Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dongshuyan/compass-skills --skill academic-humanizergit clone --depth 1 https://github.com/dongshuyan/compass-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dongshuyan/compass-skills/academic-humanizer)<a href="https://agentmods.dev/skills/dongshuyan/compass-skills/academic-humanizer"><img src="https://agentmods.dev/badge/skills/dongshuyan/compass-skills/academic-humanizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dongshuyan/compass-skills/academic-humanizer"><img src="https://agentmods.dev/badge/skills/dongshuyan/compass-skills/academic-humanizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00141 | $0.03462 |
| Opus 5 | $0.00071 | $0.01731 |
| Sonnet 5 | $0.00028 | $0.00692 |
| Haiku 4.5 | $0.00014 | $0.00346 |
Grade A, and why
academic-humanizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 thisSKILL.md. - Let
<python>mean an available Python 3 launcher, such aspython3,py -3, orpython. - 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.yamlis 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:
- Semantic contract for claim preservation, locked spans, deletion safety, and the internal claim ledger.
- Terminology contract for canonical terms, declared aliases, coined names, intentional distinctions, and the internal terminology ledger. Always load it for multi-span or manuscript-level work.
- Global pattern contract for the local-to-document audit, distribution map, scope limits, and whole-document repair. Always load it for multi-sentence work.
- Academic whitelist for protected scholarly forms in both languages.
- Contrast logic for false-opposition triage in English and Chinese. Always load it; this is a cross-language semantic rule.
- Route once by the majority language of editable prose, then read exactly one: English rules or Chinese rules.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 325 B
- references/contrast-logic.md 5.2 KB
- references/examples.md 12 KB
- references/global-pattern-contract.md 8.9 KB
- references/metrics-spec.md 5.5 KB
- references/rules-en.md 7.9 KB
- references/rules-zh.md 9.0 KB
- references/semantic-contract.md 6.9 KB
- references/terminology-contract.md 6.9 KB
- references/whitelist-academic.md 5.8 KB
- scripts/metrics.py 27 KB runs code
- scripts/terminology_audit.py 5.6 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 302 lines · 141 tokens per session scan A 03b2dd14f6a6
academic-humanizer is a skill published in the GitHub repository dongshuyan/compass-skills (724 stars, last pushed 15d ago), licensed MIT. It adds 141 tokens to every session and 3,462 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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