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/starrocks/starrocks-debug-skills/importnpx skills add StarRocks/starrocks-debug-skills --skill importgit clone --depth 1 https://github.com/StarRocks/starrocks-debug-skillsWrote 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/starrocks/starrocks-debug-skills/import)<a href="https://agentmods.dev/skills/starrocks/starrocks-debug-skills/import"><img src="https://agentmods.dev/badge/skills/starrocks/starrocks-debug-skills/import.svg" alt="Measured on agentmods" 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 | $0.00095 | $0.07635 |
| Opus 5 | $0.00048 | $0.03818 |
| Sonnet 5 | $0.00019 | $0.01527 |
| Haiku 4.5 | $0.00010 | $0.00764 |
Grade A, and why
import 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 6d 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.
- curl How it starts
The opening of the file, as written. The whole thing — 755 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Import Troubleshooting
Investigation guide for import slowness, timeouts, RPC failures, publish timeouts, Primary Key model tuning, and load profile analysis.
Six root causes account for the vast majority of cases:
- Cause A — Write slow: thread pool bottleneck (async_delta_writer / memtable_flush saturated)
- Cause B — Write slow: BRPC / network issue (connection backlog, packet loss)
- Cause C — Write slow: PK index rebuild during clone / decommission
- Cause D — Publish timeout: compaction lag on PK table
- Cause E — Read slow: source-side bottleneck (Kafka partition count, file IO, HTTP client)
- Cause F — RPC Failed: statistics collection conflict saturating BRPC
Metric Taxonomy — Read This First
Before using any metrics, understand the write pipeline and where each metric fits:
Thread pool metrics (BE-side, per-node)
| Pool | Metrics | Meaning |
|---|---|---|
async_delta_writer |
pending |
Queue wait time — high = pool size insufficient |
async_delta_writer |
execute |
Task execution time — contains wait_flush + wait_replica |
async_delta_writer |
wait_flush |
Wait for memtable flush — high → analyze memtable_flush pool |
async_delta_writer |
wait_replica |
Wait for secondary replica sync — high → analyze segment_replicate |
async_delta_writer |
pk_preload |
PK index rebuild time — high → set skip_pk_preload = true |
memtable_flush |
pending |
Queue wait time |
memtable_flush |
execute |
Total flush time |
memtable_flush |
io |
IO portion of flush — high = disk or S3 bottleneck |
memtable_flush |
rate |
Flush rate (tasks/sec) |
segment_replicate_sync |
pending / execute |
Replica sync queue wait and processing time |
segment_flush |
pending / execute / io |
Secondary disk flush metrics |
BRPC metrics (BE-side, per interface)
| Metric | Meaning |
|---|---|
total / used |
Total / in-use BRPC threads — if used ≈ total, BRPC is saturated |
latency-avg / latency-99 |
Interface latency — high P99 = server-side processing slow |
Brpc Processing Requests |
In-flight RPC count — 0 during timeout = network or BRPC issue |
What ships with it
6 files 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.
- references/02-network-and-routineload.md 1.3 KB
- references/03-import-schema-queries.md 6.6 KB
- references/case-001-broker-load-backlog.md 2.2 KB
- references/case-002-rpc-failed-statistics.md 3.2 KB
- references/case-017-reached-timeout-replica-sync.md 4.3 KB
- references/case-018-orc-compression-buffer-overflow.md 3.9 KB
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.
- 6d ago First seen · 755 lines · 95 tokens per session scan A f8e4758f85ae
import is a skill published in the GitHub repository StarRocks/starrocks-debug-skills (75 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 95 tokens to every session and 7,635 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
hermes-mnemosyne
Mnemosyne is Hermes' primary local-first memory engine — SQLite with vector + FTS5 hybrid search, 19+ tools, auto-consolidation, and a standalone CLI. It's a pip-installed plugin (not a built-in toolset) discovered via $HERMESHOME/plugins/mnemosyne/.
exasol-import-export
Exasol IMPORT and EXPORT SQL statements: syntax, file formats (CSV, FBV, Parquet), cloud storage (S3, Azure, GCS), connection objects, error handling, and ETL staging patterns.
goalfydata
Use when the user needs deep data analysis (multi-round SQL queries, aggregation, trend comparison) or wants to persist data (Excel / CSV / API / databases) as a long-lived, cross-platform structured asset — typical scenarios include complex or repeated analysis, accessing the same data across multiple Agents /…
goalfydata
Use when the user needs deep data analysis (multi-round SQL queries, aggregation, trend comparison) or wants to persist data (Excel / CSV / API / databases) as a long-lived, cross-platform structured asset — typical scenarios include complex or repeated analysis, accessing the same data across multiple Agents /…
schema-exploration
Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.
sdk-design
Doctrine for designing and evolving any SDK Grida ships — TypeScript, Rust, or otherwise. "SDK" here means a surface that crosses a foreign-or-foreign-treated boundary: published packages, separately-versioned consumers, FFI bindings, public-by-design modules. An SDK's job is to refuse; a strict, honest surface…