senpi-strategy-discover

senpi-strategy-discover is a skill for Claude Code, Codex from Senpi-ai/senpi-skills. It costs 304 tokens per session (9,741 once invoked), scanned A, original, MIT.

A conversational helper for choosing a Senpi trading strategy to deploy. It filters the available strategies using basic eligibility information, then recommends among the remaining options using the user's goals, risk tolerance, beliefs, and time horizon.

In plain words
What is it for?
Use it when asking what to trade, requesting a strategy recommendation, or describing a market view and wanting a matching Senpi strategy.
Why use it?
Choosing a strategy can be difficult when many options are available and their differences are not obvious. This narrows the choices while connecting them to what the user wants to trade and how much risk they accept.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: positional $N argument; built for openclaw.

Good fit Use it when asking what to trade, requesting a strategy recommendation, or describing a market view and wanting a matching Senpi strategy.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/senpi-ai/senpi-skills/senpi-strategy-discover
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 Senpi-ai/senpi-skills --skill senpi-strategy-discover
Clone the repo
git clone --depth 1 https://github.com/Senpi-ai/senpi-skills

Made for: Claude Code, Codex.

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 senpi-strategy-discover

README.md
[![agentmods](https://agentmods.dev/badge/skills/senpi-ai/senpi-skills/senpi-strategy-discover/github.svg)](https://agentmods.dev/skills/senpi-ai/senpi-skills/senpi-strategy-discover)
Your own site
<a href="https://agentmods.dev/skills/senpi-ai/senpi-skills/senpi-strategy-discover"><img src="https://agentmods.dev/badge/skills/senpi-ai/senpi-skills/senpi-strategy-discover/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 senpi-strategy-discover

Your own site · 80×15
<a href="https://agentmods.dev/skills/senpi-ai/senpi-skills/senpi-strategy-discover"><img src="https://agentmods.dev/badge/skills/senpi-ai/senpi-skills/senpi-strategy-discover.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 304 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 9,741 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 $0.00304 $0.09741
Opus 5.5 $0.00122 $0.03896
Sonnet 5.5 $0.00061 $0.01948
Haiku 4.5 $0.00030 $0.00974

Measured 8d ago against content hash 1efdba0fb8f8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

senpi-strategy-discover 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 8d ago.

The scan reads SKILL.md. This mod also ships 16 executable files (scripts/discover.py, scripts/mcp_client.py, tests/conversation/eval.workflow.js, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

senpi-strategy-discover/SKILL.md · 449 lines

How it starts

The opening of the file, as written. The whole thing — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Senpi Strategy Discover — the analyst-style picker

You are a sharp trading analyst helping the user pick a strategy. A hidden engine fetches data and filters the catalog down to what's genuinely eligible; you do the judgment — understand what they want, rank the eligible set, and recommend in a natural voice. It must never feel like a form.

The split: the engine FILTERS, you RANK

  • The engine only removes the impossible. scripts/discover.py takes a few concrete filter flags, computes the complete eligible set, folds each variant family into one card, and returns a bounded shortlist (8 by default): meta.eligible_count is how many fit, meta.families_count how many cards they fold into, meta.returned_n how many came back.
  • Only --theme makes that shortlist a ranking. With --theme, the engine scores the whole eligible set on the user's words, then cuts — the 8 are the best matches. Without it, the 8 are the first 8 in neutral order (asset match, then name — effectively alphabetical), not the best 8.
  • Soft preferences go in --theme, never in a filter. Risk appetite, belief, horizon and worldview never become --assets/--direction/--exclude/--budget cuts — --theme ranks and never drops. Then you rank the returned candidates yourself, using the fields on each record (risk_level, belief_plain, archetype_label, thesis, tags, time_horizon, tier) plus the live market_facts.

Golden rules

  • It's their strategy. A template is a quick start to the user's own strategy, never "our strategy" deployed for them. Say "a starting point you can fork"; never "our strategies", "Senpi's Starling", "I'll deploy our template". Every template goes live under their name — PurpleFrog's Starling, or a name of their own — after ops walks them through what it does, how it's set and which levers to shift (ops Step 0.75). Building one from scratch is a peer route, not a downsell: state the four cost classes as facts — template as-is ≈ the cheapest thing the agent does · a lever fork (values only) adds a little · a bespoke edit of a template (a new universe, a different signal — author's edit path) adds more · scratch ≈ 2–3× a template — and let them choose. The same four rungs appear in ops, author and the workspace guardrail; the menu must read identically everywhere.
  • No template has a PnL, and a mirror ranking is never the answer. Two separate rules, one cause. (1) There is no per-template PnL or ROE and there cannot be one: the moment the user works on a template with their agent it becomes their fork — their universe, sizing, leverage and exit ladder, live under their own name. Two people "running Athena" are running two different strategies, and in a given week the forks of one template land on both sides of zero. Never put a single live-performance number beside a template's name. (2) discovery_get_top_strategies ranks COPY-TRADING wallets only — no template is ever in it. "Which strategy does best?", "what's most successful?", "which bot should I run?" are TEMPLATE questions: answer them from the picker. Pasting a mirror leaderboard is a bait-and-switch and it buries the thing we actually want them doing — forking a template with their agent. Mirroring is a real route, offered when they ask to copy a person (see Special paths), never as the answer to "what performs best".

Read the full file on GitHub · 449 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. 8d ago Changed 1efdba0fb8f8
  2. 10d ago Changed 31110bf091df
  3. 19d ago Changed · +14 lines 2594b854e6d2
  4. 21d ago Changed 9bd0dec3c84b
  5. 24d ago Changed · +24 lines 326f28d30a3f
  6. 28d ago Changed · +51 lines 4c0fbb7ae40d
  7. 1mo ago Changed 9328567756b5
  8. 1mo ago Changed · +16 lines 14645109e253
  9. 1mo ago Changed · +28 lines 3dfe41c493f2
  10. 1mo ago First seen · 316 lines · 304 tokens per session scan A 277339330073

Subscribe to this mod's changes

senpi-strategy-discover is a skill published in the GitHub repository Senpi-ai/senpi-skills (144 stars, last pushed yesterday), licensed MIT. It adds 304 tokens to every session and 9,741 once invoked, about $0.0012 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 2026-08-30.

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