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/boshi-xixixi/traeskill/breakdown-plannpx skills add boshi-xixixi/TraeSkill --skill breakdown-plangit clone --depth 1 https://github.com/boshi-xixixi/TraeSkillWrote 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/boshi-xixixi/traeskill/breakdown-plan)<a href="https://agentmods.dev/skills/boshi-xixixi/traeskill/breakdown-plan"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/breakdown-plan.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.03571 |
| Opus 5 | $0.00017 | $0.01785 |
| Sonnet 5 | $0.00007 | $0.00714 |
| Haiku 4.5 | $0.00003 | $0.00357 |
Grade A, and why
breakdown-plan 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- breakdown-plan — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 510 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Issue Planning & Project Automation Prompt
Goal
Act as a senior Project Manager and DevOps specialist with expertise in Agile methodology and GitHub project management. Your task is to take the complete set of feature artifacts (PRD, UX design, technical breakdown, testing plan) and generate a comprehensive GitHub project plan with automated issue creation, dependency linking, priority assignment, and Kanban-style tracking.
GitHub Project Management Best Practices
Agile Work Item Hierarchy
- Epic: Large business capability spanning multiple features (milestone level)
- Feature: Deliverable user-facing functionality within an epic
- Story: User-focused requirement that delivers value independently
- Enabler: Technical infrastructure or architectural work supporting stories
- Test: Quality assurance work for validating stories and enablers
- Task: Implementation-level work breakdown for stories/enablers
Project Management Principles
- INVEST Criteria: Independent, Negotiable, Valuable, Estimable, Small, Testable
- Definition of Ready: Clear acceptance criteria before work begins
- Definition of Done: Quality gates and completion criteria
- Dependency Management: Clear blocking relationships and critical path identification
- Value-Based Prioritization: Business value vs. effort matrix for decision making
Input Requirements
Before using this prompt, ensure you have the complete testing workflow artifacts:
Core Feature Documents
- Feature PRD:
/docs/ways-of-work/plan/{epic-name}/{feature-name}.md - Technical Breakdown:
/docs/ways-of-work/plan/{epic-name}/{feature-name}/technical-breakdown.md - Implementation Plan:
/docs/ways-of-work/plan/{epic-name}/{feature-name}/implementation-plan.md
Related Planning Prompts
- Test Planning: Use
plan-testprompt for comprehensive test strategy, quality assurance planning, and test issue creation - Architecture Planning: Use
plan-epic-archprompt for system architecture and technical design - Feature Planning: Use
plan-feature-prdprompt for detailed feature requirements and specifications
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.
- 5d ago First seen · 510 lines · 35 tokens per session scan A ce46dc0485b0
breakdown-plan is a skill published in the GitHub repository boshi-xixixi/TraeSkill (261 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 3,571 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.
Other skills, from other repositories
mlops-automation
Automate an MLOps project with mise tasks, lefthook hooks, Docker images, GitHub Actions, and MLflow tracking on a SQL backend. Use when adding a task runner, git hooks, CI/CD, or experiment tracking to a working package.
mlops-prototyping
Structure reproducible Jupyter notebooks with a fixed section layout, hoisted configuration, and leakage-free scikit-learn pipelines. Use when exploring a dataset, training a first model, or preparing a notebook for promotion.
mlops-observability
Make an ML system a glass box with reproducible runs, MLflow dataset lineage, drift monitoring, alerting, and SHAP explanations. Use when a deployed model needs traceability, monitoring, alerting, or explanation.
mlops-industrialization
Convert notebook prototypes into a distributable Python package with a src layout, a domain/io/application split, and validated OmegaConf plus Pydantic configuration. Use when moving code out of notebooks or designing entrypoints.
mlops-initialization
Initialize a production-ready Python MLOps project with uv, git, mise, and a shared editor setup. Use when starting a new repository, writing its first pyproject.toml, or repairing an inconsistent project skeleton.
<skill-name>
当用户 时使用。典型信号：用户说" "、 。涉及关键词： 、 。只写"何时触发"，不写工作流；第三人称、中文。上限 1024 字符.