Install
$ agentstack add skill-pinexai-claude-code-skills-incident-triage ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Incident Triage
When this fires
Log aggregation tools can cluster errors by signature and show frequency, but they can't see your source code — they stop at "this error happened 1,200 times" without connecting it to the line that causes it. This skill picks up where the log tool stops: cluster the noise, then correlate each cluster back to the actual code so the on-call engineer gets a ranked, evidence-backed list of what to look at first.
Protocol
- Ingest the logs (stdin, pasted text, or a file). Don't pre-filter
before clustering — noise and signal both matter for frequency ranking.
- Cluster by error signature and frequency. Group entries that share
an error type/message shape (normalizing out request-specific values like IDs, timestamps, IPs), and count occurrences per cluster. This frequency ordering is what determines investigation priority, not recency or severity label alone.
- Correlate the top clusters to source locations. For each cluster
worth investigating, find the actual code path that would produce that error — read the source, don't infer it from the error message alone.
- Rank root-cause hypotheses, each citing the specific
file:line
that supports it, ordered by evidence strength and cluster frequency — not by which one seems most plausible in the abstract.
- Propose the smallest safe fix for the top hypothesis, plus a
concrete verification step (a test, a log query, a specific input to check) that would confirm the fix actually addresses the cluster.
Hard rules
- Every hypothesis must cite an exact
file:linein the actual source —
"probably something in the auth module" is not a hypothesis, it's a guess.
- Rank by evidence (frequency × how directly the code path explains the
error), never by gut feeling about what's "usually" the cause.
- Don't propose a broad rewrite as the fix when a small change addresses
the identified line — match the fix size to the diagnosed cause.
- If a top-frequency cluster can't be correlated to a source location with
the available code/logs, say so explicitly rather than filling the gap with speculation.
Output
Clusters (by frequency):
1. x
2. x
...
Correlated hypotheses (ranked):
1. : —
2. : —
Proposed fix (for #1):
Verification:
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: pinexai
- Source: pinexai/claude-code-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.