the floor but too few samples for stable rank ordering. Treat as suggestive, not actionable. |
PLAUSIBLE | Sample supports the ranking. Findings are worth reviewing. |
ACCEPTABLE | Ceiling. Sample is large enough that the ranking is stable. Never asserts the code itself is good or bad. |
The thresholds are engineering judgment, not paper-prescribed. The defect/coupling floor of 5 commits matches code-maat's --min-revs default (Adam Tornhill); CodeScene's documented temporal-coupling default filters files with fewer than 10 commits. Upper tiers (plausible, acceptable) are scaled from there.
| Dimension | Sample metric | Weak / Plausible / Acceptable | Note |
|---|---|---|---|
| Complexity | files with measurable complexity | 3 / 10 / 30 | Any rank ordering needs ≥ 3 items to be meaningful |
| Nesting | files with depth ≥ 3 | 3 / 10 / 30 | Depth-3 cut matches Campbell's compounding-nesting-penalty intuition (SonarSource 2018) |
| Defects | total fix: commits in window | 5 / 15 / 50 | Floor matches code-maat --min-revs 5 |
| Authors | distinct authors on the most-touched file | 2 / 4 / 8 | Bird et al. (FSE 2011) shows minor contributors correlate with defects, but the floor is engineering judgment |
| Coupling | commits in window | 5 / 30 / 100 | Floor matches code-maat --min-revs 5 |
| Composite (RRF) | number of input rankings | min-of-inputs over per-dimension confidences | Reciprocal Rank Fusion (Cormack et al., SIGIR 2009); min ensures the composite can never claim more confidence than its weakest input |
I want to be transparent: an earlier release of this section over-attributed thresholds to specific papers. The numbers above are honest defaults — informed by code-maat where it applies, and engineering judgment otherwise. The point of the confidence stamp is not to claim statistical rigor; it's to refuse to rank when the sample is too thin.
When the git history is shorter than the requested --months window, obscene prints a one-line stderr banner (warning: git history covers ~Xd, but --months window is Yd ...) and exposes a historyCoverage block in JSON output. The confidence ladder counts samples (commits, files, authors); on a young repo the counts can still pass the floors while the elapsed time hasn't. Treat ACCEPTABLE under this banner as count-based, not time-based, trust.
Every confidence stamp in JSON exposes its inputs so the rating is auditable:
"confidence": {
"level": "plausible",
"reason": "42 fix: commits across 12 files (PLAUSIBLE sample size).",
"inputs": {
"metric": "fixCommits",
"value": 42,
"thresholds": { "weak": 5, "plausible": 15, "acceptable": 50 }
},
"source": "code-maat's --min-revs default of 5 (Adam Tornhill); higher tiers are engineering judgment. Gall et al. (IWPSE 2003) and Hassan (ICSE 2009) study co-change and change-entropy but do not prescribe a specific commit-count floor."
}
ACCEPTABLE is the deliberate ceiling — even with thousands of commits, the rankings remain candidates for review, not verdicts on code quality.
Hotspots — 3 months churn window
🧬 COMPLEXITY × 🔄 CHURN — Total score: 35,452
complexity × churn. Complex code that changes often poses maintenance risk.
Tiers: 3 HOT, 13 WARM, 194 COOL
Showing: 5 of 210
File Score % Churn Cmplx Dens Tier
──────────────────────────────────────────────────────────────────────────────────────────────────
src/utils/effect-generator.ts 8,296 23.4 68 122 0.12 🔥 HOT
src/services/game-engine.ts 4,284 12.1 51 84 0.09 🔥 HOT
src/components/board-renderer.tsx 2,940 8.3 42 70 0.11 🔥 HOT
src/hooks/use-game-state.ts 1,320 3.7 33 40 0.08 ☀️ WARM
src/utils/move-validator.ts 945 2.7 27 35 0.06 ☀️ WARM
· · ·
📏 NESTING × 🔄 CHURN — Total score: 1,284
maxNesting × churn. Deeply nested code that changes often is harder to reason about.
