ppwabstract

name	ppw:abstract
description	Generate or optimize academic paper abstracts using the 5-sentence Farquhar formula. Supports generate-from-scratch and restructure-existing paths. Produces labeled output for formula verification plus a clean version for clipboard use. 摘要生成与优化，支持从原始材料生成或改写现有摘要。
triggers	{"primary_intent":"generate or optimize academic paper abstract","examples":["Write an abstract for my paper","帮我写摘要","Optimize my existing abstract","改写我的摘要，符合五句话公式","Generate a submission-ready abstract for CEUS","帮我生成一个投稿摘要","Restructure my abstract using the Farquhar formula","优化摘要让它更符合学术规范"]}
tools	["Read","Write","Structured Interaction"]
references	{"required":["references/expression-patterns.md","references/bilingual-output.md"],"leaf_hints":["references/expression-patterns/introduction-and-gap.md","references/expression-patterns/results-and-discussion.md","references/expression-patterns/conclusions-and-claims.md","references/expression-patterns/methods-and-data.md"]}
input_modes	["file","pasted_text"]
output_contract	["labeled_abstract","clean_abstract","bilingual_abstract"]

Purpose

This Skill generates or optimizes academic paper abstracts using the locked 5-sentence Farquhar formula: [1: Contribution], [2: Difficulty], [3: Method], [4: Evidence], [5: Key Result]. It operates on two mutually exclusive paths — generate a new abstract from raw materials (paper sections, bullet points, or a brief description), or restructure an existing abstract to comply with the formula. Both paths produce labeled output first so users can verify formula compliance sentence by sentence, followed by a clean plain-paragraph version ready for clipboard use. When a target journal is specified, journal-specific style conventions are applied.

Trigger

Activates when the user asks to:

• Write, generate, or create an abstract from paper content
• Optimize, restructure, or rewrite an existing abstract
• 写摘要、生成摘要、改写摘要、优化摘要

Example invocations:

• "Write an abstract for my paper" / "帮我写摘要"
• "Optimize my existing abstract" / "改写我的摘要"
• "Restructure this abstract using the 5-sentence formula"
• "Generate a CEUS-ready abstract from my introduction and results"

Modes

Mode	Default	Behavior
direct	Yes	Single-pass abstract output using the 5-sentence formula
batch		Not supported — abstract requires full paper context

Default mode: direct. User provides content and receives labeled + clean abstract in one pass.

Path selection (within direct mode):

• Input reads like a formed abstract → restructure path
• Input is raw content (sections, bullets, "my paper is about...") → generate path
• Ambiguous → ask before proceeding

References

Required (always loaded)

File	Purpose
references/expression-patterns.md	Academic expression patterns overview

Leaf Hints (loaded when needed)

File	When to Load
references/expression-patterns/introduction-and-gap.md	Contribution and gap framing (sentences 1-2)
references/expression-patterns/conclusions-and-claims.md	Calibrated claim language (sentences 1 and 5)
references/expression-patterns/methods-and-data.md	Method description (sentence 3, when weak)
references/expression-patterns/results-and-discussion.md	Evidence and result framing (sentences 4-5)

Journal Template (conditional)

• When user specifies a target journal, load references/journals/[journal].md.
• If template missing, refuse: "Journal template for [X] not found. Available: CEUS."
• If no journal specified, ask once; if declined, apply general academic style.

Ask Strategy

Before starting, ask about:

1. Path: does the user have an existing abstract to restructure, or raw content to generate from? (skip if clear from input)
2. Target journal if not specified (ask once; if declined, use general academic style)
3. Word limit if known (typically 150-300 words; ask once)

Path inference:

• User pastes text that reads like an abstract → restructure path
• User provides sections, bullets, or "my paper is about..." → generate path
• Ambiguous → ask: "Do you have an existing abstract to restructure, or shall I generate one from your materials?"

Rules:

• Never ask more than 2 questions before starting.
• Skip path question if inference is clear from input.
• In direct mode with sufficient context, proceed without pre-questions.

Workflow

Step 0: Workflow Memory Check

• Read .planning/workflow-memory.json. If file missing or empty, skip to Step 1.
• Check if the last 1-2 log entries form a recognized pattern with ppw:abstract that has appeared >= threshold times in the log. See skill-conventions.md > Workflow Memory > Pattern Detection for the full algorithm.
• If a pattern is found, present recommendation via AskUserQuestion:
  • Question: "检测到常用流程：[pattern]（已出现 N 次）。是否直接以 direct 模式运行 ppw:abstract？"
  • Options: "Yes, proceed" / "No, continue normally"
• If user accepts: set mode to direct, skip Ask Strategy questions.
• If user declines or AskUserQuestion unavailable: continue in normal mode.

Step 1: Collect Context

• Load references/expression-patterns.md overview.
• If journal specified, load references/journals/[journal].md. If missing, refuse with: "Journal template for [X] not found. Available: CEUS."
• Read user input: file via Read tool, or pasted text from conversation.
• Determine path: restructure if input reads like a formed abstract; generate if raw materials. Ask if ambiguous.
• Opt-out check: Scan the user's trigger prompt for any of these phrases (case-insensitive, exact phrase match): english only, no bilingual, only english, 不要中文. Store result as bilingual_mode (true/false). This flag governs Step 3 output below.
• Record workflow: Append {"skill": "ppw:abstract", "ts": "<ISO timestamp>"} to .planning/workflow-memory.json. Create file as [] if missing. Drop oldest entry if log length >= 50.

