hep-graph

hep-graph is a skill for Claude Code from agentlas-ai/Agentlas-OS. It costs 22 tokens per session (1,324 once invoked), scanned A, original, Apache-2.0.

An automation-graph skill for describing new Agentlas workflows, listing saved workflows, and requesting a run. A graph is a saved sequence of connected tasks; the desktop app performs the run.

In plain words
What is it for?
Use it to create workflows through an interview, inspect saved graphs, or ask the desktop app to run one.
Why use it?
It separates designing or requesting an automation from actually executing it, so the result is not reported as finished prematurely.

Skill for Claude Code

Written for Claude Code: $ARGUMENTS substitution.

Good fit Use it to create workflows through an interview, inspect saved graphs, or ask the desktop app to run one.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agentlas-ai/agentlas-os/hep-graph
About the project

Agentlas OS is a local-first system for creating, storing, borrowing, and running specialist AI agents and temporary agent teams through supported hosts and models. It serves people who want reusable agents that remain available across computers and model workspaces, and the catalogue contains its skills, commands, hooks, agents, instructions, plugin, and rule.

agentlas-ai/Agentlas-OS · 1,588 stars · on GitHub · agentlas.cloud

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add agentlas-ai/Agentlas-OS --skill hep-graph
Clone the repo
git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OS

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for hep-graph

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/hep-graph/github.svg)](https://agentmods.dev/skills/agentlas-ai/agentlas-os/hep-graph)
Your own site
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-os/hep-graph"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/hep-graph/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for hep-graph

Your own site · 80×15
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-os/hep-graph"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/hep-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,324 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00022 $0.01324
Opus 5.5 $0.00009 $0.00530
Sonnet 5.5 $0.00004 $0.00265
Haiku 4.5 $0.00002 $0.00132

Measured 7d ago against content hash d1dad7badbf1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

hep-graph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

kimi/skills/hep-graph/SKILL.md · 129 lines

How it starts

The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

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

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:

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.

Read the full file on GitHub · 129 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago Changed d1dad7badbf1
  2. 1mo ago Changed · +6 lines 33ea17972a9d
  3. 1mo ago First seen · 123 lines · 22 tokens per session scan A c3c8786c50df

Subscribe to this mod's changes

hep-graph is a skill published in the GitHub repository agentlas-ai/Agentlas-OS (1,588 stars, last pushed 3d ago), licensed Apache-2.0. It adds 22 tokens to every session and 1,324 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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