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 skills add boshi-xixixi/TraeSkill --skill finalize-agent-promptgit 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/finalize-agent-prompt)<a href="https://agentmods.dev/skills/boshi-xixixi/traeskill/finalize-agent-prompt"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/finalize-agent-prompt/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/boshi-xixixi/traeskill/finalize-agent-prompt"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/finalize-agent-prompt.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 | $0.00025 | $0.00183 |
| Opus 5 | $0.00013 | $0.00092 |
| Sonnet 5 | $0.00005 | $0.00037 |
| Haiku 4.5 | $0.00003 | $0.00018 |
Grade A, and why
finalize-agent-prompt 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 9d 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:
- finalize-agent-prompt — 100% identical, 0 lines differ
What it actually says
Finalize Agent Prompt
Current Role
You are an AI agent who knows what works best for the prompt files you have seen and the feedback you have received. Apply that experience to refine the current prompt so it aligns with proven best practices.
Requirements
- A prompt file must be provided. If none accompanies the request, ask for the file before proceeding.
- Maintain the prompt’s front matter, encoding, and markdown structure while making improvements.
Goal
- Read the prompt file carefully and refine its structure, wording, and organization to match the successful patterns you have observed.
- Check for spelling, grammar, or clarity issues and correct them without changing the original intent of the instructions.
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.
- 9d ago First seen · 27 lines · 25 tokens per session scan A 4b9b7f83671e
finalize-agent-prompt is a skill published in the GitHub repository boshi-xixixi/TraeSkill (263 stars, last pushed 4mo ago), licensed MIT. It adds 25 tokens to every session and 183 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
<skill-name>
A template for defining a coding-agent skill, including its title, trigger situations, overview, workflow, common mistakes, and optional references.
spec-writing
A method for writing a software specification: a document that records decisions, reasons, boundaries, and ways to judge whether implementation succeeded. It first checks whether important unknowns require user clarification or technical research.
onboarding-unknown-codebase
A method for quickly understanding an unfamiliar codebase, meaning a software project whose structure and behavior you do not yet know. It builds a project map by examining overview files, directories, and one main execution path.
commit-message
A guide for writing clear, traceable Git commit messages using the Conventional Commits format, which labels changes such as features, bug fixes, documentation, and refactoring.
clarifying-questions
Guidance for clarifying vague or assumption-heavy requests before making changes.
debugging
A systematic method for finding the underlying cause of a software bug by observing the failure, forming a hypothesis, and testing it.