Step 2a: Generate Path (raw content provided)

• Load references/expression-patterns/introduction-and-gap.md for contribution and gap framing.
• Load references/expression-patterns/conclusions-and-claims.md for calibrated claim language.
• Load references/expression-patterns/methods-and-data.md if the user's method description is weak.
• Synthesize sentence 1 (contribution): start with "We introduce / propose / demonstrate / show..."
• Synthesize sentence 2 (difficulty): why this problem is hard or why it matters.
• Synthesize sentence 3 (method): how the problem is solved, with key technical terminology.
• Load references/expression-patterns/results-and-discussion.md for sentences 4-5.
• Synthesize sentence 4 (evidence): what was measured, on what benchmark or dataset.
• Synthesize sentence 5 (key result): the most important number or qualitative outcome.

Step 2b: Restructure Path (existing abstract provided)

• Load the same leaf hints as the generate path.
• Map each existing sentence to one of the 5 formula positions.
• Rewrite each sentence to tighten formula compliance while preserving the user's content.
• Internal audit: After restructuring, verify all 5 positions are filled.
• Flag any missing position with [MISSING: position-name] — never silently drop content.

Step 3: Output

• Present the labeled version first (formula positions explicit, see Output Contract).

• Present the clean version (plain paragraph, no labels) separated by ---.

• If the user wants to save: offer to write to a file using the Write tool.

• If word limit was specified, report word count; warn if over limit.

• Bilingual display: If bilingual_mode is true: after presenting the labeled version and after presenting the clean version, append a > **[Chinese]** ... blockquote for each sentence of the abstract. Use a header: "双语对照 / Bilingual Comparison:" before the blockquotes. Format:

  [Chinese] [1: 贡献] ... [Chinese] [2: 难度] ... [Chinese] [3: 方法] ... [Chinese] [4: 证据] ... [Chinese] [5: 关键结果] ...

  Each blockquote corresponds to one Farquhar formula sentence. Label prefixes in Chinese are for readability only -- the clean version Chinese follows the plain paragraph structure without labels.

• If bilingual_mode is false (opt-out detected): skip bilingual display entirely.

• Optionally recommend the Polish Skill for further expression refinement.

Output Contract

Output	Format	Condition
labeled_abstract	Labeled 5-sentence block with [N: Position] markers	Always
clean_abstract	Plain paragraph with no labels, separated by ---	Always
Word count	Integer	When user specified a word limit
bilingual_abstract	> **[Chinese]** ... blockquotes in session (one per sentence)	When bilingual_mode is true (default). Skipped when opt-out detected.

Exact labeled format:

[1: Contribution] We propose...
[2: Difficulty] Despite growing interest in X, existing approaches fail to...
[3: Method] We address this by...
[4: Evidence] Evaluated on N datasets...
[5: Key Result] Our approach achieves...

---
Clean version:
We propose... Despite growing interest in X, existing approaches fail to... We address this by... Evaluated on N datasets... Our approach achieves...

Edge Cases

Situation	Handling
Input is ambiguous (not clearly raw content or existing abstract)	Ask before proceeding: "Do you have an existing abstract to restructure, or shall I generate one?"
Existing abstract has fewer than 5 sentences	Run restructure path; flag missing positions with [MISSING: ...]
User specifies word limit	Count words in output; warn if over limit with exact count
Journal specified but template missing	Refuse: "Journal template for [X] not found. Available: CEUS."
Formula position cannot be filled from available content	Flag with [MISSING: difficulty statement]; do not invent content
User provides only a title or one-sentence summary	Ask for more content before proceeding

Fallbacks

Scenario	Fallback
Structured Interaction unavailable	Ask 1-2 plain-text questions (path + journal)
Expression pattern leaf missing	Proceed with general academic register; warn user
Journal template missing (no journal specified)	Ask once; if declined, use general academic style
Write tool fails	Present output in conversation instead

Examples

Invocation: "Help me restructure my abstract to follow the 5-sentence formula."

User pastes: "In this paper we study urban mobility patterns using GPS data. We find that mobility is highly predictable. We propose a model called MobNet. It outperforms existing approaches by 12% on the next-location prediction task."

Output:

[1: Contribution] We propose MobNet, a model for predicting urban mobility using GPS data.
[2: Difficulty] Despite the prevalence of GPS data, existing mobility models fail to capture [MISSING: difficulty statement — why prediction is hard or why existing approaches fall short].
[3: Method] MobNet addresses this by [MISSING: method description — how MobNet works].
[4: Evidence] Evaluated on a next-location prediction task using GPS traces from urban environments.
[5: Key Result] MobNet outperforms existing approaches by 12% on next-location prediction.

---
Clean version:
We propose MobNet, a model for predicting urban mobility using GPS data. [MISSING: difficulty statement]. MobNet addresses this by [MISSING: method description]. Evaluated on a next-location prediction task using GPS traces from urban environments. MobNet outperforms existing approaches by 12% on next-location prediction.

Two formula positions are flagged as missing. Please provide: (1) why mobility prediction is hard or what existing approaches fail to do, and (2) a brief description of how MobNet works.

---

Skill: abstract-skill Conventions: references/skill-conventions.md