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/jpicklyk/task-orchestrator/batch-completenpx skills add jpicklyk/task-orchestrator --skill batch-completegit clone --depth 1 https://github.com/jpicklyk/task-orchestratorWhat 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 | $0.00067 | $0.02499 |
| Opus 5 | $0.00034 | $0.01249 |
| Sonnet 5 | $0.00013 | $0.00500 |
| Haiku 4.5 | $0.00007 | $0.00250 |
Grade A, and why
batch-complete 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 3d 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 — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.
batch-complete — Bulk Complete or Cancel Items
Close out a feature subtree, cancel an abandoned workstream, or clean up stale items in one operation. Handles gate checks, active-item warnings, and reports exactly what succeeded and what was skipped.
Step 1 — Identify Scope
Resolve $ARGUMENTS to a UUID via query_items search (operation="search", query=$ARGUMENTS, limit=5); if ambiguous, present matches via AskUserQuestion.
If $ARGUMENTS is empty, classify the request from conversation context:
- Feature subtree: user mentions completing "everything under" a named item → search for that item, use
rootId - Specific items: user lists names or IDs → collect each UUID, use
itemIds - Cleanup: user wants to clear old/stale items → search by status or title fragment, collect UUIDs, use
itemIds
If scope still cannot be determined, ask via AskUserQuestion: "Which item (or items) do you want to complete? Provide a root UUID, a title fragment, or a list of item IDs."
Step 2 — Preview Impact
Before executing, show the user what will happen. Call:
query_items(operation="overview", itemId="<rootId>")
Parse the child counts by role and present a preview table. Use the trigger chosen (or likely to be chosen) to set the action label — trigger="complete" shows "will be completed"; trigger="cancel" shows "will be cancelled":
◆ Impact Preview — "Auth System Feature" [trigger: complete]
○ queue: 3 items (will be completed)
◉ work: 1 item (active — will be force-completed)
◉ review: 1 item (active — will be force-completed)
✓ terminal: 2 items (already done — will be skipped)
◆ Impact Preview — "Auth System Feature" [trigger: cancel]
○ queue: 3 items (will be cancelled)
◉ work: 1 item (active — will be force-cancelled)
◉ review: 1 item (active — will be force-cancelled)
✓ terminal: 2 items (already done — will be skipped)
For itemIds path (no root item): call query_items(operation="get", itemId="<uuid>") on each item and build the same role-grouped preview table from the individual results. For large lists (10+ items), use query_items(operation="search") with filters instead of individual get calls.
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.
- 3d ago First seen · 288 lines · 67 tokens per session scan A 0bb051c9a4a5
batch-complete is a skill published in the GitHub repository jpicklyk/task-orchestrator (205 stars, last pushed 28d ago), licensed MIT. It adds 67 tokens to every session and 2,499 once invoked, about $0.0003 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.
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