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Senpi Trading Runtime

Organization
Senpi-ai
senpi-trading-runtime

How a Senpi trading strategy interacts with the runtime engine (@senpi-ai/runtime) on Hyperliquid: a strategy runs from a runtime.yaml pointing at a Python module that exports scan(inputs, ctx), which the runtime supervises and calls each interval, then owns execution, risk guard_rails, and two-phase DSL trailing-stop exits. Use when working with runtime.yaml, the scan(inputs, ctx) contract, external_scanner, ctx, or the DSL exit engine (the Ratchet Stop / tier ladder) or the risk guard rails (a latched daily-loss or drawdown halt) — including verifying open positions are protected by DSL (have a working stop-loss). The shared runtime contract the lifecycle skills reference. NOT for building, installing, or picking a strategy (→ senpi-strategy-author / senpi-strategy-ops / senpi-strategy-discover).

Overview

PublisherSenpi-ai
Repositorysenpi-skills
Skill namesenpi-trading-runtime
Stars
141
Forks
37
Bundled files
13
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.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Senpi Trading Runtime 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/Senpi-ai/senpi-skills/tree/main/senpi-trading-runtime
  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/Senpi-ai/senpi-skills.git /tmp/senpi-skills
mkdir -p .claude/skills
cp -r /tmp/senpi-skills/senpi-trading-runtime .claude/skills/senpi-trading-runtime
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Senpi Trading Runtime 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 Senpi Trading Runtime 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 Senpi Trading Runtime 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.

Senpi Trading Runtime — the runtime contract

This skill is infrastructure: the canonical knowledge of how the Senpi runtime (@senpi-ai/runtime) behaves and how a strategy interacts with it. The lifecycle skills — author (build), ops (install/monitor), discover (recommend) — reference this one for the contract.

The runtime model

A strategy runs from a runtime.yaml that points at an in-repo Python module. The runtime spawns and supervises that module and calls a frozen scan(inputs, ctx) every interval_seconds. The division of labor is fixed:

  • Your code produces signals — nothing else. scan(inputs, ctx) reads market and account data and returns a list[dict] of candidate signals. It does not open, close, size, schedule, or execute anything.
  • Zero model cost, and the only loop there is. The runtime ticks every interval_seconds without a model call. Never put an agent-turn cron beside it — a producer, a re-check, a watcher — each firing is a full model call. There is no paper-trading mode: a strategy is tested with senpi validate (one real tick, no wallet) and then run live at the $10 floor.
  • The runtime owns everything downstream: scheduling (interval_seconds), spawning + supervising + restarting the scanner, validating (signal_data_schema) + de-duplicating the signals you return, sizing & order execution (FEE_OPTIMIZED_LIMIT), slot accounting, risk.guard_rails, the two-phase DSL trailing-stop exits, and crash-safe position reconcile on restart.
  • What it does NOT own: partial exits. Every exit the runtime can make (DSL, CLOSE_POSITION) closes the WHOLE position, and edit_position — the only reduce — is blocked to scanners. A rule like "take 25% off at +10% ROE and let the rest run" cannot be authored in a runtime package at all; a scanner that tries raises PermissionError every tick while the log reads as if the rule is working. Details: references/runtime-concepts.md → DSL Exit Engine.

How your code talks to the runtime

The interaction surface is small and one-directional — you read, you return signals, the runtime acts.

  • runtime.yaml declares the scanner(s), the action gate, the exit engine, and the risk guard-rails, and passes author tunables down via inputs:. → references/runtime-yaml.md
  • scan(inputs, ctx) is the single entry point. inputs is the runtime's inputs: map; ctx gives you:
    • ctx.senpi_mcp.call_tool(name, args) — the Senpi MCP client, read-only (market, account, leaderboard, discovery, strategy_get*, …). It is the only way to fetch data.
    • ctx.state — transactional, runtime-persisted history (last() / append() / len) for dedup, rotation, and first-seen ledgers; advances only on a clean tick.
    • ctx.wallet — the strategy's wallet address.
    • → references/scan-contract.md
  • The return value is a list[dict], one per candidate signal (asset, direction, marginPct, leverage, data{}). The runtime validates each data{} against the runtime.yaml's signal_data_schema, then sizes, executes, and manages exits.

Keep the thesis logic in a sibling pure scoring.py (no I/O, no MCP) so it is unit-testable; scan.py does the reads + state, scoring.py does the math.

Runtime commands (essentials)

The plugin registers a senpi command group on the gateway. Deploying a strategy and checking it:

bash
openclaw plugins install @senpi-ai/runtime
openclaw senpi validate <dir-with-runtime.yaml>        # THE GATE, pre-money: one real tick, no wallet; a PASS records the proof deploy requires
openclaw senpi deploy -p <package-dir> --budget <usd>  # ONE verb, detached: funds preflight → wallet create+fund → install → one observed tick
openclaw senpi deploy status                           # poll until terminal; the verified report (read-only)
openclaw senpi runtime list                            # id, source, status ("running — NO ENTRY SCANNERS" = scanners never wired)

Deploy a package through senpi-strategy-ops' deploy.py create <id> --budget <usd>, which resolves the package, runs the structural preflight and drives that same verb. The verb owns the gates — the live-universe check ([E_UNIVERSE_NOT_LIVE], pre-money), the funds preflight, the skillName/skillVersion attribution and the verified tick. runtime create is internal: it installs a runtime and skips every one of those, so it is not the deploy path.

senpi validate is the gate every deploy runs through — it loads the scanners and runs one real tick with no wallet and no funding, and a full unscoped PASS at live depth is what writes the .senpi-proof.json senpi deploy refuses to fund a package without. Run it before deploy, once per instance, pointed at the directory holding that instance's runtime.yaml.

