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/forgeyclap/claude-forge/forge-predictionnpx skills add ForgeyClap/claude-forge --skill forge-predictiongit clone --depth 1 https://github.com/ForgeyClap/claude-forgeWrote 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/forgeyclap/claude-forge/forge-prediction)<a href="https://agentmods.dev/skills/forgeyclap/claude-forge/forge-prediction"><img src="https://agentmods.dev/badge/skills/forgeyclap/claude-forge/forge-prediction.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.00381 |
| Opus 5 | $0.00022 | $0.00191 |
| Sonnet 5 | $0.00009 | $0.00076 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
forge-prediction 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 5d 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.
What it actually says
Forge playbook — Prediction / sports-data
Hard rules
- Never claim certainty. Every output carries a confidence/risk label.
- No automatic real-money betting / no auto-stake code path.
- Backtest before trusting any model; show sample size.
- Verify data-source quality; track all assumptions.
- Telegram token in env only; one message format, tested.
Team (conditional)
Lead: mle-reviewer + planner. Specialists: mle-reviewer, python-reviewer, silent-failure-hunter (data gaps look like clean zeros), database-reviewer (data store).
Skills / commands / MCP
systematic-debugging, /test-coverage. For delivery, defer to forge-n8n or a Telegram layer (Telegram bots fold in here).
Fan-out & flow
L3. Parallel: data ingestion ∥ model/stat logic ∥ delivery formatting. Serial: ingest → backtest → value/odds calc → uncertainty labeling → delivery.
Domain gates
Data sources + quality verified; statistical logic reviewed; backtesting done; odds/value checked; confidence/risk labels on every output; assumptions documented; data freshness check.
Ship-readiness (unique)
Backtest results shown with sample size; every output labeled with confidence/risk; assumptions + data-quality notes attached; no auto-bet path; Telegram message format tested. The ship-readiness prediction + Telegram checklists are advisory; optionally run codex-reviewer (Codex) on important code — not a blocker.
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.
- 5d ago First seen · 31 lines · 43 tokens per session scan A c718cc28f3f1
forge-prediction is a skill published in the GitHub repository ForgeyClap/claude-forge (2 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 381 once invoked, about $0.0002 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 2026-08-31.
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