| name | human-maintainability |
| version | 1.1.2 |
| description | Assess a programming project's HUMAN maintainability (HMAP): 0-100 score, nine-tier verdict, per-dimension scores, and file:line evidence — built to catch vibe-coding / AI-generated slop, including semantic incoherence (name/contract/concept/vocabulary drift). Triggers: assess maintainability / rate this project / code quality score / readability score / AI slop detection / vibe coding check / semantic consistency check / HMAP / human maintainability / 评估项目可维护性 / 给项目打分 / 代码质量评估 / 可读性评分 / AI屎山检测 / vibe coding 体检 / 是否适合人类维护 / 语义一致性. Anti-trigger: the user asks to fix, refactor, optimize, or write code — that is a different task; this skill only assesses, never modifies. |
human-maintainability (HMAP)
Human Maintainability Assessment of Programming Projects — scores whether a project is maintainable by human programmers (0-100, nine tiers), with dedicated forensics for vibe-coding / AI-generated slop.
Core principle: assess only, never modify. No refactoring, no bug fixes, no touching source files; findings go into the report, period.
Trigger / anti-trigger
- Trigger: the user asks to "assess maintainability / score this project / check for AI slop / rate code quality / is it human-maintainable" for any path, with the target given or inferable.
- Anti-trigger: the user wants bugs fixed, refactoring, features, or performance work — do NOT apply this skill. If asked to "assess then fix", finish the assessment first; handle fixes only under a new instruction.
Scoring model (4 weighted dimensions, 100 max)
Read before scoring: this is a human readability & maintainability assessment, not a style-compliance review — comment coverage, naming consistency, DRY and similar "standards" earn no points by themselves, only when they serve human understanding and maintenance. Core question: after reading the code, ask yourself "if this project were handed to you, would you maintain it yourself? Why?" The total score must agree with the answer.
Black cat, white cat — equal treatment: AI authorship, project size, and maintainer count are NEVER scoring criteria — only whether the artifact obstructs human maintenance. READMEs are checked only for promise fulfillment (tidiness/ads/marketing/absence are not penalized).
Use the full 0-100 range: human-normal debt alone never fails a project (debt floors D2/D4 at 70; good projects with only debt land 80+), and the 0-40 bands are real landing zones — a slop pile (gods + fingerprints + broken promises + unreadable) SHOULD total 20-40. Do not cluster everything at 45-80.
| Dimension | Weight | Key question |
|---|
| D1 Design soundness & technical honesty | 25 | Does the architecture match the problem's complexity? Do promises survive code verification? Real engineering thinking (measurement/trade-offs) or wishful thinking? Do tests give maintainers safety? |
| D2 Traceability & mental load | 30 | Can a human trace request → code? Clear responsibilities or everything fused together? Change one thing — how many places must you know? Must the structure be memorized via external docs? |
| D3 Human readability & self-explanation | 15 | Does the code explain itself? Do names/comments convey "why", or are they decorative / written for AI? |
| D4 AI slop & wishful-thinking residue (reverse) | 30 | Promise betrayal, over-engineering, source-inlined AI process markers, structural gods (big AND mixed), systemic copy-paste, debug residue, hallucinated comments — more residue, lower score |
Score each dimension 0-100, weight-sum, then apply structural hard gates. Semantic coherence (names telling the truth, contracts closed, concepts single-sourced, vocabulary self-evident) is audited by the fixed SCA protocol (M14) and feeds all four dimensions — it is the third evaluation axis alongside structure and residue. Detailed rules (M1-M16 measurement table, tier mapping, SCA semantic-coherence protocol, RM responsibility-mixing protocol, tie-breaks): references/scoring-rubric.md; signal catalog: references/ai-slop-signals.md; worked scoring example: references/golden-example.md.
