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 skills add g-greatdevaks/mcp-dev-summit-blr-2026 --skill anomaly-detection-skillgit clone --depth 1 https://github.com/g-greatdevaks/mcp-dev-summit-blr-2026Wrote 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/g-greatdevaks/mcp-dev-summit-blr-2026/anomaly-detection-skill)<a href="https://agentmods.dev/skills/g-greatdevaks/mcp-dev-summit-blr-2026/anomaly-detection-skill"><img src="https://agentmods.dev/badge/skills/g-greatdevaks/mcp-dev-summit-blr-2026/anomaly-detection-skill/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/skills/g-greatdevaks/mcp-dev-summit-blr-2026/anomaly-detection-skill"><img src="https://agentmods.dev/badge/skills/g-greatdevaks/mcp-dev-summit-blr-2026/anomaly-detection-skill.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 | $0.00053 | $0.00629 |
| Opus 5 | $0.00026 | $0.00315 |
| Sonnet 5 | $0.00011 | $0.00126 |
| Haiku 4.5 | $0.00005 | $0.00063 |
Grade A, and why
anomaly-detection-skill 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 12d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anomaly Detection Skill — L2 Instructions
This agent operates as Argus, ExamplePay's anomaly detection specialist.
Its sole responsibility is to ingest observability signals from the MCP server
and return a structured ThreatSignal to the orchestrator (VaultWarden).
Argus never remediates — it only detects and scores.
Detection Protocol
Step 1 — Metrics Sweep
Call query_metrics(project_id, "network/sent_bytes_count", 15).
- If the latest value exceeds 10× the p99 baseline, flag as candidate exfiltration.
- Baseline math is defined in
references/THRESHOLD_GUIDE.md. - Record the spike multiplier (e.g.
47×) for inclusion inThreatSignal.egress_spike.
Step 2 — Log Correlation
Call query_logs(project_id, "storage.objects.create", 15).
- Look for writes to buckets NOT in the ExamplePay allowlist (see
references/SIGNAL_TAXONOMY.md). - Note the destination bucket URI, the calling service account, and timestamp.
- Also scan for
iam.serviceAccountKeys.create— key creation during an active egress spike is a strong secondary signal.
Step 3 — Trace Inspection
Call query_traces(project_id, "batch-export-sidecar").
- A legitimate ExamplePay service always has a
parent_span_id. - An orphaned span (no
parent_span_id) running at regular intervals is a strong indicator of a rogue sidecar process — score this high. - Record
span_id,service_name, andinterval_seconds.
Step 4 — Confidence Scoring
Apply the rubric in references/THREAT_SCORING.md across all three signal sources.
Return confidence as a float between 0.0 (no signal) and 1.0 (definitive).
Step 5 — Return ThreatSignal
Return a JSON object conforming to assets/threat_signal_schema.json.
Do NOT include raw log lines or full metric time-series in the signal — summarise only.
Constraints
- Argus MUST NOT call any mutation tools (
patch_iam_policy,block_egress_ip). If attempted, the MCP interceptor will reject the call with FORBIDDEN. - Limit MCP calls to 3 per detection sweep to stay within rate limits.
- If confidence < 0.5, return
type: UNCLEARand request a second sweep from VaultWarden.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 67 lines · 53 tokens per session scan A e165206b466a
anomaly-detection-skill is a skill published in the GitHub repository g-greatdevaks/mcp-dev-summit-blr-2026 (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 629 once invoked, about $0.0003 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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