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 agentmods add skills/rightnow-ai/openfang/predictornpx skills add RightNow-AI/openfang --skill predictorgit clone --depth 1 https://github.com/RightNow-AI/openfangWhat 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 | $0.00029 | $0.02454 |
| Opus 5 | $0.00015 | $0.01227 |
| Sonnet 5 | $0.00006 | $0.00491 |
| Haiku 4.5 | $0.00003 | $0.00245 |
Grade A, and why
predictor-hand-skill 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 yesterday.
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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forecasting Expert Knowledge
Superforecasting Principles
Based on research by Philip Tetlock and the Good Judgment Project:
- Triage: Focus on questions that are hard enough to be interesting but not so hard they're unknowable
- Break problems apart: Decompose big questions into smaller, researchable sub-questions (Fermi estimation)
- Balance inside and outside views: Use both specific evidence AND base rates from reference classes
- Update incrementally: Adjust predictions in small steps as new evidence arrives (Bayesian updating)
- Look for clashing forces: Identify factors pulling in opposite directions
- Distinguish signal from noise: Weight signals by their reliability and relevance
- Calibrate: Your 70% predictions should come true ~70% of the time
- Post-mortem: Analyze why predictions went wrong, not just celebrate the right ones
- Avoid the narrative trap: A compelling story is not the same as a likely outcome
- Collaborate: Aggregate views from diverse perspectives
Signal Taxonomy
Signal Types
| Type | Description | Weight | Example |
|---|---|---|---|
| Leading indicator | Predicts future movement | High | Job postings surge → company expanding |
| Lagging indicator | Confirms past movement | Medium | Quarterly earnings → business health |
| Base rate | Historical frequency | High | "80% of startups fail within 5 years" |
| Expert opinion | Informed prediction | Medium | Analyst forecast, CEO statement |
| Data point | Factual measurement | High | Revenue figure, user count, benchmark |
| Anomaly | Deviation from pattern | High | Unusual trading volume, sudden hiring freeze |
| Structural change | Systemic shift | Very High | New regulation, technology breakthrough |
| Sentiment shift | Collective mood change | Medium | Media tone change, social media trend |
Signal Strength Assessment
STRONG signal (high predictive value):
- Multiple independent sources confirm
- Quantitative data (not just opinions)
- Leading indicator with historical track record
- Structural change with clear causal mechanism
MODERATE signal (some predictive value):
- Single authoritative source
- Expert opinion from domain specialist
- Historical pattern that may or may not repeat
- Lagging indicator (confirms direction)
WEAK signal (limited predictive value):
- Social media buzz without substance
- Single anecdote or case study
- Rumor or unconfirmed report
- Opinion from non-specialist
What ships with it
1 file 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.
- yesterday First seen · 273 lines · 29 tokens per session scan A 959c2985e490
predictor-hand-skill is a skill published in the GitHub repository RightNow-AI/openfang (18,148 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 29 tokens to every session and 2,454 once invoked, about $0.0001 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
skill-creator
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
update-setup
One-time setup wizard for the nanobot upgrade skill. Triggers: setup update, configure update, 设置更新, 初始化更新.
tmux
Remote-control tmux sessions for interactive CLIs by sending keystrokes and scraping pane output.
image-generation
Generate images and iteratively edit saved image artifacts.
cron
Schedule reminders and recurring tasks.
summarize
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).