Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/attune-ai/main/.agents/skills/bulk/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/bulk)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/bulk"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/bulk/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/smart-ai-memory/attune-ai/bulk"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/bulk.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.00812 |
| Opus 5.5 | $0.00018 | $0.00325 |
| Sonnet 5.5 | $0.00009 | $0.00162 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
bulk 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 1mo 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bulk Batch Processing
IMPORTANT: Start your response with a context preamble.
Call help_lookup(topic="bulk", mode="preamble") and display
the returned preamble text as a blockquote. Then tell the
user they can say "tell me more" for a step-by-step guide, or
answer the scoping questions below to proceed.
If the MCP call fails, fall back to:
Bulk — Submits tasks to the Anthropic Batch API for 50% cost savings. Runs asynchronously (up to 24h), so it's ideal for non-urgent, high-volume analysis rather than interactive work.
Scoping
Before submitting, ask:
- What to batch: "Which tasks should I batch — e.g. a workflow run across many files, or many independent analyses?"
- How many / which targets: "List the items (files, modules, or task inputs) to process."
- Urgency check: "Batch results take up to 24h. Is non-urgent turnaround acceptable? If you need it now, run the single-shot workflow instead."
Execution
Shared command workspace (preferred)
Open adapter bulk with either the provider-ready requests list or an
existing batch_id. Present the widget or returned Markdown. New submissions
must consume the bound, explicitly confirmed submit_batch action before
calling analyze_batch; a reconnect uses the read-only check_batch action.
Publish the real provider response as submission_result or status_result.
Only a response with the exact accepted task count and a non-empty batch id may
render as submitted. Rejections and timeouts must render “did not submit” (or
“did not complete” for status), never a synthetic batch id. Preserve the same
decision and receipt in compact text when the shared tools are unavailable.
The workspace status receipt completes the interactive invocation; pending
does not mean the remote work completed. Reinvoke later with the returned
batch_id to reconnect.
Call the analyze_batch MCP tool with a requests array,
one entry per task:
analyze_batch(requests=[
{"task_id": "<unique-id>", "task_type": "<e.g. analyze_logs>",
"input_data": {...}, "model_tier": "capable"},
...
])
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.
- 1mo ago Changed 9c468c796c09
- 1mo ago Changed · +16 lines 2af5c4804a34
- 1mo ago First seen · 70 lines · 44 tokens per session scan A 6c4bb2230671
bulk is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (11 stars, last pushed yesterday), licensed Apache-2.0. It adds 44 tokens to every session and 812 once invoked, about $0.0002 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.
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