Borrowing it
Nothing to install: this file belongs to mikeOnBreeze/cc-crossbeam. 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/mikeOnBreeze/cc-crossbeam/main/.claude/skills/long-running-agent/SKILL.mdgit clone --depth 1 https://github.com/mikeOnBreeze/cc-crossbeamWrote 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/mikeonbreeze/cc-crossbeam/long-running-agent)<a href="https://agentmods.dev/skills/mikeonbreeze/cc-crossbeam/long-running-agent"><img src="https://agentmods.dev/badge/skills/mikeonbreeze/cc-crossbeam/long-running-agent/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/mikeonbreeze/cc-crossbeam/long-running-agent"><img src="https://agentmods.dev/badge/skills/mikeonbreeze/cc-crossbeam/long-running-agent.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 | $0.00044 | $0.00923 |
| Opus 5 | $0.00022 | $0.00462 |
| Sonnet 5 | $0.00009 | $0.00185 |
| Haiku 4.5 | $0.00004 | $0.00092 |
Grade A, and why
long-running-agent 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 10d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Long-Running Agent Setup
Convert specs and planning documents into phase-based task structures for autonomous multi-session execution.
When to Use This Skill
- User has a spec or planning doc to execute with a long-running agent
- User wants to break down a project into phases with verification checkpoints
- User wants to create a
claude-prompt.mdfor autonomous task execution
Source of Truth
Before doing anything, fetch and read this blog post for the core patterns: https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents
This blog from Anthropic's engineering team defines the effective patterns. Apply them directly.
Core Workflow
1. Gather Inputs
Request from the user:
- Spec/Planning Doc: The document to convert (required)
- Project Name: Short identifier
- Output Location: Where to create files
2. Read the Blog
Fetch the Anthropic blog post above. The key patterns are:
- Phase-based work with verification checkpoints
- Explicit feature/task enumeration (granular, testable items)
- Task file as progress tracker
- Git checkpointing after each task
- Strong constraints (no test deletion, no skipping phases)
3. Decompose into Phases and Tasks
Break the spec into:
Phases (3-8 typically):
- Logical groupings of related work
- Each phase has verification steps to test completion
- Agent completes ALL tasks in a phase, then stops for user verification
Tasks (per phase):
- Granular, implementable units
- Each task has specific steps
- Uses
passes: true/falseto track completion
Example structure from a real project:
Phase 1: Project Foundation (3 tasks)
- setup-001: Initialize project structure
- setup-002: Create environment config
- types-001: Define core interfaces
Verification: "Run npx tsc --noEmit, confirm no errors"
Phase 2: Storage & Skills (5 tasks)
- storage-001: Create storage interface
- skill-001 to skill-004: Create skill files
Verification: "Confirm skills load, storage works"
... etc
What ships with it
2 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.
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.
- 10d ago First seen · 127 lines · 44 tokens per session scan A 066c0f805649
long-running-agent is a skill published in the GitHub repository mikeOnBreeze/cc-crossbeam (291 stars, last pushed 6mo ago), licensed MIT. It adds 44 tokens to every session and 923 once invoked, about $0.0002 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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trellis-plan-review
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実装計画レビューゲート
A gate that reviews an implementation plan before an AI agent acts on it. It looks for dangerous operations, forbidden areas, work outside the stated scope, and steps that require human approval.
planmaxx
Use when an agent-written plan, design, or spec needs user review before implementation, or when the user asks to review, approve, or iterate on a plan.
planmaxx
Use when an agent-written plan, design, or spec needs user review before implementation, or when the user asks to review, approve, or iterate on a plan.