Borrowing it
Nothing to install: this file belongs to DuqueOM/ML-MLOps-Portfolio. 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/DuqueOM/ML-MLOps-Portfolio/main/.devin/skills/performance-degradation-rca/SKILL.mdgit clone --depth 1 https://github.com/DuqueOM/ML-MLOps-PortfolioWrote 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/duqueom/ml-mlops-portfolio/performance-degradation-rca)<a href="https://agentmods.dev/skills/duqueom/ml-mlops-portfolio/performance-degradation-rca"><img src="https://agentmods.dev/badge/skills/duqueom/ml-mlops-portfolio/performance-degradation-rca/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/duqueom/ml-mlops-portfolio/performance-degradation-rca"><img src="https://agentmods.dev/badge/skills/duqueom/ml-mlops-portfolio/performance-degradation-rca.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 | $0.00042 | $0.02249 |
| Opus 5 | $0.00021 | $0.01125 |
| Sonnet 5 | $0.00008 | $0.00450 |
| Haiku 4.5 | $0.00004 | $0.00225 |
Grade A, and why
performance-degradation-rca scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Bash(curl:*) How it starts
The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Degradation RCA — Multi-stream root cause
This skill is the post-incident counterpart to /rollback. Rollback
restores service; this skill explains WHY degradation happened and what
long-term fix is needed to prevent recurrence.
When NOT to use this skill
- Single-slice performance alert — use
concept-drift-analysisfirst; it's narrower and faster. - Active incident with user impact — run
/rollbackfirst, this skill after the dust settles. - Drift-only without performance impact — use
drift-detection; degradation RCA assumes ground truth has already revealed regression.
Evidence streams
The skill correlates FIVE independent streams, then synthesizes:
- Sliced performance metrics (from
performance_monitorPrometheus push) — which slices degraded, by how much, when - PSI drift history (
ops/drift_reports/*.json) — was the distribution shift gradual or sudden? - Deploy + release history (MLflow Registry,
kubectl rollout history, GitHub Releases) — did a deploy precede the regression? - Upstream data (feature store / ETL provenance) — did an upstream schema or semantic change leak through?
- Prediction log statistics — score distribution shift, error rate per version, logger error rate (if this spiked, our verdict is uncertain — flag it)
Execution flow
Step 1 — Incident intake (AUTO, 2 min)
# Confirm the incident is declared
gh issue view {incident-id} --json labels,body,createdAt
# Pull the current Prometheus alert state
curl -s "http://prometheus.monitoring:9090/api/v1/query?query=ALERTS{service='{service}'}" | jq '.'
# What alert(s) kicked this off? Record them as the starting hypothesis.
Output: a single-paragraph framing — "At {ts}, {alertname} fired for {service} claiming {metric}={value}. Impact window: {start}→{end}."
Step 2 — Sliced regression scan (AUTO, 3 min)
# What slices are below baseline? Which are ABOVE?
curl -s 'http://prometheus.monitoring:9090/api/v1/query_range?query=\
{service}_performance_metric{metric="auc"}&start=...&end=...&step=1h' | \
jq '.data.result' > ops/incidents/{incident}/sliced_auc.json
# Rank slices by magnitude of regression (current - baseline)
python scripts/rank_regressions.py ops/incidents/{incident}/sliced_auc.json
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.
- 10d ago First seen · 245 lines · 42 tokens per session scan A 3821ff7d6cda
performance-degradation-rca is a skill published in the GitHub repository DuqueOM/ML-MLOps-Portfolio (5 stars, last pushed 2d ago), licensed MIT. It adds 42 tokens to every session and 2,249 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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