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 nklofy/code-agent-skills --skill prediction-market-risk-reviewgit clone --depth 1 https://github.com/nklofy/code-agent-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/nklofy/code-agent-skills/prediction-market-risk-review)<a href="https://agentmods.dev/skills/nklofy/code-agent-skills/prediction-market-risk-review"><img src="https://agentmods.dev/badge/skills/nklofy/code-agent-skills/prediction-market-risk-review/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/nklofy/code-agent-skills/prediction-market-risk-review"><img src="https://agentmods.dev/badge/skills/nklofy/code-agent-skills/prediction-market-risk-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00050 | $0.00385 |
| Opus 5 | $0.00025 | $0.00192 |
| Sonnet 5 | $0.00010 | $0.00077 |
| Haiku 4.5 | $0.00005 | $0.00038 |
Grade A, and why
prediction-market-risk-review 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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- prediction-market-risk-review — 100% identical, 0 lines differ
What it actually says
Prediction Market Risk Review
Use this skill before a prediction-market workflow touches user financial context, venue authentication, portfolio data, automation, or execution-capable tools.
Review Gates
Advice Boundary
- Confirm the output is informational.
- Remove buy/sell/hold/size recommendations.
- Keep manual user decision points explicit.
Venue And Regulatory Boundary
- Identify venue terms, geography restrictions, account limits, and API rules.
- Flag betting, derivatives, securities, or commodities ambiguity for legal review when relevant.
- Do not bypass venue restrictions or rate limits.
Data Quality
- Check market liquidity, spread, resolution rules, stale prices, and source timestamps.
- Separate public venue data from Itô gated data.
- Do not mix public and private sources without labels.
Security
- Do not request or store private keys, seed phrases, or passwords.
- Keep
ITO_API_KEYand venue API keys out of logs and docs. - Use read-only scopes by default.
- Require circuit breakers, spend limits, dry runs, and human approval before any private implementation adds execution.
Privacy
- Minimize user portfolio, financial, and knowledge-base data.
- Redact private sources in public artifacts.
- Preserve only the fields needed for the review.
Output Contract
Return:
- scope reviewed
- pass/warn/fail findings
- blocked actions
- required mitigations
- safe next step
If any execution-capable step is requested, require a separate implementation plan and explicit user approval.
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.
- 8d ago First seen · 62 lines · 50 tokens per session scan A 19195e218716
prediction-market-risk-review is a skill published in the GitHub repository nklofy/code-agent-skills (18 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 50 tokens to every session and 385 once invoked, about $0.0003 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 2026-09-03.
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.