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Give YOURSELF a loop — write a tick playbook (a strategy) under your own slug, dry-run it, and start it. The shared contract for how a strategy is authored, validated and launched; read by any agent that wants to act autonomously instead of only being consulted.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/hummingbot/condor --skill strategy-builder命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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| name | strategy_builder |
| description | Give YOURSELF a loop — write a tick playbook (a strategy) under your own slug, dry-run it, and start it. The shared contract for how a strategy is authored, validated and launched; read by any agent that wants to act autonomously instead of only being consulted. |
| when_to_use | You are an agent and the user wants you to act on a loop — run every N seconds, watch a condition, report on a schedule, or trade autonomously. Also read this before editing or re-launching a strategy you already own. NOT for creating or deleting other agents (that is Condor's agent_builder). |
| created | 2026-08-11T00:00:00Z |
| source | chat |
You are an agent. You can already be consulted, delegated to, and looped — from the moment you existed. This playbook covers the last of those: giving your loop a specific tick playbook instead of the generic default.
A strategy is a tick system prompt the engine runs in a session, at a frequency
the user sets. It lives at agents/{your_slug}/strategies/{strategy_slug}/strategy.md and
it is yours — you author it, under your own slug.
Scope. This is about giving yourself a loop. Creating, editing or deleting agents belongs to Condor (). If the user wants a whole new specialist, say so and let them ask Condor.
agent_builderstart_agent(strategy_id="<your_slug>") already works with no strategy — it ticks a
default playbook driven by your AGENT.md. That default is deliberately generic. Write a
strategy when the loop needs to be specific and disciplined: a fixed analysis order, a
decision rule, executor schemas, risk limits.
The loop does NOT have to trade. Define the tick task however the user wants:
BEFORE writing the strategy, fetch the schema for every executor type the loop will use —
manage_executors(executor_type="grid_strike"), etc. — and embed the required
fields/types directly into the instructions. The tick LLM has no other way to learn them.
Same for any controller config it manages (manage_controllers).
If the loop should reason over structured data, make sure the routine exists first
(manage_routines(action="list", agent="<your_slug>")). Routines are agent-scoped, so any
strategy you own can call them. Need a new one? Do NOT write it inline — hand it to a
background worker: delegate(action="start", agent="condor", task="build a routine that … for agent <your_slug>").
manage_trading_agent(
action="create_strategy",
agent_slug="<your_slug>", # yourself
name="BRL MM",
description="…",
instructions="<tick system prompt>",
# agent_key omitted → inherits your model; overridable at launch
config={"connector_name": "binance", "frequency_sec": 60,
"total_amount_quote": 100, "execution_mode": "loop"}
)
Returns the composite key "<your_slug>.<strategy_slug>" — that is the strategy_id for
everything after.
The tick instructions MUST include:
…and, only if it trades:
Generic vs specific:
trading_context. Refer to "the configured trading pair"; keep a sensible default_config.manage_trading_agent(action="start_agent", strategy_id="<your_slug.strategy_slug>",
config={"execution_mode": "dry_run",
"trading_context": "Trade BTC-USDT on binance_perpetual",
"frequency_sec": 60, "total_amount_quote": 100,
"risk_limits": {"max_position_size_quote": 200, "max_open_executors": 3}})
Review with trading_agent_journal_read(agent_id=…, section="run:1"): routines called
right, decision logic sound, conditional language ("would place…"), no real create/stop
calls, risk rules respected. Do not go live until the user is satisfied.
A
dry_run/run_onceis an experiment — it writes a snapshot, not a journal session. Read it back the same way; don't expect a numbered run to persist.
Offer run_once (single live tick), loop (continuous), or loop + max_ticks. Confirm
the model, start it, confirm it's running, and give the user the monitoring commands.
Always include risk limits when the loop can trade.
manage_trading_agent(action="list_agent_definitions") — agents and the strategies they own.manage_trading_agent(action="list_agents") — running loop instances.manage_trading_agent(action="agent_status", agent_id=…) — instance status.trading_agent_journal_read(agent_id=…, section="summary"|"runs"|"run:N").Model: the strategy's agent_key defaults to yours; override per launch with
config={"agent_key": "…"}. Never invent one — call get_available_models and pick from
what the operator actually has, or leave it inherited. A pydantic-ai key
(ollama:/openai:/groq:/lmstudio:/openrouter:/custom@…) enforces the tools
allowlist; an ACP key (claude-code/claude-acp/gemini/copilot) runs unrestricted,
with mutations still confirmation-gated.
Server: leave server_name empty unless the user pins it. A strategy you own runs on
whichever server your agent resolves.
Editing & deleting: update_strategy(strategy_id=…, instructions=…) to revise the tick
playbook; delete_strategy(strategy_id=…) to remove it. Stop any running instance first.