Analyze Code Density

Code Density Analyzer (score mode)

Detect AI-code slop — repeated logic, filler constructs, and reviewer-repellent patterns — and score how dense the code is. This skill only reports; it never edits code.

Step 0: Determine target and mode

Diff mode (preferred) — use when inside a git repository and any of:

• the user names a branch/PR/commit range → target = that range's diff
• uncommitted changes exist → target = git diff HEAD (staged + unstaged)

Standalone mode — use when the user names specific files/snippets, or no git repo is present. If ambiguous, ask: "Analyze the current diff, or specific files?"

Step 1: Assemble the analysis input

Both modes: number all lines (cat -n style) and prefix each file with its path.

Diff mode:

1. Collect the diff and the full post-change content of each changed file (subagents need surrounding context, not just hunks).
2. Build repo-context excerpts, bounded to ~300 lines total: for each changed file, include the signatures + docstrings of util/helper modules it imports or that live in obviously shared locations (utils/, lib/, common/, helpers/), plus one sibling file as a convention sample. Use grep -rn "def \|function \|class " <shared dirs> | head -80 to build a signature index rather than pasting whole files.
3. If the diff exceeds ~3000 changed lines, split by top-level directory and run the whole analysis per split, then report per-split scores.

Standalone mode: the named files' contents. No repo context is provided, and methods that require it must return applicable: false.

Step 2: Dispatch analysis subagents in parallel

Launch ALL methods in a single message so they run concurrently. Each subagent prompt = the method file below + agents/_conventions.md + the assembled input

• this line: "Return ONLY the JSON output described in your instructions. No commentary."

Weights are provisional pending Round 0 recalibration (see workspace/).

Step 3: Aggregate (main agent)

1. Parse each envelope. A method whose JSON fails to parse after one retry, or which returned applicable: false, is EXCLUDED.
2. Renormalize: density_score = 100 × Σ(weight_i × sub_score_i) / Σ(weight_i) over included methods only. Round to an integer.
3. Labels: 80–100 Dense · 60–79 Acceptable · 40–59 Bloated · 0–39 Sloppy.

Step 4: Report

## Code Density Report

**Target:** <diff range or file list>  |  **Mode:** diff | standalone
**Density score:** <N>/100 (<label>)
**Methods:** <k>/10 included (<excluded list with reason: n/a | failed>)

### Findings by severity
For each high finding, then medium, then low:
- `<file>:<lines>` — **<pattern>** (<method>): <evidence> → <suggestion>

### Per-method breakdown
| Method | Sub-score | Findings |    (one row per included method)

### Top actions
Numbered list of the 3–7 highest-impact removals/merges, referencing findings.

Score mode only: present the report and stop. Do not offer to fix the code within this skill.

Key principle

Repeated logic + no new behavior = duplication. Constructs that do no work = filler. Every line must earn its place — the goal is never shorter code for its own sake, but code where nothing is left to delete without losing behavior or clarity.