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cross-source-correlation

cross-source-correlation is a skill for Claude Code, Codex from aozyildirim/Agena. It costs 64 tokens per session (801 once invoked), scanned A, original, MIT.

A tool for comparing events from code changes, deployments, monitoring systems, and issue trackers within the same time window. It groups related events into confidence-scored incident clusters.

In plain words
What is it for?
Use it during incident investigation to correlate pull-request merges, deployments, error alerts from services such as Sentry or Datadog, and tickets from Jira or Azure DevOps.
Why use it?
It reduces the manual work of switching between systems to determine which code change or deployment may have caused a bug.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 103repo 15d ago A scan SkillSpector: pass 64 tokens original MIT

Good fit Use it during incident investigation to correlate pull-request merges, deployments, error alerts from services such as Sentry or Datadog, and tickets from Jira or Azure DevOps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aozyildirim/agena/cross-source-correlation
View source ↗ aozyildirim/Agena agena.dev
Install

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.

Any agent
npx skills add aozyildirim/Agena --skill cross-source-correlation
Clone the repo
git clone --depth 1 https://github.com/aozyildirim/Agena

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for cross-source-correlation

README.md
[![agentmods](https://agentmods.dev/badge/skills/aozyildirim/agena/cross-source-correlation/github.svg)](https://agentmods.dev/skills/aozyildirim/agena/cross-source-correlation)
Your own site
<a href="https://agentmods.dev/skills/aozyildirim/agena/cross-source-correlation"><img src="https://agentmods.dev/badge/skills/aozyildirim/agena/cross-source-correlation/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.

agentmods 80×15 button for cross-source-correlation

Your own site · 80×15
<a href="https://agentmods.dev/skills/aozyildirim/agena/cross-source-correlation"><img src="https://agentmods.dev/badge/skills/aozyildirim/agena/cross-source-correlation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 801 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.00801
Opus 5.5 $0.00026 $0.00320
Sonnet 5.5 $0.00013 $0.00160
Haiku 4.5 $0.00006 $0.00080

Measured 1mo ago against content hash 3ee09b6f5118, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

cross-source-correlation 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.

skills/agena/cross-source-correlation/SKILL.md · 70 lines

How it starts

The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cross-Source Correlation

When a Sentry alert fires, a senior engineer mentally cross-references the last deploy time, recent PRs, NewRelic apdex, and the support tickets opened in the last hour. AGENA's Cross-Source Correlation engine does the same correlation deterministically, producing a single confidence-scored cluster per incident.

How to apply this pattern

  1. Pick a time window (default 60 minutes ending now) and pull every event your platform observed inside it: PR merges, deploys, error imports (Sentry / NewRelic / Datadog / AppDynamics), and work-item imports (Jira / Azure DevOps).

  2. Score candidate clusters using a heuristic that rewards co-location in time + repo:

    • PR merge inside the window → +40
    • Deploy from the same repo inside the window → +20
    • One monitoring signal in the window → +20 (+30 if more than one)
    • One work-item opened in the window → +10 (+20 if more than one)
    • Bonus +10 when a PR is present alongside any monitoring signal

  3. Surface only clusters ≥ 70. Below that the noise / signal ratio collapses; above it you get the "vay" moment for incident triage.

  4. Persist the cluster with: primary_kind, primary_label, related_events[], confidence, severity, narrative, repo_mapping_id. Keep a fingerprint for idempotency so repeated poller runs don't double-insert the same cluster.

  5. Offer triage actions on each cluster: confirm, false-positive, undo, and (when a PR is the prime suspect) one-click rollback.

Example

🔴 CRITICAL · confidence 94%

PR #4519 (erinc, merged 14:18) in checkout-api correlates with
2 monitoring signal(s) (sentry, newrelic) and 1 work-item opened
in the same window.

Timeline:
  14:18  🔀 PR #4519 merged — payment_service.py, +47/-12
  14:18  🚀 deploy a1b2c3d4 → production
  14:23  🚨 Sentry: TypeError in payment_service.py:88 (47×)
  14:24  📡 NewRelic: apdex 0.92 → 0.41
  14:31  🪐 Jira SUP-128 opened — 12 customers report failed checkout

Read the full file on GitHub · 70 lines

Changes

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.

  1. 1mo ago First seen · 70 lines · 64 tokens per session scan A 3ee09b6f5118

Subscribe to this mod's changes

cross-source-correlation is a skill published in the GitHub repository aozyildirim/Agena (103 stars, last pushed 15d ago), licensed MIT. It adds 64 tokens to every session and 801 once invoked, about $0.0003 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.

Analytics and metricsCrewAIDebuggingDevOps and cloudagentic-aiagentic-workflowai-agentai-agentscorrelationincident-responseobservabilitypixelpixel-agents
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