Structural hard gates (pass-line verdicts): structural facts that make human maintenance impossible or infeasible hard-cap the total — core god-function/god-file that is also responsibility-mixed → 60 (pass-line edge); source-inlined AI process markers at scale → 45; AI debugging residue in hot paths ≥5 (coordinate/offset special-cases, magic offsets, embedded debugging narratives, probe residue; ordinary bare printlns excluded) → 45; G1 together with G3/G4 → 40. Dimension scores may not lift the total above a cap. Gates judge structure only — never size, headcount, or AI authorship; top-level convention files (AGENTS.md/CLAUDE.md etc.) are not AI process markers.
Nine tiers (60 is the split: ≥60 pass = human-maintainable; <60 fail = unfit for direct human maintenance)
| Range | Verdict |
|---|
| 90-100 | Ideal: clear responsibility boundaries, request→code traceable, comments explain "why", all promises kept |
| 80-90 | Smooth: generally fluent, only acceptable debt; no structural mixing, no AI fingerprints at scale |
| 70-80 | Burdened: cross-file tracing costs, a few bloated spots, or minor AI traces |
| 60-70 | Pass-line edge: visible AI-slop traces or structural bloat emerging (god-file/bloated functions); takeover is hard but humans can still maintain it |
| 50-60 | Fail — unfit for direct human maintenance: severe responsibility mixing, poor readability; takeover cost disproportionate |
| 40-50 | Unfit: core bloated, structure untraceable; external docs/AI required to understand |
| 30-40 | Unfit (heavier): structural gods rampant; humans cannot change code safely |
| 20-30 | Extremely unfit: structural gods + AI process fingerprints everywhere |
| 0-20 | AI-agent-only: total slop, beyond rescue |
Boundary semantics: >=90 lands in the 90 tier; and so on; <20 in the 0 tier.
The 60 split is this skill's anchor: ≥60 = pass = a human programmer can maintain it (matching cent exam intuition); <60 = fail = unfit for direct human maintenance. Tier descriptions encode the "can humans maintain it" judgment, not surface quality — structural mixing lands below 60; AI fingerprints at scale land below 60.
Assessment workflow
Before starting you MUST read references/scoring-rubric.md, references/ai-slop-signals.md, and references/golden-example.md to align scoring lenses and calibration.
Step 1: parse arguments
- Target path (required): the project directory, relative or absolute; defaults to the current working directory.
--depth=light|medium|deep (optional): sampling intensity. Default medium.--output=path (optional): write the full report to the given markdown file.--focus=dimension (optional): assess only the named dimension (D1 design / D2 traceability / D3 readability / D4 slop); no total score.
Step 2: inventory
Glob / Grep / Bash: total file count and file tree (exclude dependency & build dirs: node_modules, .git, dist, build, venv, target, __pycache__, .next, bin/Release, obj, etc.); language mix and estimated total LOC; locate README, build/package entry, entry file, test directories.
Step 3: sampling protocol
Sample and declare the sampling scope and confidence. Unread files must not be scored as if read.
| Project size | Sampling strategy | Default reads |
|---|
| ≤50 source files | read all | all |
| 50-500 | entry + core modules + per-layer representatives + largest files + most-commented files | 15-30 files |
| >500 | stratified sampling: entry, business core, boundary/infra | 30-60 files |
Must-read regardless of size: README, build/package entry, entry file, largest source file, most recently modified files. --depth=deep doubles the read count; --depth=light halves it. Repeated evaluations must fix the same sampling depth.
Step 4: per-dimension forensics
- Measure first (mandatory before scoring): run M1-M16 from the rubric's global measurement table and fill the sheet. Measured values decide tiers — before impressions.
- Semantic Coherence Audit (SCA, M14): run
references/semantic-surface.py <root> → adjudicate the top-15 most-referenced symbols (SI-1 names), review SI-2/SI-3/SI-4 candidates against the exclusion rules, run the 20-site convention census (CV); record SI1_viol / SI2_unconsumed / SI2_trap / SI3_pairs / SI4_unrooted / CV in the sheet. Never adjudicate by impression.
- Promise verification (mandatory): list README/doc promises → verify each in code (claiming "performance gains" while only adding abstraction layers, claiming "extensible" with no extension points ⇒ major wish evidence). README: promises only; tidiness/ads/absence not penalized.
