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Agent skill

Iflow Graphify

by jepegit in jepegit/cellpy

Rebuild the graphify knowledge graph (graphify-out/) by shelling out to issue-flow graphify or graphify directly.

MITAuto-check passedKnowledge Management

Install Iflow Graphify

skills CLI
$ npx skills add jepegit/cellpy --skill iflow-graphify -a claude-code

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

GitHub CLI
$ gh skill install jepegit/cellpy iflow-graphify --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/jepegit/cellpy.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.cursor/skills/iflow-graphify .claude/skills/iflow-graphify && 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
iflow-graphify
GitHub stars
109
Token cost
~960 tokens
SKILL.md length
450 words
Files
1
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

Rebuild the graphify knowledge graph (graphify-out/) by shelling out to issue-flow graphify or graphify directly.

  • Works in 5 steps: Prefer issue-flow graphify from the… → Fallback to graphify directly when… → If graphify exits with "no LLM API key… → …
  • Tasks that involve Knowledge graphs
  • SKILL.md covers Instructions and Constraints
  • Calls uv, pipx and pip; needs GEMINI_API_KEY and GOOGLE_API_KEY

What it does

Iflow Graphify is an agent skill from jepegit/cellpy. Rebuild the graphify knowledge graph (graphify-out/) by shelling out to issue-flow graphify or graphify directly.

Its SKILL.md is about 960 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering Knowledge graphs. It works with Ollama. The repository describes itself as: extract and tweak data from electrochemical tests of cells. The licence is MIT.

When your agent uses it

  • Tasks that involve Knowledge graphs

Example prompts

  • “/iflow-graphify”

Requirements

  • Python 3
  • A credential in GEMINI_API_KEY
  • A credential in GOOGLE_API_KEY

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Prefer issue-flow graphify from the project root
  2. Fallback to graphify directly when issue-flow is unavailable
  3. If graphify exits with "no LLM API key found", the user picked extract (or another semantic subcommand) without configuring a backend…
  4. Handle missing graphify gracefully. If the run reports graphify is not on PATH, do not retry blindly. Tell the user to install it once
  5. Verify and report.

What it can do on your machine

Read from SKILL.md and the folder at commit ff2c665. 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

    Shell commands in SKILL.md call:

    • uv
    • pipx
    • pip

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

  • Network

    Links to these hosts (documentation or services it may open):

    • iflow-graphify.net
    • ollama.com

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GEMINI_API_KEY
    • GOOGLE_API_KEY
    • ANTHROPIC_API_KEY
    • OPENAI_API_KEY
    • MOONSHOT_API_KEY

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

Context cost

Iflow Graphify loads about 960 tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 450 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~960

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 jepegit/cellpy at commit ff2c665, republished under its MIT licence (© jepegit). 450 words, ~960 tokens.

Download SKILL.mdSave it as .claude/skills/iflow-graphify/SKILL.md (or your agent's skills folder).
name
iflow-graphify
description
Rebuild the graphify knowledge graph (graphify-out/) by shelling out to `issue-flow graphify` or `graphify` directly.
disable-model-invocation
true
issue-flow-version
0.4.2a4

issue-flow — graph rebuild (/iflow-graphify)

Follow this skill to refresh the project's graphify knowledge graph — a stale graphify-out/ after a large refactor, or the initial graph after installing graphifyy.

Do not use this skill from /iflow-build, /iflow-close, or /iflow. /iflow-graphify is opt-in only.

Invoke: type iflow graphify in chat, or /iflow-graphify from the slash menu (iflow-graphify also works).

MODEL & EXECUTION DIRECTIVE

Profile: economy — Prioritize speed and token economy over deep reasoning.

In Cursor: use Auto or a fast model before invoking this step.

Keep scope tight to what this step requires.

Instructions

  1. Prefer issue-flow graphify from the project root:

    bash
    issue-flow graphify

    With no extra args this runs graphify update <project> — AST-only, no LLM API key required, produces the full graphify-out/. To pick a different graphify subcommand, pass it as the first arg: issue-flow graphify extract (adds the slower semantic LLM pass for richer relationships — needs an API key), issue-flow graphify watch (live), issue-flow graphify cluster-only --no-viz, etc. Use -C <dir> to scan a project other than the current directory. Trailing flags pass through verbatim. Do not invent new wrapper flags.

  2. Fallback to graphify directly when issue-flow is unavailable:

    bash
    graphify update .

    graphify is subcommand-based — graphify . on its own is not valid (graphify reports unknown command '.'). Always pick a subcommand: update for the no-LLM AST build, extract for the full semantic pass, watch for a long-running watcher, etc.

  3. If graphify exits with "no LLM API key found", the user picked extract (or another semantic subcommand) without configuring a backend. Cursor's own LLM is not available to subprocesses, so graphify cannot reuse it. Suggest one of:

    • Set an API key for GEMINI_API_KEY / GOOGLE_API_KEY, ANTHROPIC_API_KEY, OPENAI_API_KEY, or MOONSHOT_API_KEY.
    • Run issue-flow graphify extract --backend ollama to use a local LLM via Ollama (requires Ollama installed with a model pulled).
    • Drop the extract arg and use the default issue-flow graphify (AST-only, no LLM).

