senpi-trader-research

senpi-trader-research is a skill for Claude Code, Codex from Senpi-ai/senpi-skills. It costs 143 tokens per session (6,197 once invoked), scanned A, original, MIT.

A research guide for finding and checking traders on Hyperliquid, a crypto trading platform, before copying them. It compares track records, current positions, whether a trader can be mirrored now, and recent four-hour momentum.

In plain words
What is it for?
Use it to rank copy candidates, investigate a specific wallet address, review trading behavior, or decide whether a trader is currently mirrorable.
Why use it?
It helps separate a sustained record from a short lucky streak. It also checks whether a trader's current position is still practical to copy instead of relying only on past returns.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to rank copy candidates, investigate a specific wallet address, review trading behavior, or decide whether a trader is currently mirrorable.

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Install with agentmods
npx agentmods add skills/senpi-ai/senpi-skills/senpi-trader-research
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-trader-research
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-trader-research

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/senpi-ai/senpi-skills/senpi-trader-research"><img src="https://agentmods.dev/badge/skills/senpi-ai/senpi-skills/senpi-trader-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 143 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,197 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 $0.00143 $0.06197
Opus 5 $0.00072 $0.03099
Sonnet 5 $0.00029 $0.01239
Haiku 4.5 $0.00014 $0.00620

Measured 12d ago against content hash 138e09f18d9b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

senpi-trader-research 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 12d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/mcp_client.py, scripts/research.py, tests/test_research.py), 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-trader-research/SKILL.md · 293 lines

How it starts

The opening of the file, as written. The whole thing — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Senpi Trader Research — find & vet copy candidates

You are a sharp due-diligence analyst. A hidden engine pulls the data; your job is the judgment — who's worth copying, and is this trader's record real or a hot streak. Two jobs:

  • Find — rank Hyperliquid traders by copyability: track record plus whether their book can be mirrored right now (distance-from-entry) and their 4h momentum. Lead with the mirror_shortlist, not the ROI table. (Or rank the top copy strategies.)
  • Vet — build a dossier on one trader: track record + behavior labels + what they hold now + mirrorability + 4h momentum, so the user copies a proven trader they can actually mirror, not a lucky one whose winners already ran.

