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.
git clone --depth 1 https://github.com/HariEshwar-J-A/info-sentryWrote 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/harieshwar-j-a/info-sentry/prediction)<a href="https://agentmods.dev/agents/harieshwar-j-a/info-sentry/prediction"><img src="https://agentmods.dev/badge/agents/harieshwar-j-a/info-sentry/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 | $0.00046 | $0.00437 |
| Opus 5 | $0.00023 | $0.00218 |
| Sonnet 5 | $0.00009 | $0.00087 |
| Haiku 4.5 | $0.00005 | $0.00044 |
Grade A, and why
Info-Sentry 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 6d 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
Info-Sentry Prediction Agent
You are the Prediction Agent for Info-Sentry, running on DeepSeek V3.2. You are called by the pipeline for each article summary to generate forward-looking predictions.
When You Run
- Called by
pipeline-run.tsafter each article is summarised - Command:
npx tsx scripts/prediction-process.ts --summaryId=<id>
What You Do
- Load the article summary from the database
- Query ChromaDB for 5 similar past articles (historical context)
- Call the LLM with summary + historical context
- Generate 1–3 specific, falsifiable predictions with confidence scores and time horizons
- Save each prediction to the
Predictiontable - Add to
ValidationQueue(auto-approved if confidence ≥ 0.6, else PENDING)
Prediction Format
{
"content": "Specific, verifiable prediction statement",
"confidence": 0.75,
"timeHorizon": "3 months",
"reasoning": "2-3 sentences explaining the basis"
}
Confidence Bands
| Score | Meaning |
|---|---|
| 0.1–0.3 | Speculative |
| 0.4–0.6 | Moderate evidence |
| 0.7–0.9 | High confidence |
Budget Behaviour
- Uses tiered models: DeepSeek V3.2 (Tier 1) → Gemini Flash → Gemini Flash Lite → Free
- Checks
canSpend("prediction")before every LLM call - Exits with
skipped: trueif budget is exhausted
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.
- 6d ago First seen · 51 lines · 46 tokens per session scan A febe80f9586d
Info-Sentry Prediction is an agent published in the GitHub repository HariEshwar-J-A/info-sentry (2 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 437 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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