To CHANGE a strategy that is already live → openclaw senpi update, not a delete-and-redeploy. Deleting a runtime abandons its DSL position files, so every open position loses the trailing stop the strategy already promised and its replacement re-adopts from scratch at whatever price it finds. update swaps the components in place and keeps DSL state, scanner stores and action history — no wallet is created, nothing is funded, and no position is closed.

bash
openclaw senpi update ./pkg                       # PLAN — what changes, what it leaves alone
openclaw senpi update ./pkg --apply               # commit (needs the same proof deploy needs)
openclaw senpi update ./pkg --apply --code-only   # assert only scan.py changed; refuses if not

It plans by default; --apply is the only way to commit. phase2.tiers is what users call the ratchet; with no open position, the edit plus --apply is the whole change. dsl_preset changes are forward-only — new entries only, while every open position keeps a snapshot of the preset it was opened under (other exit: fields, e.g. order_type, are read live and DO reach open positions). Refused outright, changing nothing: a different strategy.wallet (that is a new deployment, and the old wallet's positions would be left unmanaged), an external scanner renamed or moved (state is keyed by name and position together, and inserting or removing one moves every external scanner below it — appending at the end moves nobody and is allowed), or a changed action_type under a stable name. --apply also needs a passing proof that covers the recipe being applied, so point it at the package DIRECTORY: a recipe handed over as bare text has nothing to verify against and is refused. Exit 2 means the runtime was never touched; exit 1 means an apply was attempted and it may not be where you left it — read the message before retrying.

Saved is not applied. Without --apply, update changes nothing, and only the first line of its output says so — Dry run for <runtime_id> — nothing has been applied. — so never pipe openclaw senpi output through tail or head. A scanner imports scan.py once, when it starts, so a running strategy keeps its old code until --apply restarts its scanners (a crash or a gateway restart would load the edit too, unannounced). senpi validate runs the copy on disk, never the running one, and re-running deploy on a strategy that is already running applies nothing. The edit is live once the apply exits 0 with Updated <runtime_id>. as its first line and no WARNING that its external scanners are NOT running, and the running strategy shows it:

bash
openclaw senpi events -r <runtime_id> --name runtime.updated   # an entry for this apply (code-only edits too)
openclaw senpi update ./pkg --id <runtime_id>                  # read-only: a recipe edit that landed plans "The recipe is unchanged."

Until both reads show it, tell the user the edit is saved, not applied. What a tick keeps and what discards one: references/scan-contract.md.

Beyond validate, deploy/deploy status, update and runtime list/delete, the CLI exposes the runtime's live state — senpi dsl positions|inspect|closes (the exit engine), senpi action list|inspect|history|decisions (the decision layer), senpi risk (am I allowed to trade, and why not — the daily loss and drawdown halts latch: they stay CLOSED after PnL recovers and survive restarts; a latched halt is the user's call, never clear it or resume entries without their explicit approval → senpi-strategy-author/references/risk-gates.md), senpi audit (backend trade trail with AI reasoning), senpi scanner (per-scanner health, liveness, and a (no signals yet) flag for scanners that run but produce nothing), senpi events/senpi explain <asset> (the local domain-event log — the trade narrative, and one asset's stitched lifecycle), senpi status/senpi state (health — fail-closed: an external scanner never proven by a tick reads unknown, not healthy; non-healthy scanners get their own line with restart count and cause), and senpi guide … (in-shell reference). Full surface with every option → references/runtime-cli.md.

To confirm open positions are actually stop-loss protected (a position with no DSL shows up as an absence in dsl positions, so it's easy to miss) → the verdict procedure in references/dsl-protection-check.md.

The reference set

Read thisFor
references/runtime-concepts.mdHow the runtime behaves end to end: the runtime pipeline, position_tracker, and the two-phase DSL exit engine
references/runtime-yaml.mdThe runtime.yaml schema — every section, the external_scanner fields, the risk guard-rails
references/scan-contract.mdThe author contract in depth: scan(inputs, ctx), the ctx surface, the signal shape, and scoring.py
references/runtime-cli.mdThe full openclaw senpi … command surface — validate (the pre-deploy gate: flags, depths, exit codes, the proof it records), deploy, runtime, dsl, action, status/state, skills, guide
references/dsl-protection-check.mdVerify open positions are DSL-protected — the PROTECTED / UNPROTECTED / STOP-NOT-ON-VENUE verdict + the open-vs-tracked reconciliation

Package naming (load-bearing)

The runtime package is @senpi-ai/runtime (with -ai) — the one users install on their hosts. Always write it with the -ai.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Senpi Trading Runtime AI skill do?

How a Senpi trading strategy interacts with the runtime engine (@senpi-ai/runtime) on Hyperliquid: a strategy runs from a runtime.yaml pointing at a Python module that exports scan(inputs, ctx), which the runtime supervises and calls each interval, then owns execution, risk guard_rails, and two-phase DSL trailing-stop exits. Use when working with runtime.yaml, the scan(inputs, ctx) contract, external_scanner, ctx, or the DSL exit engine (the Ratchet Stop / tier ladder) or the risk guard rails (a latched daily-loss or drawdown halt) — including verifying open positions are protected by DSL (...

Why use Senpi Trading Runtime on TypingMind?

Because you install it once and use it with any model. Senpi Trading Runtime 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 Senpi Trading Runtime in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Senpi-ai/senpi-skills/tree/main/senpi-trading-runtime. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Senpi Trading Runtime?

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 Senpi Trading Runtime?

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

Is the Senpi Trading Runtime AI skill free?

Yes. It is published on GitHub by Senpi-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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