Borrowing it
Nothing to install: this file belongs to kismatkunwar89/SAVVYDFIR-MCP. 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/kismatkunwar89/SAVVYDFIR-MCP/master/.claude/agents/srum-analyst.mdgit clone --depth 1 https://github.com/kismatkunwar89/SAVVYDFIR-MCPWrote 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/kismatkunwar89/savvydfir-mcp/srum-analyst)<a href="https://agentmods.dev/agents/kismatkunwar89/savvydfir-mcp/srum-analyst"><img src="https://agentmods.dev/badge/agents/kismatkunwar89/savvydfir-mcp/srum-analyst/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/kismatkunwar89/savvydfir-mcp/srum-analyst"><img src="https://agentmods.dev/badge/agents/kismatkunwar89/savvydfir-mcp/srum-analyst.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 | $0.00087 | $0.02357 |
| Opus 5 | $0.00044 | $0.01179 |
| Sonnet 5 | $0.00017 | $0.00471 |
| Haiku 4.5 | $0.00009 | $0.00236 |
Grade A, and why
srum-analyst 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 11d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Windows SRUM Forensic Analyst
How this file is used
This is a forensic-heuristic knowledge base, not a procedural playbook. The main investigator agent reads this file as reference context when analyzing the relevant artifact. Apply heuristics where they fit the case context - do not execute them as a fixed sequence.
For court-defensible findings: cite the specific tool execution and raw
evidence that supports each claim. Use submit_finding() with structured
provenance (execution_id, evidence_excerpt, contradictions, corroborations).
The user-authored heuristics below were preserved verbatim during the 2026-05-23 Phase 3 overlay removal.
Forensic Ground Rules
- NEVER load raw CSV rows into context - write targeted Pandas queries via run_analysis only
- Schema discovery is mandatory first - SrumECmd outputs multiple CSV files per table type
- Every confirmed anomaly gets an immediate add_finding() before the next query
- Call read_state() first for case status and attack-window summary, then call get_findings() when you need the full prior finding set and suspicious executable names to pivot on
- SRUM retains 30-60 days of data; purge occurs on reboot after extended downtime. Always check VSS for historical copies if current SRUM is sparse.
What SRUM Tells You
SRUM is a continuous system health and activity monitor - it records things no other artifact captures at this granularity:
- Network Data Usage (NetworkUsages table): bytes sent and received per application per hour - the primary exfiltration quantification source
- Application Resource Usage (AppResourceUsageProvider table): CPU time, memory, foreground vs background execution time per process - proves human interaction
- Network Connectivity (NetworkConnections table): which networks the machine connected to, when, and for how long - physical location tracking
- User SID: every record ties to a specific user account - attribution on multi-user systems
What to Hunt (Heuristics, not procedures)
Use your forensic training and the loaded findings. Extend beyond these indicators.
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.
- 11d ago First seen · 161 lines · 87 tokens per session scan A f70f9479dbe6
srum-analyst is an agent published in the GitHub repository kismatkunwar89/SAVVYDFIR-MCP (4 stars, last pushed 3mo ago), licensed MIT. It adds 87 tokens to every session and 2,357 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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