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 AnyGenIO/anygen-selected-skills --skill predict-intelligencegit clone --depth 1 https://github.com/AnyGenIO/anygen-selected-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/anygenio/anygen-selected-skills/predict-intelligence)<a href="https://agentmods.dev/skills/anygenio/anygen-selected-skills/predict-intelligence"><img src="https://agentmods.dev/badge/skills/anygenio/anygen-selected-skills/predict-intelligence/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/anygenio/anygen-selected-skills/predict-intelligence"><img src="https://agentmods.dev/badge/skills/anygenio/anygen-selected-skills/predict-intelligence.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 | $0.00074 | $0.04162 |
| Opus 5 | $0.00037 | $0.02081 |
| Sonnet 5 | $0.00015 | $0.00832 |
| Haiku 4.5 | $0.00007 | $0.00416 |
Grade A, and why
predict-intelligence 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 406 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Predict Intelligence Skill
You generate a professional PDF intelligence brief. The user should grasp the key finding in 30 seconds. You own information efficiency.
Agent Requirements
| Capability | What you need |
|---|---|
| Web search | Search the internet for news, analysis, data |
| URL fetch | Open a URL and read its content |
| File read/write | Read template, write HTML report |
| Shell execute | Run Python 3.9+ scripts |
First-Time Setup
pip install playwright
playwright install chromium
No other Python packages needed (Jinja2, requests, etc. are NOT required).
How It Works
Read template → Do research → Write HTML → Convert to PDF
- Read
SKILL_DIR/templates/report_template.html— your structural reference. - Research and analyze the topic (Steps 1–8 below).
- Generate a NEW
.htmlfile following the template's exact structure. - Convert to PDF:
python SKILL_DIR/scripts/to_pdf.py report.html predict_report.pdf
The template IS the spec. It contains:
- All CSS (copy verbatim — never modify)
- All D3 visualization code (copy verbatim — only change data variables)
- Example content showing exact formatting for every section
- Extensive comments explaining what each section does and how to fill it
Step 0 — Domain Detection
| Signal | Domain |
|---|---|
| Countries, leaders, military, diplomacy, sanctions | Geopolitical |
| Stocks, crypto, Fed, rates, commodities | Financial |
| Tech releases, AI models, products | Technology |
| M&A, acquisitions, IPO, corporate | Corporate |
| Other | Custom |
Classification bar is always: ANYGEN PREDICT INTELLIGENCE ASSESSMENT
with #YY-MM-DD on the right (2-digit year, e.g. #26-03-12).
The report structure and design are IDENTICAL across all domains. Only research sources and visualization choices change. See Domain Adaptation at the bottom.
Step 1 — Parse Query
Extract:
- event: what is being predicted
- actors: who is involved
- regions / sectors: geographic or industry scope
- timeframe: any dates or deadlines
- question type:
temporal("when"),binary("will"), ormulti_outcome("what")
What ships with it
9 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.
- 10d ago First seen · 406 lines · 74 tokens per session scan A fa8ef06f98da
predict-intelligence is a skill published in the GitHub repository AnyGenIO/anygen-selected-skills (11 stars, last pushed 5mo ago), licensed MIT. It adds 74 tokens to every session and 4,162 once invoked, about $0.0004 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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