القائمة
Turn a one-sentence objective into a downloadable .flw workflow that wires the right Tlamatini visual agents and connections.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Run the daily automated Tlamatini chat regression — drive a visible Chrome via Playwright, log into agent_page.html, ask up to 1000 curated safe questions one-by-one (Multi-Turn ON, ACPX/Ask-Execs/Exec-Report/Internet OFF), wait for and qualify each answer (heuristic + LLM judge on failures), then write a dated report + summary. Invoke when the user says "run the daily chat test", "test Tlamatini with the 1000 questions", "daily Tlamatini regression", or schedules this test.
Turn a natural-language objective into a downloadable .flw workflow by driving the FlowCreator engine (full 83-agent catalog), then emit a canvas-loadable .flw.
The authoritative, exhaustive end-to-end runbook for creating a BRAND-NEW Tlamatini workflow agent — every surface, in order, with 530+ numbered steps across 26 phases. Invoke whenever Angela says "create a new agent", "add an agent", "make a <X>er agent", "I want a new canvas agent", or asks to wire any new pool agent across backend + frontend + Multi-Turn + Parametrizer + FlowCreator + FlowHypervisor + watchdog + config dialog + demo prompts + Python tests + Playwright harness tests + docs + packaging. Covers naming, coloring, the inputs/outputs connector contract in agentic_control_panel.html, the Multi-Turn (wrapped chat-agent) tool, Exec Report, the configuration dialog, automated unit tests AND Playwright tests in Claude's harness. Pairs with tlamatini-agent-naming (casing) and the @-imported create_new_agent.md / create_new_mcp.md.
Build and edit in Roblox Studio via the Roblox Studio MCP the RIGHT way - preflight the Studio connection, do the whole build in a few big execute_luau scripts (not dozens of tiny calls), make REALISTIC terrain with the Terrain VOXEL api driven by Perlin noise (NEVER stacked Parts or concentric layers - those give ugly blocky stepped pyramids), poll generative jobs, check the console, and fail honestly. Invoke for ANY "in Roblox / Roblox Studio" request - terrain, mountains, parts, scripts, models, materials, assets.
The authoritative 8-step contract for scaffolding a NEW Tlamatini workflow agent end-to-end (backend script + config.yaml, connection-update view + URL, migration seeding the Agent row, CSS gradient, four JS files, agentic_skill.md, README.md, lint). READ THIS BEFORE adding or renaming any of the 83 visual agents, before touching `agent/agents/<name>/`, before writing a `00NN_add_<name>.py` migration, before adding a `.canvas-item.<x>-agent` rule, or before extending the `acp-*.js` classMap / connectors. Companion to the `tlamatini-new-acp-agent` skill (which drives the procedure) and to `tlamatini-agent-naming` (the naming-convention guard).
The authoritative Tlamatini agent NAMING CONVENTION — invoke before adding, renaming, casing, displaying, or auditing any workflow agent (STM32er, Node Manager, ACPXer, Kalier, ...). Use whenever you touch agentDescription, a pool/agent directory, a CSS .canvas-item class, a JS connection handler, agents_descriptions.md, or any place an agent name is shown. Prevents mis-casing display names (e.g. STM32er must never become STM32Er/STM32ER/Stm32Er).
| name | tlamatini-flow-from-objective |
| description | Turn a one-sentence objective into a downloadable .flw workflow that wires the right Tlamatini visual agents and connections. |
| metadata | {"openclaw":{"emoji":"🌊"},"tlamatini":{"runtime":"in-process","requires_tools":["execute_command","chat_agent_file_creator"],"requires_mcps":[],"budget":{"max_iterations":12,"max_seconds":180,"max_tokens":30000},"permissions":{"filesystem":{"read":["Tlamatini/agent/agents/**/*"],"write":["Tlamatini/**/*.flw"]},"shell":[],"network":"deny","db":"deny"},"inputs":[{"name":"objective","type":"string","required":true,"description":"One-sentence high-level goal"},{"name":"out_path","type":"string","required":true,"description":"Where to write the .flw file. With no user-given folder, default it under the Tlamatini Templates directory (TLAMATINI_TEMPLATES) — never C:\\Temp / %TEMP%."}],"outputs":[{"name":"flw_path","type":"string","required":true},{"name":"agent_count","type":"integer","required":true},{"name":"connection_count","type":"integer","required":true}],"triggers":{"keywords":["flow from","make a flow","build a flow","scaffold flow",".flw"]}}} |
Produce a canvas-loadable .flw for the user's stated objective.
Superseded by the
flow-makingskill. Preferflow-making: it drives the FlowCreator engine (full 83-agent catalog + connection contracts) and emits a validated, schemaVersion-2.flw. This skill is kept as an alias/entry point — do NOT hand-author the.flwJSON, because you do not carry the agent catalog in context and a hand-written flow hallucinates agent types and will not load.
flow-making skill with the same inputs:
invoke_skill('flow-making', { "objective": "${input.objective}", "out_path": "${input.out_path}" }).{ flw_path, agent_count, connection_count }.Use the shipped driver — it copies the FlowCreator template to an isolated dir,
runs it, and writes the .flw:
python Tlamatini/agent/skills_pkg/flow_making/scripts/make_flow.py \
--objective "${input.objective}" --out "${input.out_path}"
The last stdout line is agent_count=<N> connection_count=<M> flw_path=<path>.
.flw shape (schemaVersion 2)If you ever emit .flw JSON by hand, it MUST match the loader contract
(acp-file-io.js::loadDiagram / flow_spec.py) — NOT a {version, agents, connections:[{from,to,kind}]} shape (that is obsolete and will not load):
{
"schemaVersion": 2,
"nodes": [
{"id": "starter-1", "text": "Starter", "left": "50px", "top": "50px",
"agentPurpose": "", "configData": {"target_agents": ["monitor_log_1"]}}
],
"connections": [
{"sourceIndex": 0, "targetIndex": 1, "inputSlot": 0, "outputSlot": 0}
],
"artifacts": {}
}
See agent/skills_pkg/flow_making/references/flw_schema.md for the full contract.