agent-infini
Use the InfiniSynapse CLI (`agent_infini`) for multi-turn AI data-analysis tasks,…
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.
$ npx -y skills add xuzhougeng/wisp-science --skill paper-narrative --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/paper-narrativeContext 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.
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
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.
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:
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.
and viewed.
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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