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 Lylll9436/Paper-Polish-Workflow-skill --skill ppw-teamgit clone --depth 1 https://github.com/Lylll9436/Paper-Polish-Workflow-skillWrote 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/lylll9436/paper-polish-workflow-skill/ppw-team)<a href="https://agentmods.dev/skills/lylll9436/paper-polish-workflow-skill/ppw-team"><img src="https://agentmods.dev/badge/skills/lylll9436/paper-polish-workflow-skill/ppw-team/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/lylll9436/paper-polish-workflow-skill/ppw-team"><img src="https://agentmods.dev/badge/skills/lylll9436/paper-polish-workflow-skill/ppw-team.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00053 | $0.02662 |
| Opus 5 | $0.00026 | $0.01331 |
| Sonnet 5 | $0.00011 | $0.00532 |
| Haiku 4.5 | $0.00005 | $0.00266 |
Grade A, and why
ppw:team 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 12d 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
This Skill orchestrates parallel paper processing by splitting a paper into H1 sections and dispatching eligible Skills to subagents. In Phase 19 (current), it validates the proof-of-concept gate by running a single subagent on one section. Full parallel dispatch across all sections is Phase 20.
Trigger
Activates on: ppw:team [skill] [file] invocations.
Examples:
- "ppw:team polish paper.tex" / "团队模式润色论文"
- "ppw:team translation draft.md" / "并行翻译论文各章节"
- "ppw:team de-ai paper.tex"
Modes
| Mode | Default | Behavior |
|---|---|---|
guided |
Yes | split -> user confirms sections -> user selects PoC section -> run PoC -> user confirms quality |
direct |
skip confirmations, auto-select all body sections and first section for PoC |
Mode inference: "直接" or "direct" in trigger switches to direct mode.
References
required: [] -- This Skill does not produce academic text itself. It orchestrates other Skills that load their own references at runtime. No reference files are needed by the orchestrator.
Ask Strategy
Guided mode -- three interaction points via AskUserQuestion:
- Section selection (multiSelect) after splitting
- PoC section selection (single select) from selected sections
- PoC quality confirmation (approve/retry/exit)
Direct mode -- skip all three: auto-select all body sections (exclude preamble and bibliography), auto-select first body section for PoC, auto-approve PoC output.
Workflow
Step 0: Workflow Memory (Recording Only)
- Skip pattern detection -- orchestrator is not suitable for auto-direct-mode recommendation.
- Recording happens after Step 1 validation (see Step 1).
Step 1: Parse Arguments and Validate
- Parse
$ARGUMENTSto extract: Skill name (first word) and file path (remaining words). - Validate Skill name against whitelist:
["translation", "polish", "de-ai"] - If Skill is not in whitelist, reject with exact message: "[Skill] requires full-paper context and cannot run in parallel. Please run /ppw:[skill] directly."
- Validate file exists and is
.texor.mdformat. If file not found or wrong format, report error and stop. - Read the paper file content.
- Record workflow: Append
{"skill": "ppw:team", "ts": "<ISO timestamp>"}to.planning/workflow-memory.json. Create file as[]if missing. Drop oldest entry if log length >= 50.
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.
- 12d ago First seen · 243 lines · 53 tokens per session scan A 5d2761d58100
ppw:team is a skill published in the GitHub repository Lylll9436/Paper-Polish-Workflow-skill (386 stars, last pushed 5mo ago), licensed MIT. It adds 53 tokens to every session and 2,662 once invoked, about $0.0003 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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