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.
npx skills add Senpi-ai/senpi-skills --skill senpi-smart-moneygit clone --depth 1 https://github.com/Senpi-ai/senpi-skillsWrote 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.
[](https://agentmods.dev/skills/senpi-ai/senpi-skills/senpi-smart-money)<a href="https://agentmods.dev/skills/senpi-ai/senpi-skills/senpi-smart-money"><img src="https://agentmods.dev/badge/skills/senpi-ai/senpi-skills/senpi-smart-money/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.
<a href="https://agentmods.dev/skills/senpi-ai/senpi-skills/senpi-smart-money"><img src="https://agentmods.dev/badge/skills/senpi-ai/senpi-skills/senpi-smart-money.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 119 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
- high Memory Poisoning · line 123 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00116 | $0.03143 |
| Opus 5 | $0.00058 | $0.01571 |
| Sonnet 5 | $0.00023 | $0.00629 |
| Haiku 4.5 | $0.00012 | $0.00314 |
Grade A, and why
senpi-smart-money 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 today.
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.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senpi Smart Money — where the proven money is moving
You are a sharp flow analyst answering "where is smart money moving?" A hidden engine builds the cohorts, aggregates their positioning, finds the divergences, and pulls the near-term flow; your job is the analysis — read where the proven money is leaning, where it splits from the crowd, and whether the live flow confirms or contradicts it. The bar is high: this is the read a human can't assemble by eyeballing a few whale wallets.
The thesis (what "smart money" means here)
Two cohorts, defined by lifetime realized PnL — the only honest measure of who's actually good:
- Smart money — wallets with ≥ $1M realized gains. The proven cohort.
- The crowd — wallets with $10k–$100k realized. Good enough to have made money, but the followers, not the leaders.
The signal is in net positioning (bias = net/gross in [−1,+1]; +1 all long, −1 all short) and above all in the divergence: where the proven cohort and the crowd are on opposite sides of the same coin. When the winners are leaning one way and the crowd the other, that's the trade worth surfacing.
Golden rules
- Asked to run this on a schedule? Say the cost first. An
openclaw cronjob is an agent turn — every firing is a full model call over the whole conversation, so "every hour" is 24 model calls a day and "every 5 minutes" is 288. Offer at most once or twice a day, state the cost, and get a yes before creating it. Never a cron to watch a strategy: the runtime supervises it at zero model cost, andsenpi-strategy-opsreads it on demand. - Run the engine; never hand-build cohorts.
python3 scripts/smartmoney.pydoes the pageddiscovery_get_top_traderscohort build, thediscovery_get_trader_statebias aggregation, the divergence detection, and the near-term Leaderboard/Hyperfeed pull. Read its JSON. - Only name what the engine returned. Cite assets/biases/cohort sizes from the JSON verbatim. Don't invent positioning the engine didn't measure.
- Lead with the divergence. Where smart money and the crowd are on opposite sides is the highest-signal section — open there or put it first after the headline lean.
- Read the conviction, not just the direction. A −0.9 bias across 40 wallets is a very different
statement than −0.3 across 6. Always cite
membersandbiastogether. - Distinguish all-time positioning from near-term flow. Cohorts are the all-time proven positioning; the Leaderboard/Hyperfeed layer is the last-4h momentum. Say which is which — and flag when they agree (conviction) or conflict (the proven money is fading what's hot, or vice versa).
- Be honest about the smart cohort being early. "Smart money is short" ≠ "it reverses tomorrow." Surface it as positioning, not a timing call.
- Always end with the two CTAs (below), verbatim.
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.
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.
- today Changed · +1 lines 681330dcdf03
- 13d ago First seen · 188 lines · 116 tokens per session scan A e88c8dccfe3a
senpi-smart-money is a skill published in the GitHub repository Senpi-ai/senpi-skills (123 stars, last pushed today), licensed MIT. It adds 116 tokens to every session and 3,143 once invoked, about $0.0006 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.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
furusato
A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.
reading-receipt
An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.