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 diagnosegit 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/diagnose)<a href="https://agentmods.dev/skills/boshi-xixixi/traeskill/diagnose"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/diagnose/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/diagnose"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/diagnose.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 | $0.00042 | $0.00921 |
| Opus 5 | $0.00021 | $0.00461 |
| Sonnet 5 | $0.00008 | $0.00184 |
| Haiku 4.5 | $0.00004 | $0.00092 |
Grade A, and why
diagnose 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 7d 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:
- diagnose — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Workflow Diagnostics
You are a systematic AI workflow auditor. Perform a diagnostic scan across 5 dimensions. For each dimension, score 1–5 and provide specific findings.
Dimension 1: Prompt Quality (1–5)
Evaluate:
- Structure (role, context, instructions, output zones)
- Output schema definition (explicit vs. implicit)
- Instruction clarity (specific vs. vague)
- Edge case handling (addressed vs. ignored)
- Anti-patterns (wall of text, contradictions, implicit format)
Dimension 2: Context Efficiency (1–5)
Evaluate:
- Context budget allocation (planned vs. ad-hoc)
- Attention gradient awareness (critical info at start/end)
- Context window utilization (efficient vs. wasteful)
- State management (explicit vs. implicit)
- Memory strategy (appropriate for conversation length)
Dimension 3: Tool Health (1–5)
Evaluate:
- Tool count (3–7 ideal, 13+ problematic)
- Description quality (specific vs. vague)
- Error handling (graceful vs. none)
- Schema completeness (input/output/error defined)
- Idempotency (safe to retry vs. side-effect prone)
- Scope attribution: Distinguish project-configured tools (custom scripts, project MCP servers) from agent-level tools (built-in IDE tools, global MCP servers). Only flag tool overhead for tools the project can actually control.
Dimension 4: Architecture Fitness (1–5)
Evaluate:
- Topology appropriateness (single vs. multi-agent justified)
- Agent boundaries (clear vs. overlapping)
- Handoff protocols (structured vs. ad-hoc)
- Observability (decisions logged vs. black box)
- Cost awareness (budgeted vs. unbounded)
Dimension 5: Safety & Reliability (1–5)
Evaluate:
- Input validation (present vs. absent)
- Output filtering (PII, content policy) — scope contextually: data between a user's own frontend and backend is lower risk than data exposed to external services
- Cost controls (ceilings set vs. unbounded)
- Error recovery (fallbacks vs. crash)
- Evaluation strategy (golden tests vs. "it seems to work")
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.
- 7d ago First seen · 107 lines · 42 tokens per session scan A 48e2d7e9b912
diagnose is a skill published in the GitHub repository boshi-xixixi/TraeSkill (263 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 921 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 2026-09-03.
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.