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 Jacobinwwey/notemdpro --skill batch-processorgit clone --depth 1 https://github.com/Jacobinwwey/notemdproWrote 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/jacobinwwey/notemdpro/batch-processor)<a href="https://agentmods.dev/skills/jacobinwwey/notemdpro/batch-processor"><img src="https://agentmods.dev/badge/skills/jacobinwwey/notemdpro/batch-processor/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/jacobinwwey/notemdpro/batch-processor"><img src="https://agentmods.dev/badge/skills/jacobinwwey/notemdpro/batch-processor.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 | $0.00017 | $0.00488 |
| Opus 5.5 | $0.00007 | $0.00195 |
| Sonnet 5.5 | $0.00003 | $0.00098 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
batch-processor 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 16d 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.
What it actually says
Batch processor
English
Use workflow-runner for the common task record. Resolve the authorized folder and output policy once. Enumerate eligible files deterministically, exclude symlinks outside the selected tree, and process each file through read -> transform -> validate -> write. Recheck the source before writing; preserve unrelated notes and source artifacts.
Bound concurrency according to the host/provider. Retry only transient transport failures and HTTP 408/425/429/500/502/503/504 within a finite attempt/time budget; honor Retry-After when available. Authentication, invalid configuration, parser errors and cancellation do not retry. Stop scheduling new work when cancelled, retain completed outcomes, and report pending/cancelled files separately.
Keep a single task transcript across stages and files. Log stage, path, attempt, severity and actionable error summaries; never log credentials, authorization headers or raw provider payloads. Record partial failures honestly. File helper progress is separate from transcript retention. No app.vault API or local provider implementation exists here.
中文
共享任务记录遵循 workflow-runner。一次性确认授权目录及输出策略,确定性枚举可处理文件,排除指向选定目录外的符号链接,逐文件执行读取 -> 转换 -> 验证 -> 写入。写入前重新检查源文件,保留无关笔记与源产物。
按宿主/provider 能力限制并发。仅对暂态传输失败和 HTTP 408/425/429/500/502/503/504 在有限次数及时间内重试;可用时遵循 Retry-After。认证、配置、解析错误及取消不得重试。取消后停止调度,保留已完成结果,分别报告待处理及已取消文件。
跨阶段、跨文件保留一份完整任务日志。记录阶段、路径、重试次数、严重级别及可操作的错误摘要,不记录凭据、认证头或原始 provider 载荷。如实报告部分失败。进度与日志保留独立;此处不存在 app.vault API 或本地 provider 实现。
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.
- 16d ago Changed · -229 lines · -19 tokens per session b9693c56f9dd
- 1mo ago First seen · 252 lines · 36 tokens per session scan A fe7941355ce7
batch-processor is a skill published in the GitHub repository Jacobinwwey/notemdpro (2 stars, last pushed 16d ago), licensed MIT. It adds 17 tokens to every session and 488 once invoked, about $0.0001 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.
Other skills, from other repositories
parallel-feature-development
Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system…
nft-standards
Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.
cost-optimization
Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.
linkerd-patterns
Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, or implementing zero-trust networking with minimal overhead.
postgresql-table-design
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.