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/hayatelin/tracehunt/skillnpx skills add Hayatelin/tracehunt --skill skillgit clone --depth 1 https://github.com/Hayatelin/tracehuntWrote 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/hayatelin/tracehunt/skill)<a href="https://agentmods.dev/skills/hayatelin/tracehunt/skill"><img src="https://agentmods.dev/badge/skills/hayatelin/tracehunt/skill.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 | $0.00066 | $0.00350 |
| Opus 5 | $0.00033 | $0.00175 |
| Sonnet 5 | $0.00013 | $0.00070 |
| Haiku 4.5 | $0.00007 | $0.00035 |
Grade A, and why
tracehunt 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 3d 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.
What it actually says
tracehunt — OSINT username recon skill
When to use
The user asks to: look up / investigate a username or handle, find where an account exists, assess an online footprint, or gather OSINT on a handle for authorized security research.
⚠️ Only for accounts/targets the user is authorized to investigate. If intent looks like stalking or harassment, decline.
How to run
Preferred — via the MCP tool (see ../mcp/README.md): call hunt_username.
Or via the CLI:
python -m tracehunt <username> --summary # terminal summary
python -m tracehunt <username> --html report.html # shareable HTML report
python -m tracehunt <username> --site GitHub --site Reddit
How to interpret
foundlists the platforms where the handle exists (with URLs).footprint_score(0–100) summarizes how exposed the handle is.- A high score across many platforms suggests a well-established online identity.
Report back
Give the user the count found, the notable platforms, and the footprint score. Offer the HTML report for sharing. Remind them results are heuristic (a site can return false positives behind a WAF).
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.
- 3d ago First seen · 40 lines · 66 tokens per session scan A 445f6c0b864a
tracehunt is a skill published in the GitHub repository Hayatelin/tracehunt (0 stars, last pushed 2mo ago), licensed MIT. It adds 66 tokens to every session and 350 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.
Other skills, from other repositories
skill-creator
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
watch-shape
通过对话帮用户设计和迭代 Watch 的完整认知结构。当用户想创建新 Watch、深化现有 Watch 的 intent、或调整监控策略时使用。.
run-watch
执行某个 Watch 的完整情报采集与分析流程。当用户要求运行、更新、检查某个 Watch，或者要求获取某个方向的最新情报时使用。.
identity-shape
通过对话帮用户塑造和迭代 identity.md。当用户想完善个人画像、调整信息偏好、或 identity.md 还是空模板时使用。.
setup
初始化 Signex 项目。创建用户数据目录和模板文件。幂等执行，不会覆盖已有内容。.
update-memory
更新 Watch 的 memory.md 文件。当用户对报告或分析给出反馈时使用，将新反馈整合到现有记忆中。.