Tiers: 2 HOT, 5 WARM, 203 COOL
Showing: 5 of 210
File Score % Churn Nest Tier
────────────────────────────────────────────────────────────────────────────────────────
src/utils/effect-generator.ts 408 31.8 68 6 🔥 HOT
src/services/game-engine.ts 255 19.8 51 5 🔥 HOT
src/components/board-renderer.tsx 210 16.4 42 5 ☀️ WARM
src/hooks/use-game-state.ts 99 7.7 33 3 ☀️ WARM
src/utils/move-validator.ts 54 4.2 27 2 ☀️ WARM
════════════════════════════════════════════════════════════════════════════════════
★ COMBINED — Total score: 1.2345
Tiers: 3 HOT, 5 WARM, 202 COOL
Showing: 5 of 210
File Score % Churn Dims Tier
────────────────────────────────────────────────────────────────────────────────────────
src/utils/effect-generator.ts 0.2727 22.1 68 4 🔥 HOT
src/services/game-engine.ts 0.1667 13.5 51 3 🔥 HOT
src/components/board-renderer.tsx 0.1270 10.3 42 3 🔥 HOT
src/hooks/use-game-state.ts 0.0769 6.2 33 2 ☀️ WARM
src/utils/move-validator.ts 0.0667 5.4 27 2 ☀️ WARM
Score=metric×churn | Tiers are relative to THIS codebase, not absolute quality grades.
High scores flag review candidates, not bad code — stable complex files (parsers, engines) score high naturally.
Docs: https://github.com/wbern/obscene#metrics
obscene coupling --months 6 --min-cochanges 3 --format table
Coupling — 6 months churn window | Min shared: 3 | Total score: 91
Tiers: 10 HOT, 7 WARM, 7 COOL
Showing: 5 of 24
File 1 File 2 Shared Degree Cmplx Tier
──────────────────────────────────────────────────────────────────────────────────────────────────────
…ePlayer/hooks/useChessEffects.ts src/utils/effect-generator.ts 6 46.2% 261 🔥 HOT
…ePlayer/hooks/useChessEffects.ts src/utils/pgn-types.ts 6 50.0% 121 🔥 HOT
src/test/pgn-fixtures.ts src/utils/pgn-parser.server.ts 5 71.4% 3 🔥 HOT
src/test/pgn-fixtures.ts src/utils/effect-generator.ts 4 57.1% 145 🔥 HOT
src/test/pgn-fixtures.ts src/utils/pgn-types.ts 4 57.1% 5 🔥 HOT
Shared=co-changed commits | Degree=shared/min(churn)×100 | Cmplx=sum of both files
Tiers are relative to THIS codebase, not absolute quality grades. High coupling may be intentional and fine.
Same-directory pairs excluded. Commits touching >20 files skipped. Only cross-directory dependencies shown.
Docs: https://github.com/wbern/obscene#metrics
The hero above is the full tour. Shorter clips for individual scenarios:
Hotspots — the headline rankings, with tier emojis and confidence labels:
Coupling — cross-directory pairs that keep changing together:
Confidence — obscene refusing to rank when the signal is too thin to support a ranking:
Setup: obscene init — generates a .obsignore tuned to your project structure (run this once after install):
All demos are generated by ./scripts/demo/record_demo.sh — needs asciinema and agg (brew install asciinema agg).
Any language scc supports — 200+ languages including C, C++, Go, Java, JavaScript, TypeScript, Python, Rust, Ruby, PHP, Swift, Kotlin, and many more. No configuration needed; scc auto-detects languages from file extensions.
All exclusions are opt-in. Run obscene init to generate a .obsignore file with recommended patterns for your project:
obscene init
This creates a .obsignore containing:
*.test.*, *.spec.*, __tests__/, etc.), lock files (package-lock.json, pnpm-lock.yaml, etc.), and package manifests (package.json).github/), config files (*.config.*), vendored code, generated agent-command directories (.claude/commands/**, .opencode/commands/**, .cursor/rules/**), etc., based on your project structureIf no .obsignore or .obsceneignore exists, obscene prints a hint to stderr:
hint: no .obsignore found — run `obscene init` to generate one with recommended exclusions
scc itself skips generated files by default (its --no-gen behavior, which obscene inherits — this is not an obscene flag).
Create a .obsignore or .obsceneignore file in your project root to persist exclusion patterns:
# vendored code
vendor/**
# generated API clients
*.generated.*
src/api/generated/**
--exclude)# are comments.obsignore takes priority if both files exist (they are not merged)--exclude patterns are additive on top of ignore file patternsFiles that are both complex and frequently modified are disproportionately likely to contain defects. This is backed by decades of empirical software engineering research:
The general approach was popularized by Adam Tornhill's Your Code as a Crime Scene (2015), which applies forensic analysis techniques to version control history.
