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Skill

/inno-pipeline-planner

Guides the user through an interactive conversation to define their research project, then generates research_brief.json and tasks.json.

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oh-my-paper
74336 skills5 agents15 commands
Install
$ npx -y skills add LigphiDonk/Oh-my--paper --skill inno-pipeline-planner --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/inno-pipeline-planner

Context preview

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

Guides the user through an interactive conversation to define their research project, then generates research_brief.json and tasks.json.

SKILL.md

inno-pipeline-planner.SKILL.md
id: inno-pipeline-planner
name: inno-pipeline-planner
version: 1.0.0
description: |-
  Guides the user through an interactive conversation to define their research project, then generates research_brief.json and tasks.json.
stages: ["ideation"]
tools: ["read_file", "search_project", "write_file"]
summary: |-
  Guides the user through an interactive conversation to define their research project, then generates research_brief.json and tasks.json. Use when starting a new project, when no research_brief.json exists, when the user wants to start from...
primaryIntent: research
intents: ["research", "experiment"]
capabilities: ["research-planning", "agent-workflow"]
domains: ["general"]
keywords: ["inno-pipeline-planner", "research-planning", "agent-workflow", "inno", "pipeline", "planner", "guides", "user", "through", "interactive", "conversation", "define"]
source: builtin
status: verified
upstream:
  repo: dr-claw
  path: skills/inno-pipeline-planner
  revision: 8322dc4ef575affaa374aa7922c0a0971c6db7d7
resourceFlags:
  hasReferences: true
  hasScripts: false
  hasTemplates: false
  hasAssets: false
  referenceCount: 4
  scriptCount: 0
  templateCount: 0
  assetCount: 0
  optionalScripts: false

inno-pipeline-planner

Canonical Summary

Guides the user through an interactive conversation to define their research project, then generates research_brief.json and tasks.json. Use when starting a new project, when no research_brief.json exists, when the user wants to start from...

Trigger Rules

Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.

Resource Use Rules

  • Read from `references/` only when the current task needs the extra detail.

Execution Contract

  • Resolve every relative path from this skill directory first.
  • Prefer inspection before mutation when invoking bundled scripts.
  • If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
  • Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

Upstream Instructions

Inno Pipeline Planner

Run an interactive planning flow that turns user conversation into:

  • `.pipeline/docs/research_brief.json`
  • `.pipeline/tasks/tasks.json`

Keep this file short. Load full schemas and field-level rules from:

  • `references/pipeline-contract.md` (index)

Read only what you need:

  • `references/generation-rules.md`: generation logic, ordering, dependencies, `nextActionPrompt`
  • `references/brief-schema.md`: `.pipeline/docs/research_brief.json` contract
  • `references/tasks-schema.md`: `.pipeline/tasks/tasks.json` contract

Non-negotiables

  • Work only inside the current project directory.
  • Do not fabricate papers, datasets, metrics, or results.
  • Ask follow-up questions when information is vague; do not guess.
  • Ask in small batches (2-3 questions), not a long static form.

Workflow

1) Inspect existing pipeline state

Check:

  • `.pipeline/docs/research_brief.json`
  • `.pipeline/tasks/tasks.json`
  • `instance.json` (legacy source)
  • Content in `Survey/`, `Ideation/`, `Experiment/`, `Publication/`, and `Promotion/` directories (to detect pre-existing artifacts)

If brief exists, summarize title, goal, current `startStage`, and completion status, then ask:

  • Refine existing brief/tasks
  • Regenerate from scratch
  • Change the starting stage

2) Collect project context via conversation

Capture at least:

  • Topic/problem
  • Goal or hypothesis
  • Success criteria or evaluation signal
  • Current survey depth or known reference set

**Determine the starting stage** early in the conversation:

  • Ask what the user already has: "Do you already have a research idea, experimental results, or are you starting from scratch?"
  • If the user mainly needs literature review, gap analysis, or reference collection -> `startStage = "survey"`
  • If the user has a concrete idea with problem framing and success criteria -> `startStage = "experiment"`
  • If the user has experimental results and analysis -> `startStage = "publication"`
  • If the user already has a paper/manuscript and mainly needs a homepage, slide deck, narration, or demo assets -> `startStage = "promotion"`
  • If the user is starting from scratch or only has a vague direction -> `startStage = "survey"` (default)
  • Detect automatically from conversation context (e.g., "I already ran all experiments" implies publication; "I need slides for my paper" implies promotion).

Typical question buckets:

  • Project identity: topic, prior paper/method/dataset, target venue (optional)
  • Scope and method: core question, approach, expected outcome
  • Evaluation: data source, metrics/protocol, baseline expectations

Adapt to context:

  • Skip already-provided details.
  • **Skip questions for stages before `startStage`**: If starting from experiment, do not ask survey or ideation questions in detail — just capture a brief summary of the existing context in those sections.
  • If exploratory, keep experiment/publication/promotion sections lightweight.
  • If user provides concrete plan, prepare for `pipeline.mode = "plan"`; otherwise use `"idea"`.

3) Write pipeline files

Create if missing:

  • `.pipeline/config.json`
  • `.pipeline/docs/research_brief.json`
  • `.pipeline/tasks/tasks.json`

Use the exact JSON contracts and generation rules in:

  • `references/pipeline-contract.md` and linked reference files

Rules:

  • Set `pipeline.startStage` to the determined starting stage (default: `"survey"`).
  • **Generate tasks only for stages >= `startStage`** in the stage order (survey < ideation < experiment < publication < promotion).
  • For skipped stages: still populate their `sections.*` fields in the brief with whatever context the user provided, but do not create task bluepr
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Ships withoh-my-paper

A Claude Code plugin that turns your terminal into an autonomous research lab — literature survey, experiment execution, paper writing, all in one pipeline.

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