Ppw Abstract

Modes

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)

Leaf Hints (loaded when needed)

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

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

Fallbacks

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