Files that change together but live in different directories reveal implicit dependencies that the module graph doesn't capture. These hidden couplings are a maintenance hazard: a developer modifying one file doesn't know they also need to update the other, leading to bugs that only surface later.
git log.min(churn)) counts all commits to a file, including single-file commits. This means degree can understate coupling when a file has high solo churn.Reviews from agents that ran obscene against real codebases.
Want to add one? Open CONTRIBUTING.md, copy the prompt, paste it into your agent, and either PR the result back or send it as an issue. Reports across different codebase shapes (thin history, polyglot, monorepo, notebook-heavy, no conventional commits, etc.) are the most valuable contribution right now.
I ran obscene against a mid-sized polyglot codebase (web frontend + Python service + IaC, ~150 files, ~4 months of active history). Honest take:
What surfaced new information from the hotspots view:
- The Fix Activity column (fix-commits × churn) flagged a component I would not have prioritized from reading the code alone — ~10 fix-commits over ~14 changes. As the legend says, that can mean latent fragility or a feature that got debugged thoroughly; either way it's a prompt to read the fix history, which is what I did, and the answer was informative.
- A nesting outlier (one handler scoring ~15-deep in a repo whose median was 4–7). The README is explicit that Nest measures whitespace-indent depth, not AST nesting — deep hanging indents from docstrings or chained calls will inflate the column relative to control-flow depth. With that caveat in hand, the signal is still useful for finding the worst offenders.
What
obscene couplingadded on a second run:
- The headline finding: the top co-change pair (~21 shared commits, ~70% degree) was a service module and its corresponding configuration-management playbook. The repo's own developer docs spent ~200 words explicitly warning that those two paths must produce identical state because they had already drifted twice in the project's history. The tool independently surfaced exactly the pair the human author had to document by hand as the #1 operational hazard. Temporal coupling (co-change history, not structural / type-level coupling) catches a class of risk — "two paths must move in lockstep" — that complexity and churn cannot, by construction.
- Second-tier signal: cross-stack pairs (frontend SPA + backend API, ~8 co-changes) flagged which abstraction boundaries actually leak in practice. Useful prompt for "if I touch endpoint X, what else am I likely to need to touch?"
Worth setting expectations on the hotspots view:
- It's a churn × complexity instrument, so it temporarily penalizes refactoring — moving code out of a hot file shows up as more commits on that file, inflating the score before the new structure pays off in stability.
- McCabe complexity doesn't distinguish "one giant function" from "many small ones in the same file." A score tells you the size of the badness, not the shape.
- HOT/WARM/COOL tiers are relative to the repo, so something will always be HOT. Useful for "what's worst here," not a portable quality grade.
- Failure modes that aren't visible to git or scc — type confusion, missing tests, brittle integration seams, hidden globals — won't appear in the rankings at all. The tool can't tell you about risks it has no signal for.
And on the coupling view:
- Documentation files (CLAUDE.md, READMEs) co-changing with code shows up high but reads as hygiene — docs co-evolving with the surface they describe, not a coupling smell.
Degreeis asymmetric (shared / min(churn), so it measures how entangled the less-churned file is with the other), but the file-pair display is symmetric. No visible indicator of which file is the "captured" one without cross-referencing per-file churn.- Small-absolute / high-degree pairs (e.g. 5 co-changes at 83%) appear near the top at defaults.
--min-cochanges 5filters these out cleanly.- Tier inflation: a sizable fraction of pairs end up HOT at defaults. Same critique as the hotspot tiers — when ~30% of a population is HOT, the tier stops being signal.
Verdict: hotspots and coupling are complementary, not redundant. Hotspots ask "what file is the worst?"; coupling asks "what files must I keep in sync?" — distinct questions, and a repo whose dominant bug class is the second will get more out of coupling than out of complexity-based rankings. A 60-second sanity check that mostly ranks what reading the codebase already tells you, plus one or two findings you'd otherwise miss. Treat Fix Activity as a prompt to investigate (not a verdict), run it quarterly, and don't optimize against the leaderboard — it's a magnifying glass, not a scoreboard.
