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/nevaberry/nevaberry-plugins/apache-spark-knowledge-patchnpx skills add Nevaberry/nevaberry-plugins --skill apache-spark-knowledge-patchgit clone --depth 1 https://github.com/Nevaberry/nevaberry-pluginsWhat 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 | $0.00010 | $0.02218 |
| Opus 5 | $0.00005 | $0.01109 |
| Sonnet 5 | $0.00002 | $0.00444 |
| Haiku 4.5 | $0.00001 | $0.00222 |
Grade A, and why
apache-spark-knowledge-patch 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.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apache Spark Knowledge Patch
Use this skill when upgrading, configuring, extending, or debugging modern Apache Spark applications. Check the application's Spark version before using version-specific advice, and prefer the application's manifests, code, and tests when they demonstrate different behavior.
Reference index
| Reference | Topics |
|---|---|
| references/migration-and-runtime.md | Core runtime defaults, compatibility switches, API corrections, and upgrade hazards |
| references/sql-and-dataframes.md | SQL semantics, parser and DDL changes, expressions, types, views, and DataFrame behavior |
| references/data-sources-and-formats.md | Data Source V2, JDBC, catalogs, file formats, schema evolution, and connector APIs |
| references/streaming-and-state.md | Structured Streaming triggers, checkpoints, state APIs, joins, and Python streaming sources |
| references/python-and-pandas.md | PySpark requirements, Arrow execution, pandas API on Spark, UDFs, exceptions, and inference |
| references/connect-ml-and-pipelines.md | Spark Connect, Spark ML, Declarative Pipelines, client modes, and remote lifecycle APIs |
| references/deployment-security-observability.md | Kubernetes, YARN, standalone deployment, security, metrics, logging, web UI, and History Server |
| references/dependencies-and-packaging.md | Bundled libraries, language runtimes, build flags, packaging, and dependency alignment |
Upgrade triage
Before changing code, identify whether the failure comes from a new default, a removed compatibility alias, a stricter correctness check, or a genuinely new API. Use the following order:
- Pin the exact Spark runtime used by driver and executors.
- Compare SQL, Python, streaming, storage, and deployment compatibility flags.
- Confirm bundled dependency overrides match Spark's own dependency line.
- Re-run query-result and checkpoint-restart tests, not only compilation.
- Use a legacy switch only as a temporary migration step and document it.
What ships with it
9 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.
- coverage.json 1.3 KB
- references/connect-ml-and-pipelines.md 3.4 KB
- references/data-sources-and-formats.md 8.4 KB
- references/dependencies-and-packaging.md 3.8 KB
- references/deployment-security-observability.md 5.7 KB
- references/migration-and-runtime.md 5.9 KB
- references/python-and-pandas.md 8.9 KB
- references/sql-and-dataframes.md 10 KB
- references/streaming-and-state.md 5.0 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.
- 2d ago First seen · 194 lines · 10 tokens per session scan A a15e211c02b9
apache-spark-knowledge-patch is a skill published in the GitHub repository Nevaberry/nevaberry-plugins (24 stars, last pushed 5d ago), licensed MIT. It adds 10 tokens to every session and 2,218 once invoked, about $0.0001 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
design-mcp-server
Design the tool surface, resources, and service layer for a new MCP server. Use when starting a new server, planning a major feature expansion, or when the user describes a domain/API they want to expose via MCP. Produces a design doc at docs/design.md that drives implementation.
api-context
Canonical reference for the unified Context object passed to every tool and resource handler in @cyanheads/mcp-ts-core. Covers the full interface, its RequestContext base, all sub-APIs (ctx.log, ctx.state, ctx.requestInput, ctx.inputs, ctx.enrich, ctx.content), and when to use each.
api-canvas
DataCanvas primitive reference — a Tier 3 SQL/analytical workspace for tabular MCP servers, backed by DuckDB. Use when registering tables from upstream APIs, running ad-hoc SQL across them, and exporting results. Covers the acquire → register → query → export flow, per-table TTL, the token-sharing pattern for…
api-testing
Testing patterns for MCP tool/resource handlers using createMockContext and Vitest. Covers mock context options, handler testing, McpError assertions, format testing, Vitest config setup, and test isolation conventions.
field-test
Exercise tools, resources, and prompts against a live HTTP server via MCP JSON-RPC over curl. Starts the server, surfaces the catalog, runs real and adversarial inputs, and produces a tight report with concrete findings and numbered follow-up options. Use after adding or modifying definitions, or when the user asks to…
api-config
Reference for core and server configuration in @cyanheads/mcp-ts-core. Covers env var tables with defaults, priority order, server-specific Zod schema pattern, and Workers lazy-parsing requirement.