- Responsibility mixing via the RM protocol: every ≥400-line function / ≥1800-line file goes through RM (enumerate domains → count → precedents → arbiter) for "single responsibility vs mixed". Big-but-single is not penalized; bloat is.
- Boundary decisions: when a measurement lands on a boundary or signals conflict, record a boundary decision per rubric rule 8 (object / both-side reasoning / final choice / evidence) — no systematic rounding to either side.
- Every scoring judgment must land on
file:line evidence; impressions without evidence may not be used.
Step 5: weighted scoring & structural gates
- Per the rubric's measurement→anchor tables: tier per dimension → enumerated adjustments → four raw scores.
- Weighted total = 0.25×D1 + 0.30×D2 + 0.15×D3 + 0.30×D4; apply hard gates (G1/G3/G4; multiple hits → lowest cap).
- Forced judgment: honestly answer "would you maintain it yourself? Why?" — "no" ⇒ total ≤60, "absolutely not" ⇒ ≤40. If the answer says so but the total hasn't reached the cap, you were dazzled by surface discipline (tests/CI/docs) — lower it.
- 60-split check (mandatory, the anchor): answer "which side of the 60 line — ≥60 pass (human-maintainable) or <60 fail (unfit)?" A <60-side answer with total ≥60 ⇒ the mechanical score was fooled by surface quality — force it below 60; a ≥60-side answer with total <60 ⇒ recheck measurements. On conflict, the answer wins.
- Map the total to the nine tiers; round to integer.
- Scoring discipline: evidence-based, neither inflated nor soft; declare low confidence when evidence is thin; totals differing by >3 across two evaluations ⇒ run the rubric's consistency self-check — no bargaining.
Step 6: report
- Output language rule (mandatory): the final report MUST be written in the same language as the user's input (user writes Chinese ⇒ report in Chinese; English ⇒ English; and so on). The skill text itself is English; this rule governs only the delivered report.
- Default: full report in the conversation. With
--output: a condensed version in the conversation, the full report written to the file — state the path.
Report template
## HMAP Report: <project name> (<target path>)
**Total: XX/100 — <tier verdict>**
Scope: N source files, M read (depth: light/medium/deep), confidence: high/medium/low. Measurement sheet: key M1-M16 values (≤5 lines).
### Dimension scores
| Dimension | Raw | Weight | Weighted | One-line rationale |
|---|---|---|---|---|
| D1 Design soundness & honesty | | 25 | | |
| D2 Traceability & mental load | | 30 | | |
| D3 Readability & self-explanation | | 15 | | |
| D4 AI slop & wishful residue | | 30 | | |
### Overall assessment
(3-5 sentences: overall impression and main tensions)
### Strengths (evidenced)
- `file:line` — strength
### Key problems (evidenced, most severe first)
- `file:line` — problem
### AI-slop findings
- `file:line` — slop signal (cite the signal number/name from `references/ai-slop-signals.md`)
### Evidence appendix (mandatory)
- Comment sample classification: every sampled comment → I/R/S/H with its classification reason
- T_trace hop lists for the 3 probed journeys; T_impact probe result; D (decorative abstraction) enumeration
- All boundary decisions (object / both-side reasoning / final choice / evidence)
### Conclusion & recommendations
- Fit for human maintenance? (nine-tier verdict)
- If <60: the 1-3 worst concentrations, and whether a human or an AI agent should fix them
- Hard gates hit (if any) + forced-judgment answer
- Optional improvements (suggest only, never implement)
Boundary discipline
- Assess only, never modify: no refactoring, no bug fixes, no touching any source file.
- No fabricated evidence: judgments without
file:line evidence may not enter the report; verify the cited line exists before citing.
- Honest scoring: the score must be derivable from the measurement sheet and tier tables; declare thin sampling; prefer low confidence over inflation.
- Stay in scope: with
--focus, assess only that dimension.