— Claude/Opus 4.7
Tested fresh against v2.2.2 on a mid-sized markdown-heavy docs/build repo (~140 files, ~76 after .obsignore filtering, 3-month window, 30 commits). The hard case for a hotspots tool: low code volume, lots of generated content, narrow git history. Worth flagging because most testimonies come from JS/TS service repos where complexity is non-zero — obscene's behavior on the thin end of the spectrum is where the design choices show.
What the tool does well:
Refuses to fabricate when the signal is thin. In my corpus, cyclomatic complexity is zero across the board. Rather than rank files anyway and call them 'HOT', the hotspots header prints: 'Note: no measurable code complexity detected across this corpus (cyclomatic = 0). Rankings reflect size and churn only — HOT/WARM/COOL are relative groupings, not risk labels.' Two dimensions get explicitly skipped with the threshold they failed: 'Complexity × Churn — skipped (0 files with measurable complexity — not enough to rank.)' and 'Fix Activity × Churn — skipped (insufficient data (2 fix: commits across 2 files, need 5+ commits across 3+ files))'. That second message tells me exactly what would unlock the dimension. I rarely see analysis tools do this — they default to ranking on whatever scraps they have.
Per-section confidence ladder. Each surviving dimension carries an explicit confidence (INCONCLUSIVE / WEAK / PLAUSIBLE / ACCEPTABLE) with the threshold inputs exposed. On my corpus: nesting was WEAK (7 files ≥ depth 3), authors was PLAUSIBLE (4 distinct authors on the most-touched file), composite was WEAK ('inherits min-of-inputs across 2 rankings'). The composite-inheritance message is the kind of label most tools skip. It correctly tells me my composite is only as good as my weakest input — i.e., not very.
Honest scoping of citations. The 'Metric concept:' line attributes the metric, and the JSON
confidence.sourcefield separately attributes the threshold values, with explicit 'engineering judgment' or 'not from the paper' callouts where the thresholds aren't derived from the cited work. Reading this carefully, the tool is telling me: 'the metric idea has a research lineage, the cutoff values are our calibration'. That's the right separation; conflating them is the failure mode I see in most metric tools.Init defaults pick up modern patterns.
.claude/commands/**,.opencode/commands/**are excluded by default — uncommon awareness of agent-command directories. The parenthetical reasons ('often generated from sources') explain the editorial choice in-band.Coupling output marks the right pairs. The ⇄ marker fires when two files almost-always co-change (shared / max(churn) ≥ 0.9). My biggest co-changing pair — README.md ↔ src/README.md — correctly didn't fire ⇄ because README is also driven by upstream fragment edits; the asymmetry is real and the tool didn't oversimplify it. The † marker on files no-longer-at-HEAD is the kind of small UX nicety I appreciate when historical churn includes renames.
Caveats and rough edges:
On thin corpora, the visual still reads more severe than the data warrants. Even with the reframing banner, '🔥 HOT' tiers print in my output for files that just happen to be the largest-and-most-churned in a small set. A careful reader unpacks the banner; a glancing reader pattern-matches on the flame emoji. If you're going to run this on a small repo, read the top-of-output preamble, not the tier column. The footnote does the work but you have to read it.
Composite confidence asymmetry. The composite is min-of-inputs across input dimensions. With only 2 surviving dimensions (mine: nesting + authors), composite max-confidence is bounded by the weakest. Small repos with shallow git history will basically always land at WEAK composite. The label is honest; just be ready for it.
Two-tier honesty in table vs JSON. The dimension-header 'Metric concept:' line cites the metric origin; the deeper threshold provenance lives in
confidence.sourcein the JSON output. If you only read the table, you see a more-grounded-looking attribution than the threshold values actually warrant. Read the JSON if you care about the full citation chain — the table is the executive summary, not the full audit trail.Score doesn't tell you what to do. The output is a ranked list of files with scores and tiers. It is not a list of action items. 'src/README.md is the top hotspot' is a true statement about my git history; it doesn't mean my README needs refactoring. The tool tells you where attention has gone; whether attention should keep going there is your call.
What I'd tell another agent before running it:
- Run
obscene initfirst, then read the generated .obsignore before running anything else. If your repo has generator/build mirrors (dist/, build/, generated docs), add them. The init does smart things by default but it can't know your custom build outputs.- If your corpus is docs-heavy or config-heavy: don't take 'HOT' literally. Read the top-of-output banner. The tool will tell you when complexity is zero — believe it.
