Borrowing it
Nothing to install: this file belongs to pallerana/agentic-loop-engineering-kit. 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/pallerana/agentic-loop-engineering-kit/main/.cursor/skills/ops-incident-loop/SKILL.mdgit clone --depth 1 https://github.com/pallerana/agentic-loop-engineering-kitWrote 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/pallerana/agentic-loop-engineering-kit/ops-incident-loop)<a href="https://agentmods.dev/skills/pallerana/agentic-loop-engineering-kit/ops-incident-loop"><img src="https://agentmods.dev/badge/skills/pallerana/agentic-loop-engineering-kit/ops-incident-loop/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/pallerana/agentic-loop-engineering-kit/ops-incident-loop"><img src="https://agentmods.dev/badge/skills/pallerana/agentic-loop-engineering-kit/ops-incident-loop.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 | $0.00046 | $0.00637 |
| Opus 5.5 | $0.00018 | $0.00255 |
| Sonnet 5.5 | $0.00009 | $0.00127 |
| Haiku 4.5 | $0.00005 | $0.00064 |
Grade A, and why
ops-incident-loop 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.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ops Incident Loop
Thin extension for ops-incident profile. Orchestrator: agentic-loop.
Triggers
/agentic-loop --profile ops-incident --pagerduty INC-…
/agentic-loop --profile ops-incident --datadog-monitor <id>
/agentic-loop --profile ops-incident --mode L1 --datadog-monitor <id>
Phase 0 — Incident context
| Source | Action |
|---|---|
| Datadog MCP | Monitor, logs, metrics, traces for service: tag |
| PagerDuty MCP | Incident timeline, services, notes |
| graphify | graphify query on ddog__ nodes; path to Java handlers |
| Wiki | ops-and-oncall.md, observability-ddog.md |
| Glean | /codebase-context for alert service |
| DDog repo wiki | observability-iac//docs/wiki/ (cross-link only) |
Record in ops-incident-state.md.
Phase 1 — Hypothesis doc (not feature plan)
Template in STATE file:
- Symptom (user-visible / SLO)
- Timeline (UTC)
- Suspects (service, deploy, dependency)
- Evidence (DD/PD links, log snippets)
- Next queries (specific MCP calls)
Phase 2 — Hypothesis review
- Cross-check monitor definition vs actual traffic
- Confirm or rule out infra vs application
- Human gate before L2 fix scope (
human_gates: always)
L2 fix scope (human approved)
/agentic-loop --profile ops-incident --handoff cell-health \
--repo your-service-cell-health-aggregator PROJ-153 \
--from-state loop-kit/ops-incident-state.md
Continues feature phases 0–9 on target profile.
Constraints
- No push in L1 (
ship: none) - Never
terraform applyin loop - Infra-only issues →
STATE.mdhuman inbox
Query templates (Datadog)
- Logs:
service:<name> status:error @timestamp:>… - Traces:
service:<name> @http.status_code:5* - Metrics: monitor message +
avg:trace.servlet.request.errors{service:<name>}
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.
- 1mo ago First seen · 75 lines · 46 tokens per session scan A ae90e8a98c3a
ops-incident-loop is a skill published in the GitHub repository pallerana/agentic-loop-engineering-kit (4 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 46 tokens to every session and 637 once invoked, about $0.0002 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 2026-08-31.
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