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 agentmods add skills/lablup/backend.ai/observabilitynpx skills add lablup/backend.ai --skill observabilitygit clone --depth 1 https://github.com/lablup/backend.aiWhat 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 | $0.00052 | $0.00978 |
| Opus 5 | $0.00026 | $0.00489 |
| Sonnet 5 | $0.00010 | $0.00196 |
| Haiku 4.5 | $0.00005 | $0.00098 |
Grade A, and why
observability 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 2d 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.
The source is not reproduced here
Licensed LGPL-3.0
The repository is licensed LGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 2d ago First seen · 91 lines · 52 tokens per session scan A 97347ba1940d
observability is a skill published in the GitHub repository lablup/backend.ai (672 stars, last pushed 4d ago), licensed LGPL-3.0. It adds 52 tokens to every session and 978 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 2026-08-30.
Other skills, from other repositories
greptimedb-development-docker-image
Builds a development-only GreptimeDB Docker image from a local debug binary for local-cluster testing, and optionally pushes it to a development registry. Use when the user asks to package, build, tag, publish, or cross-build a non-release GreptimeDB or GreptimeDB Enterprise image for debugging.
greptimedb-fuzz-ci-failure-investigation
Investigate a failed GreptimeDB fuzz CI target link by downloading GitHub Actions job logs plus fuzz artifacts such as kind logs, monitor dumps, and CSV dumps, then correlate the failure with local GreptimeDB source code. Use when the user provides a failed fuzz CI target/job URL or asks to diagnose GreptimeDB fuzz CI…
greptimedb-release-note
Generate a GreptimeDB release changelog with git cliff (correct range, subtract already-released patch PRs, rebuild contributors, add human-curated highlights), output to a file, and prepare the docs-repo blog PR. Use when asked to write/generate a GreptimeDB release note or changelog.
greptimedb-release
Runbook for publishing a new GreptimeDB version (tag + GitHub release + docs release-note PR) on the upstream GreptimeTeam/greptimedb repo. Use when asked to "release" / "publish" a GreptimeDB version (e.g. v1.1.0, v1.0.3).
observability
Structured logging (structlog/Python, pino/Node), health endpoints, PM2 and Docker metrics, alerting patterns.
monitoring-expert
Expert-level monitoring and observability with Prometheus, Grafana, logging, and alerting.