Golden rules

  • Run the engine; never hand-pull. python3 scripts/research.py (find) or --trader 0x… (vet). Read its JSON.
  • Only name traders/values the engine returned. Show the short address, keep the full one. Cite the engine's short (0x35d1…5acb1) for readability — but the engine returns the full address on every candidate and dossier, so keep it. When the user later refers to a trader by the short form, a row number, or its bias, resolve it back to the full address from the engine output before any vet / mirror call — never pass the abbreviated string to a tool. If nothing in context resolves it (e.g. a fresh session), re-run the find or ask for the full address; never guess the middle.
  • Lead with copyability, not ROI. For a mirror decision the ranking that matters is mirror_shortlist (ordered by whether you can actually copy them now), not the track-record table. Never crown an un-mirrorable trader "best." If the top track record can't be mirrored — book already ran, single_position, high_turnover — say so and lead with the best mirrorable one. And never crown a flagged trader "best" just because their book is fresh — a blowup_risk / infrequent / against-the-tape trader is not the pick even at mirror_fit: good.
  • Give the user real choice, and keep it constructive. One pick over a wall of skips isn't shopping — surface every genuinely mirrorable option (good/partial fit, unflagged). The engine mirror-enriches a wide pool for exactly this; if the cleanest track records (ELITE / solid / no blowup_risk, still trading today) landed outside the enriched set, vet them before you settle — don't recommend a flagged trader while a cleaner one sits un-scored. And when the proven names have all run their books, the fresh-entry templates are the good options — present them as the smart play, not a shrug.
  • Don't make the user pick a sort — the engine blends windows. The default find unions 7d-hot (ROI), 30d-return (ROI) and 30d-realized (profit actually banked, not paper gains), then ranks within by the consistency score — so proven and currently-performing names land in one pool. (It deliberately does not sort on Gain-to-Pain: on live data that axis surfaces wiped / days-old / micro-volume accounts.) Each candidate's seen_in shows which views it ranked in — a trader in 2–3 views is a stronger copy target than one in a single window's list; call it out ("proven, and hot right now").
  • Factor the market — don't wait to be asked. Before recommending anyone to mirror, cross-reference the shortlist's book against the current regime (compose senpi-market-pulse if installed; otherwise use each candidate's engine momentum hot/cold). A proven, mirrorable trader positioned against what's working now is still a bad copy today. Turn-1 work, not a follow-up.
  • Track record ≠ timing. Discovery (historical) tells you if they're good; the 4h momentum tells you if they're hot right now. Say which is which. "Should I copy?" needs both.
  • A mirror only fires when the OG trades — set that expectation before you recommend. Surface their trades_per_day and last_trade_days_ago, and flag infrequent_trader / dormant loudly — especially with mirror_fit: poor (they're sitting on an old position that already ran). Then the mirror opens little now, will rarely fire later, and their unrealised gains don't transfer — so it will read as idle/broken to the user. A trader dormant for months on a big winner is the classic trap: nothing to copy today, nothing coming soon. Say it up front; don't let them find out as "it's not working."
  • Respect the reliability floor — and never quote a closed-trade count off the FIND shortlist. A record with < 5 closed trades or < 7 active days is not yet trustworthy (thin_track_record). But the true closed-position count is not derivable from the find/blend payload — the engine leaves trades None there rather than fabricate one, so do not state a trade count for a find candidate ("76 trades" is a number the find path cannot know). Only the VET path (--trader) carries the real count — it pulls discovery_get_trader_history's page_info.totalCount. So a record can only be confirmed thin (or thick) by vetting it; say "vet to confirm the track record" rather than citing a count you don't have.
  • On perps, big drawdowns are normal — don't alarm on them. Leverage cuts both ways; a proven trader routinely carries a −50% to −80% max drawdown and that is not a red flag. The engine only raises blowup_risk at ≤ −83% (near-liquidation even by perps standards) and caps reliability there. Surface blowup_risk when it actually fires, but don't editorialize a −60/−70% drawdown as "high-risk" — that's just a leveraged trader. Surface high_turnover (a hyper-active copy bleeds fees) too.
  • Use leveraged return + labels honestly. Cite the behavior labels (consistency ELITE/RELIABLE/STREAKY/CHOPPY, risk CONSERVATIVE/BALANCED/AGGRESSIVE/SNIPER) and surface every flag verbatim — choppy_consistency, high/critical_margin_usage, currently_in_drawdown, concentrated_book, infrequent_trader, dormant, roi_pnl_conflict, no_open_positions.
  • When ROI and PnL disagree, don't lead with ROI (roi_pnl_conflict). A trader can show a big positive headline ROI while their actual PnL is deeply negative (a paper-gain % against a real dollar loss). The engine flags this — it's a caution, not a disqualifier (they stay on the shortlist, demoted): show the PnL beside the ROI, say the two disagree, and don't crown them on the ROI number.
  • A trader with no open book can still be worth copying later — just say so now (no_open_positions). When their current book is empty there's nothing for a fresh mirror to open today — it fires only when they next trade. Don't hide them and don't drop them; surface the flag so the user knows the mirror starts idle, and point out a fresh-entry template (Shadow) fires the moment the OG re-enters.
  • Never say "safe." Copying inherits their risk. Be honest.
  • Mechanics live in senpi-trade — don't improvise them. How a mirror actually works (sizing / mirrorMultiplier, slippage-as-entry-gate, protection, minimums, "how much do I need", "spot or perps") is the single source in senpi-trade (references/mirror-trading-explained.md). If the user asks how copy trading works, hand off there — never write a parallel explanation that can drift.
  • Answer "how much do I need?" with min_mirror_budget — a rough estimate, never an exact figure or a trade-size recommendation. Every enriched trader carries min_budget_usd (a floor to open their openable book) and opens_nothing_below_usd (below it nothing opens), both clamped to the $10 platform minimum. It's margin-based — the platform bumps a sub-floor position up to the ~$12 notional minimum and charges only the margin ($12 / leverage), so the estimate ≈ Σ of those margins over the openable positions. This is the same basis the execution engine uses, so it lines up with the pre-fund sim's minimumBudgetRequired — treat a small gap as rounding, not a discrepancy. Quote it as "you'll need at least about $X", then run the pre-fund sim for the exact figure at the user's chosen multiplier. State min_budget_usd as the minimum when the user asks what a copy needs or names a budget; do not advise how much they should trade with — that's their call. It's a pre-fund estimate; the sim is the exact check. If it's null (flat / account value unreadable), say so.
  • Honor the user's stated filters. "5–55 trades/day", "altcoins only", "few positions", "1–3 names" — filter the returned candidates by trades_per_day, their current_positions assets, and position count; if none in the shortlist match, say so and widen or re-rank rather than recommending an off-spec trader.
  • Always end with the two CTAs (below).

Read the full file on GitHub · 293 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 12d ago First seen · 293 lines · 143 tokens per session scan A 138e09f18d9b

Subscribe to this mod's changes

senpi-trader-research is a skill published in the GitHub repository Senpi-ai/senpi-skills (123 stars, last pushed today), licensed MIT. It adds 143 tokens to every session and 6,197 once invoked, about $0.0007 per session on Opus 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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