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/paper-narrative

Judge and reshape the story told by a manuscript and its figure deck. Use when revising paper structure, testing whether Figure 1 is a hook, ordering figures, moving panels, identifying missing analyses, or defining the claim passed to figure-composer.

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wisp-science
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$ npx -y skills add xuzhougeng/wisp-science --skill paper-narrative --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/paper-narrative

Context preview

The summary Claude sees to decide when to auto-load this skill.

Judge and reshape the story told by a manuscript and its figure deck. Use when revising paper structure, testing whether Figure 1 is a hook, ordering figures, moving panels, identifying missing analyses, or defining the claim passed to figure-composer.

SKILL.md

paper-narrative.SKILL.md
name: paper-narrative
description: Judge and reshape the story told by a manuscript and its figure deck. Use when revising paper structure, testing whether Figure 1 is a hook, ordering figures, moving panels, identifying missing analyses, or defining the claim passed to figure-composer.
license: Apache-2.0

Paper narrative

Use this skill before `figure-composer`. The sidecar only builds schemas and self-contained reviewer instructions. Model reasoning happens in the current Agent or an explicitly delegated Wisp Agent, never inside Python.

Workflow

1. Read the abstract, introduction, captions, and concrete figure paths. 2. Build an instruction with `paper_brief_task(...)`. Produce a brief matching `paper_brief_schema()` in the current Agent. If `delegate_tasks` is advertised, the brief may instead be assigned to one `reasoning` task with a structured output schema. 3. Review the entire brief. Preserve supplied `composite_path` values and fix unsupported claims before continuing. The brief and review instructions include JSON figure records so each path stays associated with its claim; keep those records when delegating, including paths with spaces or Unicode. 4. If the deck is a PDF, use `pdf-explore` to render the relevant pages to local images. Pass their concrete paths to `narrative_review_task(...)`. 5. Inspect the images with `view_image`. Optionally delegate one handling-editor task with the minimum advertised `reasoning`, `project_read`, and `image_inspection` capabilities and `narrative_review_schema()`. 6. Act on the result:

  • use `arc` as the main-figure order;
  • apply `figure_moves`;
  • turn `missing_panels` into explicit analyses;
  • demote or remove the `kill_list`;
  • send `boldest_defensible_fig1` to `figure-composer`.

7. Re-review the revised deck. Converge when the hook verdict is `yes` and no figure moves or missing panels remain.

Use `run_in_context` only for deterministic analyses requested by `missing_panels`. Narrative reasoning and Agent delegation are not Runs.

Boundaries

  • Do not pass artifact markers where a path is required.
  • Do not call another model from `python`.
  • Do not claim the deck was inspected unless every relevant page was rendered

and viewed.

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The open-source, local-first AI research workbench. WISP — Workspace for Intelligent Scientific Practice Search the literature, run Python and R, query ~80 scientific databases, and keep the trail — figures, runs, decisions, and drafts — in one project.

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Repo: xuzhougeng/wisp-science

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