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Hep Graph

OrganizationPopular
agentlas-ai
hep-graph

Build an Agentlas automation by describing it, list saved ones, or request a run.

Overview

Publisheragentlas-ai
RepositoryAgentlas-OS
Skill namehep-graph
Stars
1.6K
Forks
111
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by agentlas-ai on GitHub. Read the source before you install it.

Installation

Install the Hep Graph AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agentlas-ai/Agentlas-OS/tree/main/kimi/skills/hep-graph
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS.git /tmp/Agentlas-OS
mkdir -p .claude/skills
cp -r /tmp/Agentlas-OS/kimi/skills/hep-graph .claude/skills/hep-graph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hep Graph in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hep Graph on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hep Graph is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Update fallback: 자동 업데이트가 안 되면 hephaestus update를 한 번 실행하세요. 업데이트하지 않아도 현재 버전 명령은 그대로 동작합니다.

/hep-graph

Saved automation graphs live in the local Agentlas database, shared with the desktop app. This command reads that database and can ask for a graph to run.

Raw arguments: $ARGUMENTS

What this command can and cannot do. It lists graphs, shows what a graph does, and requests a run. It does not execute the graph — the desktop app is what runs it. Say that plainly when you report back; do not tell the user their automation ran.

Locate the CLI

bash
CLI=""
for candidate in \
  "$(command -v agentlas 2>/dev/null)" \
  "$HOME/.agentlas/runtime/current/bin/agentlas" \
  "./bin/agentlas"
do
  if [ -n "$candidate" ] && [ -x "$candidate" ]; then CLI="$candidate"; break; fi
done
[ -n "$CLI" ] || { echo "Agentlas CLI not found. Install it with: npm i -g agentlas" >&2; exit 1; }

New — build one by talking it through

With new <what they want> (or when the user describes an automation they want and no saved graph matches), run the CLI's interview. It asks the user things it must not decide for them — when it runs, whether a step goes outside, how many times a repeat may run.

The CLI reads answers from stdin, one per line. So: run it once with no answers to see the first questions, relay them to the user in their own words, get their answers, then run it again with every answer so far:

bash
printf '%s\n' "<answer 1>" "<answer 2>" "y" | "$CLI" graph new "<what they want>"

Rules that matter here:

  • Never invent an answer. If the user has not said when it runs, ask them — do not pick a time. The whole point of the interview is that these come from the person.
  • The interview proposes a grading checklist for steps that repeat until good enough (what must exist / what must not appear). Relay those items so the user can confirm or edit them — they are the pass/fail criteria, and the person should see them before saving.
  • If the user does not know or says you decide, pass that through verbatim (알아서 해주세요 / you decide). The CLI then takes the most conservative option and says what it chose. Do not decide on their behalf yourself.
  • The last line must be y to save. Until then nothing is written.
  • It is created switched off. Say so, and relay the two commands the CLI prints (graph show to look it over, automation on to turn it on).
  • If the CLI stops with "답을 받지 못해 여기서 멈췄습니다: <질문>" / "Stopped here without an answer to: ", it needed one more answer. Relay that exact question to the user and run again with the fuller list. Do not retry with a guess.

List

With no arguments, or with list:

bash
"$CLI" graph list

Report each graph with its trigger kind (schedule or input), step count, and whether it is on. If nothing is saved, say so and point at the desktop app's Graph page — do not invent graphs.

Show

With show <name>:

bash
"$CLI" graph show "<name>"

The output is a tree, not a list — indentation is the wiring. Relay it as wiring, because on a surface with no canvas this is the only way the user can see where a graph branches. These marks must survive into your summary: a step that changes something outside, a step that asks first, a branch's [yes]/[no] sides, a ↩ back to … line (a repeat), a checklist under a verification step (the · [must] / [must not] lines — those items are exactly what the result is graded on), and a code step (a script the AI wrote runs there, not a model prompt). If the graph starts from a value the user provides, the output says so — carry that into the summary too.

Run

With run <name>, the user's direct command is the authority to request that run. Do not ask for a second yes: graph approval is captured when the graph is created, and adding another confirmation here makes an approved automation needlessly stall.

  1. Run "$CLI" graph show "<name>" first and report what the graph does, including any step that changes something outside.
  2. If the graph starts from a value (graph show says so), require that value from the user in their own words. This is missing execution input, not an approval prompt. Do not invent one or reuse an example from the graph.
  3. Request the run immediately once all required input is present:
bash
"$CLI" graph run "<name>" -y

If the graph starts from a value, pass it — without it the CLI refuses, because a graph run with a blank value silently produces something else:

bash
"$CLI" graph run "<name>" -y --input "<the value the user gave>"

Report exactly what the CLI reported: the run was requested, the desktop app picks it up within a minute while open, and a closed app runs it on next open. If the CLI refuses because the automation is switched off, relay that refusal and its reason rather than retrying.

Failure

If the CLI exits non-zero, show its message verbatim and stop. Do not substitute a guess about why, and do not retry a run request.

Frequently asked questions

What does the Hep Graph AI skill do?

Build an Agentlas automation by describing it, list saved ones, or request a run.

Why use Hep Graph on TypingMind?

Because you install it once and use it with any model. Hep Graph is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hep Graph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentlas-ai/Agentlas-OS/tree/main/kimi/skills/hep-graph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hep Graph?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hep Graph?

As many as you like. As long as a model supports skills, you can use Hep Graph with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hep Graph AI skill free?

Yes. It is published on GitHub by agentlas-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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