跳到正文
cccskills
免费在 GitHub 公开

hep-graph

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

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(1)

  • SKILL.md5.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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: <question>", 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:

"$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>:

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

"$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.

Raw arguments: $ARGUMENTS

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Use when designing a new multi-agent team, visible agents folder, role boundaries, handoff flow, PM Soul, Memory Curator, Policy Gate, or evaluation role. Use for agent-team repo creation even when the user only says they want a meta-agent or agent operating system.

中文简介正在准备中。当前显示原文说明。

agentlas-ai/Agentlas-OS16062026年10月17日 更新

agentlas

免费

Single entry point for every Agentlas command: /agentlas network, /agentlas build, /agentlas storm, /agentlas graph, /agentlas one, /agentlas upload, /agentlas cloud, /agentlas hub, /agentlas local, /agentlas browser, /agentlas call, /agentlas search, /agentlas connect, /agentlas login, /agentlas orch, /agentlas update.

中文简介正在准备中。当前显示原文说明。

agentlas-ai/Agentlas-OS16062026年10月17日 更新

agentlas

免费

Single entry point for every Agentlas command: /agentlas network, /agentlas build, /agentlas storm, /agentlas graph, /agentlas one, /agentlas upload, /agentlas cloud, /agentlas hub, /agentlas local, /agentlas browser, /agentlas call, /agentlas search, /agentlas connect.

中文简介正在准备中。当前显示原文说明。

agentlas-ai/Agentlas-OS16062026年10月17日 更新

Use when adding or auditing local runtime behavior that turns a project folder into an Agentlas-aware workspace with .agentlas memory and sitemap files.

中文简介正在准备中。当前显示原文说明。

agentlas-ai/Agentlas-OS16062026年10月17日 更新

Use the Agentlas browser hardpoint for browser-required work.

中文简介正在准备中。当前显示原文说明。

agentlas-ai/Agentlas-OS16062026年10月17日 更新

Use when the user types /agentlas-browser <url-or-query>, /agentlas browser, or /hep-browser for browser-required tasks, rendering pages, form flows, or visual evidence.

中文简介正在准备中。当前显示原文说明。

agentlas-ai/Agentlas-OS16062026年10月17日 更新

查看 agentlas-ai 的全部技能

报告此技能的问题