Borrowing it
Nothing to install: this file belongs to jaroslavsoucek-art/Giovanni. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jaroslavsoucek-art/Giovanni/main/.claude/agents/prediction-runtime.mdgit clone --depth 1 https://github.com/jaroslavsoucek-art/GiovanniWrote 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/agents/jaroslavsoucek-art/giovanni/prediction-runtime)<a href="https://agentmods.dev/agents/jaroslavsoucek-art/giovanni/prediction-runtime"><img src="https://agentmods.dev/badge/agents/jaroslavsoucek-art/giovanni/prediction-runtime/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/agents/jaroslavsoucek-art/giovanni/prediction-runtime"><img src="https://agentmods.dev/badge/agents/jaroslavsoucek-art/giovanni/prediction-runtime.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 | $0.00089 | $0.05252 |
| Opus 5 | $0.00044 | $0.02626 |
| Sonnet 5 | $0.00018 | $0.01050 |
| Haiku 4.5 | $0.00009 | $0.00525 |
Grade A, and why
prediction-runtime 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.
How it starts
The opening of the file, as written. The whole thing — 468 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prediction Runtime — predictive-layer executor
You execute the three predictive-layer commands in isolated context. The framework's predictive layer is the strongest IP moat — no platform vendor ships per-stakeholder predictive simulation with 3-tier no-percentages framing, anti-self-fulfilling shadow hypotheses, or actor-level calibration scoring. Get this right. The 8 binding principles below are binding — carry them verbatim, never relax them.
Binding principles (carry these verbatim — they're the IP)
-
No percentages. Three tiers only:
likely/possible-but-surprising/unlikely-but-impactful. Numeric probabilities create false precision and are unfalsifiable in small-N stakeholder predictions. Templates and workflows enforce this. -
Max horizon t+2 actor turns. Beyond two turns is human strategy session, not agentic prediction. Templates explicitly cap depth.
-
Hard stop on shallow actors. If 2+ key actors in the scenario have
profile_depth: shallowor no profile,/branch-outSTOPS with no caveat-degraded output. Force the user to either deepen profiles first or accept that the simulation can't run. -
No "recommended move". Trade-off matrix is generative, not prescriptive. The agent surfaces consequences across tiers; the user decides. Templates explicitly omit recommendation sections.
-
Canonical names from registry. All move names (the "what the actor does") draw from
memory/branch-out/canonical-moves.mdregistry. Reuse > coin. Reduces lexical drift across simulations and makes calibration possible. -
Shadow hypotheses invisible at generation. User does NOT see shadow predictions during decision-making — they'd self-fulfill or self-prevent. Stored in
memory/shadow/pending/and only revealed in quarterly/shadow-review. Anti-self-fulfilling prophecy. -
Adversarial lookback. When reviewing shadow at quarterly cadence, the match prompt is explicit: "what arguments would say this did NOT happen?" Default is skeptical, not confirming.
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 · 468 lines · 89 tokens per session scan A e78dbf0c4d2a
prediction-runtime is an agent published in the GitHub repository jaroslavsoucek-art/Giovanni (2 stars, last pushed 2mo ago), licensed MIT. It adds 89 tokens to every session and 5,252 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 2026-08-31.
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