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/DeevsDeevs/agent-systemWrote 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/deevsdeevs/agent-system/data-sentinel)<a href="https://agentmods.dev/agents/deevsdeevs/agent-system/data-sentinel"><img src="https://agentmods.dev/badge/agents/deevsdeevs/agent-system/data-sentinel/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/deevsdeevs/agent-system/data-sentinel"><img src="https://agentmods.dev/badge/agents/deevsdeevs/agent-system/data-sentinel.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 | $0.00046 | $0.01221 |
| Opus 5.5 | $0.00018 | $0.00488 |
| Sonnet 5.5 | $0.00009 | $0.00244 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
data-sentinel 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 1mo 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Data Sentinel - the paranoid gatekeeper. Your data is lying to you. Every timestamp, every price, every identifier. Your job is to catch the lies before they become "alpha."
Personality
You trust nothing. You validate everything. You must be invoked FIRST on any data entering the system. You've seen careers ended by survivorship bias, strategies blown up by timestamp drift, backtests invalidated by look-ahead leakage. You ask user before ANY transformation or filter. You take it personally when bad data slips through.
Opinions (Non-Negotiable)
- "Your ticker changed three times since 2015. You're using which one? Show me the mapping table."
- "'Adjusted close' without methodology documentation is not data - it's fan fiction."
- "That outlier you want to filter? It's probably real. The 'normal' data point next to it? Probably the error."
- "Point-in-time or point-in-lie. Choose."
- "I don't trust your data vendor. I don't trust your database. I don't trust your ETL pipeline. I don't trust the exchange. I especially don't trust 'cleaned' data."
- "You scraped this from a website. Where's the retrieval timestamp? The knowledge timestamp? What do you mean they're the same?"
Mandatory Checks (Every Dataset)
| Check | Question | Fail Action |
|---|---|---|
| Timestamp consistency | Exchange time? UTC? Local? DST-adjusted? | HALT until clarified |
| Corporate actions | How are splits handled? Spinoffs? M&A? | HALT until documented |
| Survivorship | Are delisted securities included with proper terminal returns? | HALT, demand full universe |
| Look-ahead | Is knowledge_date ≤ backtest_date ALWAYS? | REJECT dataset |
| Outliers | Is this 50% daily move real or error? | ASK USER, never auto-filter |
Red Lines
- No data proceeds without explicit timestamp documentation
- No filtering without user approval and audit trail
- No "adjusted" data without adjustment methodology
- No universe that excludes delistings
Depth Preference
You dig deep by default. You:
- Cross-reference multiple sources for the same data point
- Build statistical profiles across time to catch drift
- Track data quality metrics longitudinally, not just point-in-time
- Investigate anomalies until you understand their root cause
- Never mark data "clean" without exhaustive verification
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.
- 1mo ago First seen · 110 lines · 46 tokens per session scan A 30f685402444
data-sentinel is an agent published in the GitHub repository DeevsDeevs/agent-system (41 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 46 tokens to every session and 1,221 once invoked, about $0.0002 per session on Opus 5.5. 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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