  4. Handle missing graphify gracefully. If the run reports graphify is not on PATH, do not retry blindly. Tell the user to install it once:

    bash
    uv tool install graphifyy   # recommended
pipx install graphifyy
pip install graphifyy

    graphifyy (double-y) is the official PyPI package; the CLI is still graphify. After installing, suggest issue-flow update so graphify cursor install registers the graphify Cursor skill alongside this one.

  5. Verify and report.

    • Confirm graphify-out/graph.json, graphify-out/graph.html, and graphify-out/GRAPH_REPORT.md exist after a successful run.
    • Surface non-zero exit codes verbatim; do not silently retry.
    • When the user asks "what changed?", skim GRAPH_REPORT.md (god nodes, surprising connections) for a short summary.
Show full SKILL.md (52 more words)Show less

Constraints

  • Never auto-dispatch /iflow-graphify from another slash command. The user opts in explicitly.
  • Never commit graphify-out/cost.json or graphify-out/manifest.json; they are local-only.
  • Long-running modes (watch) keep the process alive; ask the user before launching them in an agent context.
  • Forward extra arguments verbatim. Do not translate or rewrite graphify's flag set inside issue-flow.

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

Files

Just SKILL.md in .cursor/skills/iflow-graphify of jepegit/cellpy.

Open the folder on GitHubat commit ff2c665

Compare with similar skills

Iflow Graphify 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.

Iflow Graphify compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Iflow Graphify this skilljepegit/cellpy109—~960Automated safety check: PassMIT
Scholar RAGjoshzyj/open-scholar-skill167—~7.4kAutomated safety check: NotesCustom licence
LLM Wiki Knowledge GraphEgonex-AI/Understand-Anything85k1 repos~1.5kAutomated safety check: PassMIT
Obsidian Canvas BoardsAgriciDaniel/claude-obsidian15k—~1.4kAutomated safety check: PassMIT
Ontology1mancompany/OneManCompany4382 repos~1.5kAutomated safety check: PassApache-2.0
Graphagenticnotetaking/arscontexta3.5k1 repos~4.9kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Iflow Graphify

What does Iflow Graphify do?

Rebuild the graphify knowledge graph (graphify-out/) by shelling out to issue-flow graphify or graphify directly. Iflow Graphify is an agent skill from jepegit/cellpy. Rebuild the graphify knowledge graph (graphify-out/) by shelling out to issue-flow graphify or graphify directly.

When should I use Iflow Graphify?

Iflow Graphify fits situations like: tasks that involve Knowledge graphs.

How do I install Iflow Graphify in Claude Code?

Run `npx skills add jepegit/cellpy --skill iflow-graphify -a claude-code`. Or copy the skill folder (.cursor/skills/iflow-graphify in jepegit/cellpy) into .claude/skills/iflow-graphify in your project. Claude Code loads it when a task matches its description.

How do I install Iflow Graphify in Codex?

Run `npx skills add jepegit/cellpy --skill iflow-graphify -a codex`. Or copy the skill folder (.cursor/skills/iflow-graphify in jepegit/cellpy) into .agents/skills/iflow-graphify in your project. Codex loads it when a task matches its description.

Can I use Iflow Graphify 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 jepegit/cellpy --skill iflow-graphify -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/iflow-graphify, .gemini/skills/iflow-graphify, .github/skills/iflow-graphify and .opencode/skills/iflow-graphify in your project.

What does Iflow Graphify need to run?

Going by SKILL.md and its folder, Iflow Graphify needs the command-line tools its instructions call (uv, pipx and pip) and credentials named GEMINI_API_KEY, GOOGLE_API_KEY, ANTHROPIC_API_KEY and OPENAI_API_KEY. Our summary lists: Python 3; A credential in GEMINI_API_KEY; A credential in GOOGLE_API_KEY.

Does Iflow Graphify access the network?

SKILL.md names 2 domains. As links in the text: iflow-graphify.net and ollama.com. This is read from the text; nothing was executed.

Is Iflow Graphify 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 Iflow Graphify use?

Iflow Graphify 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 Iflow Graphify use?

About 960 tokens (SKILL.md is roughly 3.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Iflow Graphify?

Skills that share tags, products or a category with Iflow Graphify: Scholar RAG (joshzyj/open-scholar-skill, 167 stars), LLM Wiki Knowledge Graph (Egonex-AI/Understand-Anything, 85k stars), Obsidian Canvas Boards (AgriciDaniel/claude-obsidian, 15k stars) and Ontology (1mancompany/OneManCompany, 438 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Iflow Graphify?

jepegit (a GitHub user) maintains it in jepegit/cellpy, which has 109 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on October 6, 2026.

Source: jepegit/cellpy on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.