git clone --depth 1 https://github.com/dongshuyan/compass-skills /tmp/academic-humanizer && cp -r /tmp/academic-humanizer/skills/academic-humanizer ~/.claude/skills/academic-humanizerSKILL.md
# Academic Humanizer Improve academic prose by removing observable writing defects, not by imitating imperfection or optimizing an authorship detector. Preserve the author's facts, argument, uncertainty, and disciplinary voice. This skill does not guarantee how any reader or detector will classify a text. ## Portability This skill is agent-agnostic. Its core behavior is defined by `SKILL.md` and `references/`; Python is optional and supports reproducible diagnostics. - Resolve `<skill-dir>` from the directory containing this `SKILL.md`. - Let `<python>` mean an available Python 3 launcher, such as `python3`, `py -3`, or `python`. - Let `<input-file>` mean a user-authorized local text file. Quote paths that contain spaces and use the host shell's path separator. - Do not assume a fixed skill root, home directory, shell, operating system, agent name, or path separator. - `agents/openai.yaml` is optional interface metadata. Core behavior does not depend on a particular agent runtime. - If Python is unavailable, skip the scripts and apply the same contracts directly. ## Load the operating references Read these before drafting or editing: 1. [Semantic contract](references/semantic-contract.md) for claim preservation, locked spans, deletion safety, and the internal claim ledger. 2. [Terminology contract](references/terminology-contract.md) for canonical terms, declared aliases, coined names, intentional distinctions, and the internal terminology ledger. Always load it for multi-span or manuscript-level work. 3. [Global pattern contract](references/global-pattern-contract.md) for the local-to-document audit, distribution map, scope limits, and whole-document repair. Always load it for multi-sentence work. 4. [Academic whitelist](references/whitelist-academic.md) for protected scholarly forms in both languages. 5. [Contrast logic](references/contrast-logic.md) for false-opposition triage in English and Chinese. Always load it; this is a cross-language semantic rule. 6. Route once by the majority language of editable prose, then read exactly one: [English rules](references/rules-en.md) or [Chinese rules](references/rules-zh.md). Read [worked examples](references/examples.md) on first use, after changing a rule, or whenever fact preservation, contrast, or over-correction is uncertain. Read [metrics specification](references/metrics-spec.md) before running `scripts/metrics.py`; its output is descriptive evidence only. ## Supported operations - **generate**: draft from user-supplied claims, outline, data, and sources. - **detect**: identify high-confidence defects without rewriting. - **rewrite**: minimally revise supplied prose; this is the default when the user asks to de-AI or humanize text. - **edit**: apply the same minimal revisions to a named file. Do not create another routing tree for paper section or discipline. Methods, Results, Discussion, reviewer responses, and grants use the same contracts; the whitelist handles legitimate register differences. Ask one direct question only when the requested genre changes what counts as acceptable and context does not resolve it. ## Language route Route on editable prose, excluding fenced code, formulas, block quotations, and a trailing reference list. Use orthographic tokens: each CJK character is one token and each contiguous Latin word is one token. This keeps embedded terms such as `Transformer` or `ImageNet` from outweighing the Chinese sentence around them: `r = CJK tokens / (CJK tokens + Latin word tokens)` - `r >= 0.5`: Chinese branch. - `r < 0.5`: English branch. - No countable prose: stop and ask for text or an intended output language. English terms in Chinese prose and Chinese terms in English prose remain verbatim. If Python is available and the route is genuinely unclear, optionally run `<python> "<skill-dir>/scripts/metrics.py" "<input-file>" --route`. Routing is internal and never appears in the clean artifact. ## Single arbitration order Earlier rows win. References may elaborate this table but must not define a second priority order. | Priority | Constraint | Operational meaning | |---|---|---| | **C0** | Artifact boundary | Process instructions, editor narration, and tool residue never enter the artifact. C0 applies only to process-layer text; it never authorizes deletion of real content. | | **C1** | Semantic fidelity | Every output claim maps to the source bundle; every material source claim remains represented. No added facts, relations, examples, citations, motivations, or limitations. | | **C2** | Locked-span protection | Quotations, formulas, code, references, citation keys, statistical notation, proper nouns, and requested verbatim text remain unchanged. | | **C3** | Terminology identity | One scientific concept uses one canonical term across the editable manuscript. Preserve declared full-name/abbreviation pairs, necessary grammatical forms, and intentional distinctions; never infer identity from similarity alone. | | **C4** | Academic register | Preserve functional hedging, passive voice, nominalization, discourse markers, and Chinese scholarly morphology. | | **C5** | Argument structure | Preserve causal strength, contrast, concession, addition, chronology, scope, and paragraph-level reasoning. Surface connectives may change when the relation survives. | | **C6** | Document patterning | Audit recurrence, clustering, dispersion, positional regularity, sentence rhythm, and rhetorical-function saturation across the complete editable scope. A count is evidence, never a verdict. | | **C7** | Local style repair | Apply language-specific rules only to locally unsupported, vacuous, mechanical, or stacked defects. | Examples of conflict resolution: - A style rule suggests adding a number, mechanism, baseline, or limitation that is absent from the source: C1 blocks the addition. - A leak and a result share one sentence: C0 removes only the process phrase; C1 and C5 pre
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Maintains a repo-local task forest or task DAG for the current workspace. Use when the user asks to initialize or update a task forest, close a session, summarize evolving project work, align a request with a global goal, track progress/history/deviations/todos, save or apply a task proposal, or export the client-readable task-forest HTML. Do not use for executing the tracked tasks themselves or for generic HTML work.
Local user-profile maintenance skill for Codex, Claude Code, OpenClaw, OpenCode, and other agent harnesses. Use only when the user explicitly invokes $user-profile-keeper to create, initialize, update, query, correct, delete, export, or audit a local persistent user profile. It can extract durable collaboration preferences, requirement-expression habits, capability boundaries, recurring omissions, risk preferences, privacy boundaries, and typical events from the current session into auditable, confirmable, retractable local profile data. Do not auto-invoke, upload profile data, or replace task-clarifier's normal clarification flow.
Create a concise continuation prompt that a fresh agent session can paste in to resume a long or degraded session. Use when the user asks for a handoff prompt, restart prompt, continuation prompt, context transfer, fresh-session resume, or a compact summary for opening a new session. Do not use for ordinary summaries, task-forest maintenance, durable user-profile updates, automatic session creation, code execution, or external publishing.
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Turn a completed task, browser flow, artifact pipeline, failure-recovery trace, or repeatedly refined workflow into a new reusable skill package or a reviewed skill-design plan. Use when the user asks to make a new skill from real run history, extract a reusable workflow from conversation/logs/files, summarize lessons into a new skill, or produce a plan before writing files. Do not use to upgrade an existing skill or to execute the business workflow itself.
Use real run evidence, validation failures, source drift, platform drift, and user feedback to plan and, only after explicit approval, apply structural upgrades to an existing skill. Use when the user asks to improve an existing skill from recent runs, recurring failures, outdated sources, excessive bloat, changed platform behavior, or validated workflow feedback. Do not use to create a brand-new skill or to execute the business workflow itself.