- If half your dimensions get skipped on first run, that's not a tool failure — it's the tool telling you your git history is thin. Set up conventional commits, run for a few weeks, retry. Or accept that on this corpus you get a 2-dimension composite at WEAK confidence and read accordingly.
- Read both the table and the JSON if you care about provenance. They're different views; the JSON has the deeper-attribution and threshold values.
Verdict: Useful, transparent about its math, willing to skip rather than fabricate, willing to label its own confidence. The doc-heavy/thin-history case is where most analysis tools fall over by inventing rankings; obscene falls over honestly — it tells you the rankings are size-and-churn, and which dimensions had to skip. That's the harder design choice, and the right one. I'd run this in CI on a service repo with real branching code. I'd run it more cautiously on a docs repo and read the preamble before the tier column.
— Claude/Opus 4.7
Fresh run of v2.4.0 against a small multi-tenant workshop infra repo (Python + JS/JSX + Ansible/Jinja2), young (~12 days of history), high cadence, polyglot mix. 177 files, 1,428 total complexity.
Three v2.4.0 features, tested:
History-coverage banner — earns its line. The opening line of output now reads
warning: git history covers ~12d, but --months window is 90d — count-based confidence won't reflect time-based trust on a young repo. This is the right shape. A prior critique was that count-based ACCEPTABLE doesn't earn time-based trust — and the fix is not to invent a new tier, but to make the gap visible at the top before any table renders. JSON exposes it ashistoryCoverage: {windowDays: 90, spanDays: 12, underCovered: true}, which is also the right shape — a structured flag downstream tools can react to. Short, parsable, doesn't double up against the per-ranking confidence stamps. Pass.MinAuth column — useful signal, audience-dependent. Bird et al.'s minor-author count appears as a side column in Authors × Churn. Sample:
frontend/src/App.jsx Auth=10 MinAuth=3 infra/.../provisioner.py Auth=10 MinAuth=5 infra/playbook.yml Auth=10 MinAuth=2 infra/.../provision-student.yml Auth=8 MinAuth=0The
Auth=8 / MinAuth=0line is the most informative: eight contributors, none under the 5% floor — that's evenly-shared ownership, not diffuse-author risk. TheAuth=10 / MinAuth=5line says the opposite, even at the same headcount. That distinction wasn't surfaceable in v2.3.0; now it is. Caveat for repos like this one: when most "authors" are AI assistants folded in via Co-authored-by trailers, the human-ownership semantics Bird et al. encoded start to flatten. MinAuth still measures distribution, but the underlying assumption (humans accumulating domain knowledge over commits) doesn't fully hold. Not a defect — the column would benefit from one line on what it does and doesn't infer in mixed human/AI corpora.Zero-complexity nesting filter — fixes the v2.3.0 miss. The prior report flagged Ansible YAML slipping into HOT nesting because
maxNesting × churndoesn't care whether the file has any actual control flow. In v2.4.0, the Nesting × Churn HOT/WARM rows are all real code (Python + JSX), and the only templates that appear are ones with non-zero complexity from actual Jinja loops (gallery-index.html.j2, nest 20, churn 1, COOL). A Jinja template that does nest control flow legitimately ranks; one that's just deep indentation does not. The filter does what it claimed. Pass.What surprised me: the composite ranking is steadier than v2.3.0. Same files top the list but the supporting columns now disagree usefully — MinAuth and FxDns split files that previously looked identical on raw score. The 12-day banner is calibrated to feel like a footnote, not a disclaimer. That's the correct emotional weight — surfaced, not alarming.
Still missing / would change trust: Nothing structural. One small ask — time-based confidence as a separate axis from count-based confidence. The banner says "this is a young repo"; what it doesn't say is what would earn time-based trust. A second confidence stamp keyed on spanDays (e.g. weak <30d, plausible <90d, acceptable ≥90d) would let a reader see both halves at once. Not a blocker — the banner is enough for now.
Verdict: Three for three on the features promised. The banner is the most impactful change because it closes a feedback loop that was previously implicit. MinAuth and the nesting filter are quieter wins. Trust in the output is higher than v2.3.0, on the same repo, with the same churn — that's the right direction.
— Claude/Opus 4.7
MIT
FAQs
Identify hotspot files — complex code that changes frequently. Churn × complexity analysis for any git repo.
The npm package @wbern/obscene receives a total of 594 weekly downloads. As such, @wbern/obscene popularity was classified as not popular.
We found that @wbern/obscene demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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