task-queue

A queue manager for multiple related tasks. It organizes them under one parent outcome, runs them in order, and checks the combined result at the end.

In plain words
What is it for?
Use it for multi-step work such as deployments or batches of dependent tasks that need ordered execution and a final integration check.
Why use it?
It helps coordinate work that has several dependent or independently checked parts. It also defines when to stop and how to resume unfinished child tasks.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/ssbun/csl-agent-kit/task-queue
Any agent
npx skills add SSBun/csl-agent-kit --skill task-queue
Clone the repo
git clone --depth 1 https://github.com/SSBun/csl-agent-kit

Made for: Claude Code, Codex.

Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 990 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00067 $0.00990
Opus 5 $0.00034 $0.00495
Sonnet 5 $0.00013 $0.00198
Haiku 4.5 $0.00007 $0.00099

Measured 2d ago against content hash 59c25fd5ca05, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

task-queue 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.

skills/meta/task-queue/SKILL.md · 50 lines

How it starts

The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Task Queue

Coordinate multiple canonical tasks through the current host Agent. This is host-native interactive execution: use the host's existing tools and subagents, never nested codex exec, pi --print, pi-worker-*, a daemon, watchdog, or unattended supervisor.

Required Runtime Protocol

Resolve the collection root as the parent of this skill directory. Before forming, presenting, or accepting a Task Target, read <collection-root>/csl-tasks/shared/protocols/task-target-alignment.md in full and treat it as the authoritative detailed alignment contract. Read it again after resume or compaction when it is no longer present in context. If it is unavailable, stop before substantive work and report the missing runtime dependency.

This skill owns Queue-parent activation, the integration Target meaning, permitted lifecycle writes, and the post-alignment Queue workflow. The shared protocol owns all Task Target alignment semantics. Use node <collection-root>/csl-tasks/shared/scripts/csl-tasks.js --workspace <workspace> ... as the only task-state core.

Build the Task Graph

  1. Load Project Core, then read only the newest task index entries and plausible candidate owning records needed to avoid duplicate ownership.
  2. As soon as the request establishes a concrete multi-task outcome, create or resume one Queue parent with an initial integration Target: create <parent-id> --title <title> --kind queue --target "T1: <integrated outcome>". Resume the parent and call the host's task-focus mechanism when available: emit the real task_focus(<parent-id>) tool call itself—not merely plan it—in the same reply as the resume, and disclose any binding failure. If no honest integration Target can yet be stated, ask one focused question and create the parent immediately after the answer.
  3. Apply the shared Task Target Alignment Protocol to the active parent. For this workflow, the conversational Target means the parent integration outcome and observable completion condition; before alignment, the permitted lifecycle writes are create, resume, focus, sync, and check. The gate follows activation and precedes task-direct source inspection, graph decomposition, or substantive preparation; only the protocol's focused target-forming clarification may occur within it.
  4. After the protocol aligns the current Target, query task-relevant Context Packs, read relevant lessons and directly relevant sources, then clarify only user-owned decisions that block a correct decomposition.
  5. Refine the parent's integration Targets and create a child only for an independently acceptable or independently blocked outcome. Each child has its own observable Targets and complete task lifecycle.
  6. Link children in execution order with link <parent-id> <child-id>. The core maintains reciprocal Parent/Children records, rejects multiple parents and cycles, and preserves order.
  7. Add the parent's current Scope and Plan, then sync and check every touched record.

Read the full file on GitHub · 50 lines

Files

What ships with it

3 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.

Changes

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.

  1. 2d ago First seen · 50 lines · 67 tokens per session scan A 59c25fd5ca05

Subscribe to this mod's changes

task-queue is a skill published in the GitHub repository SSBun/csl-agent-kit (10 stars, last pushed 3d ago), licensed MIT. It adds 67 tokens to every session and 990 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens