Apache SkyWalking BanyanDB is a database for collecting, analyzing, and storing metrics, traces, and logs produced by observability and application-performance systems. It is intended for platforms such as Apache SkyWalking that need to work with monitoring data.
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 skills add apache/skywalking-banyandb --skill gh-issuegit clone --depth 1 https://github.com/apache/skywalking-banyandbWrote 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/apache/skywalking-banyandb/gh-issue)<a href="https://agentmods.dev/skills/apache/skywalking-banyandb/gh-issue"><img src="https://agentmods.dev/badge/skills/apache/skywalking-banyandb/gh-issue/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/apache/skywalking-banyandb/gh-issue"><img src="https://agentmods.dev/badge/skills/apache/skywalking-banyandb/gh-issue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 19 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 281 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium MCP Rug Pull · line 158 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00091 | $0.03020 |
| Opus 5.5 | $0.00036 | $0.01208 |
| Sonnet 5.5 | $0.00018 | $0.00604 |
| Haiku 4.5 | $0.00009 | $0.00302 |
Grade A, and why
gh-issue 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 20d 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing a BanyanDB Issue
BanyanDB's issues live in the apache/skywalking tracker, not in this
repository — apache/skywalking-banyandb has issues disabled. Label them
database, which is the component label for BanyanDB.
A good issue is one somebody can implement from without asking you a question. That is a higher bar than "clear", and it is the bar that decides whether an issue sits untouched for a year.
Size the work before polishing the issue
Issue length is not issue size. Inspect the design and the relevant production code before drafting. Count independently testable behaviour, integration seams, callers, format families, lifecycle domains, fixture work, and test suites. A short issue that says "replace the index" can be much larger than a long issue that fixes one query operation.
Always produce a short size audit during review, even if it will not appear in the filed body:
## Size audit
Classification: executable leaf | tracking parent
Boundary: <one seam, or the several seams that make this a parent>
Production activation: <the caller switched by this merge>
RED test: <the command and the observable failure on current main>
End to end: <the real operation proved by the merge>
Format/lifecycle scope: <sections or algorithms; read/write/publish/merge/GC/etc.>
Fixtures and oracle: <who supplies expected values independently of the code under test>
Focused suites: <commands>
Dependencies present on main: yes | no — <evidence>
Decision: <fits one run, or the exact split required>
Do not infer the answers only from the proposal. Use repository evidence such as interfaces, call sites, packages, fixtures, and existing tests. Treat line count as supporting evidence, never as the sole sizing rule.
Executable leaf versus tracking parent
An executable leaf must satisfy all of these:
- It exposes one agreed boundary and delivers one behaviour end to end.
- The same merge switches at least one named production or CLI caller to it.
- Every requirement has a RED test that fails on current
mainfor a stated reason and passes after the change. - One focused end-to-end scenario proves the real caller, not just an isolated codec or helper.
- Expected values and persisted bytes come from an independent oracle.
- Its prerequisites and the seam it builds on are already merged to
main. - A contract author can create the boundary and RED tests in one workflow turn, and an implementer can reasonably complete the production change in the next.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 20d ago Changed · -4 lines 474763ca8b70
- 1mo ago First seen · 293 lines · 91 tokens per session scan A e63f7a791d8e
gh-issue is a skill published in the GitHub repository apache/skywalking-banyandb (354 stars, last pushed today), licensed Apache-2.0. It adds 91 tokens to every session and 3,020 once invoked, about $0.0004 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 2026-09-03.
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