Agent skill

Inno Pipeline Planner

by LigphiDonk in LigphiDonk/Oh-my--paper

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

MITAuto-check passedResearch & Science

Install Inno Pipeline Planner

skills CLI
$ npx skills add LigphiDonk/Oh-my--paper --skill inno-pipeline-planner -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LigphiDonk/Oh-my--paper inno-pipeline-planner --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LigphiDonk/Oh-my--paper.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/inno-pipeline-planner .claude/skills/inno-pipeline-planner && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
inno-pipeline-planner
GitHub stars
739
Token cost
~1.8k tokens
SKILL.md length
721 words
Files
6 (incl. references)
Skills in repo
27
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 5 steps: Inspect existing pipeline state → Collect project context via conversation → Write pipeline files → …
  • Tasks that involve Deep research
  • SKILL.md covers Canonical Summary, Trigger Rules, Resource Use Rules and Execution Contract, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Inno Pipeline Planner is an agent skill from LigphiDonk/Oh-my--paper. Guides the user through an interactive conversation to define their research project, then generates researchbrief.json and tasks.json.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `agents/openai.yaml`, `references/brief-schema.md` and `references/generation-rules.md`).

It sits in Research & Science, covering Deep research. The repository describes itself as: A Claude Code plugin that turns your terminal into an autonomous research lab — literature survey, experiment execution, paper writing, all in one pipeline. The licence is MIT.

When your agent uses it

  • Tasks that involve Deep research

Example prompts

  • “Use the inno-pipeline-planner skill to guide the user through an interactive conversation to define their research project, then generates…”
  • “/inno-pipeline-planner”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Inspect existing pipeline state
  2. Collect project context via conversation
  3. Write pipeline files
  4. Summarize and confirm next action
  5. Handle iteration requests

What it can do on your machine

Read from SKILL.md and the folder at commit 6baece9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Inno Pipeline Planner loads about 1.8k tokens when it runs, and up to ~4.9k if it reads all its reference files. Until then it costs about 40 tokens; SKILL.md has 721 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LigphiDonk/Oh-my--paper at commit 6baece9, republished under its MIT licence (© LigphiDonk). 721 words, ~1,756 tokens.

Download SKILL.mdSave it as .claude/skills/inno-pipeline-planner/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
inno-pipeline-planner
description
Guides the user through an interactive conversation to define their research project, then generates research_brief.json and tasks.json.
id
inno-pipeline-planner
version
1.0.0
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…
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

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".
Show full SKILL.md (209 more words)Show less

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 blueprints or tasks for them.
  • Tailor blueprint titles/descriptions to the user topic (never generic filler).
  • Keep quality gates domain-appropriate.
  • Resolve recommended skills from local available skills (.agents/skills/ or skills/), optionally using stage-skill-map.json if present.

4) Summarize and confirm next action

After writing files, present:

  • Brief summary (title, goal, starting stage, filled vs missing sections)
  • Task overview (count by stage + first 2-3 task titles per stage) — only for active stages
  • Recommended first task and why

5) Handle iteration requests

If user asks for updates:

  • Update brief content directly when only text/content changes.
  • Regenerate tasks.json when pipeline structure/blueprints/stages change.
  • If user asks to change the starting stage: update pipeline.startStage in the brief, then regenerate tasks.json to include only the active stages.
  • If asked to add one task only, append a single task with next numeric id instead of full regeneration.

© LigphiDonk, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in skills/inno-pipeline-planner of LigphiDonk/Oh-my--paper.

  • SKILL.md
  • agents/openai.yaml
  • references/brief-schema.md
  • references/generation-rules.md
  • references/pipeline-contract.md
  • references/tasks-schema.md

Open the folder on GitHubat commit 6baece9

Compare with similar skills

Inno Pipeline Planner next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Inno Pipeline Planner compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Inno Pipeline Planner this skillLigphiDonk/Oh-my--paper739—~1.8kAutomated safety check: PassMIT
GitHub Deep Researchbytedance/deer-flow84k4 repos~1.3kAutomated safety check: PassMIT
Deep Research WorkflowTokenRhythm/opensquilla7.1k—~1.3kAutomated safety check: PassApache-2.0
Deep Researchsanjay3290/ai-skills4329 repos~683Automated safety check: NotesApache-2.0
Horizontal-Vertical Deep ResearchKKKKhazix/khazix-skills21k—~2.1kAutomated safety check: PassMIT
Academic Research PipelineImbad0202/academic-research-skills51k—~15kAutomated safety check: PassCustom licence

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Questions about Inno Pipeline Planner

What does Inno Pipeline Planner do?

Guides the user through an interactive conversation to define their research project, then generates researchbrief.json and tasks.json. Inno Pipeline Planner is an agent skill from LigphiDonk/Oh-my--paper.json.

When should I use Inno Pipeline Planner?

Inno Pipeline Planner fits situations like: tasks that involve Deep research.

How do I install Inno Pipeline Planner in Claude Code?

Run `npx skills add LigphiDonk/Oh-my--paper --skill inno-pipeline-planner -a claude-code`. Or copy the skill folder (skills/inno-pipeline-planner in LigphiDonk/Oh-my--paper) into .claude/skills/inno-pipeline-planner in your project. Claude Code loads it when a task matches its description.

How do I install Inno Pipeline Planner in Codex?

Run `npx skills add LigphiDonk/Oh-my--paper --skill inno-pipeline-planner -a codex`. Or copy the skill folder (skills/inno-pipeline-planner in LigphiDonk/Oh-my--paper) into .agents/skills/inno-pipeline-planner in your project. Codex loads it when a task matches its description.

Can I use Inno Pipeline Planner in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LigphiDonk/Oh-my--paper --skill inno-pipeline-planner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/inno-pipeline-planner, .gemini/skills/inno-pipeline-planner, .github/skills/inno-pipeline-planner and .opencode/skills/inno-pipeline-planner in your project.

What does Inno Pipeline Planner need to run?

SKILL.md names no scripts, command-line tools or credentials: Inno Pipeline Planner is instructions for the agent only.

Does Inno Pipeline Planner access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Inno Pipeline Planner safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Inno Pipeline Planner use?

Inno Pipeline Planner is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Inno Pipeline Planner use?

About 1.8k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.1k tokens, read only when the agent opens those files.

What are the alternatives to Inno Pipeline Planner?

Skills that share tags, products or a category with Inno Pipeline Planner: GitHub Deep Research (bytedance/deer-flow, 84k stars), Deep Research Workflow (TokenRhythm/opensquilla, 7.1k stars), Deep Research (sanjay3290/ai-skills, 432 stars) and Horizontal-Vertical Deep Research (KKKKhazix/khazix-skills, 21k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Inno Pipeline Planner?

LigphiDonk (a GitHub user) maintains it in LigphiDonk/Oh-my--paper, which has 739 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on April 15, 2026.

Source: LigphiDonk/Oh-my--paper on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.