Tracely is a CI/CD system for AI agents that turns failed production traces into replayable regression tests. Development teams use it to detect and group agent failures, run the resulting cases on pull requests, and block changes that reproduce those failures. The catalogue entries provide skills for operating this trace-based testing and observability workflow.
Borrowing it
Nothing to install: this file belongs to Jwuthri/Tracely-ai. 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/Jwuthri/Tracely-ai/master/.claude/skills/link-prospecting/SKILL.mdgit clone --depth 1 https://github.com/Jwuthri/Tracely-aiWrote 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/jwuthri/tracely-ai/link-prospecting)<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/link-prospecting"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/link-prospecting/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/jwuthri/tracely-ai/link-prospecting"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/link-prospecting.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 | $0.00023 | $0.00994 |
| Opus 5 | $0.00012 | $0.00497 |
| Sonnet 5 | $0.00005 | $0.00199 |
| Haiku 4.5 | $0.00002 | $0.00099 |
Grade A, and why
link-prospecting 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.
This is a copy
97% identical to link-prospecting — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO Link Prospecting
Goal
Find realistic pages, sites, and authors that might reference the user's page, product, study, guide, or tool. Use OpenSEO for prospect discovery, then use available web/search/browser tools for contact discovery.
Required inputs
projectId- User domain or target URL
- Linkable asset, page, product, study, tool, or topic
- Optional competitors
- Optional market/location/language
OpenSEO MCP tools
get_serp_results: find ranking articles, listicles, resource pages, comparisons, and topical publishers.get_backlinks_overview: inspect competitor domain or page backlink/referring-domain patterns.get_domain_overview: qualify important prospect domains.get_ranked_keywords: understand what a prospect or competitor ranks for when topical fit matters.search_local_businessesandget_local_serp_results: use for local SEO link prospecting when nearby businesses, local competitors, or Maps/category signals can reveal partnership targets.research_keywords: expand prospecting queries.
Contact discovery tools
After OpenSEO identifies good prospects, use available non-OpenSEO browsing or search tools for public contact discovery. Depending on the client, this may be web search, page fetches, browser automation, or a search API.
Look for:
- Author byline pages
- Contact pages
- Editorial guidelines
- About/team pages
- LinkedIn, X, Bluesky, or other professional profiles
- Newsletter or publication masthead pages
- Public email addresses in page HTML or visible page text
- Structured data such as
Person,Organization,sameAs, oremail
Only record contact details that were actually found. Include the source URL for any email, profile, or contact form.
Prospecting query patterns
Build queries from the asset/topic:
<topic> resourcesbest <category> tools<competitor> alternatives<topic> statistics<topic> guide<topic> examples<topic> templates<topic> software<topic> for <audience>
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 · 108 lines · 23 tokens per session scan A 5b4f99dbe1fc
link-prospecting is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,216 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 994 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to link-prospecting, differing in 9 lines, and is treated as a copy.
Other skills, from other repositories
Prompt Version Control Workflow
Sets up a prompt versioning system with naming conventions, diff tracking, A/B evaluation gates before promotion, and rollback triggers.
llm-tester
You are the LLM Tester, specializing in systematic prompt evaluation, red-teaming, and LLM output quality assurance. You replace "vibes-based" AI evaluation with rigorous, automated, and repeatable verification suites.
report-repair
Repair invalid local report.json files by inserting required report fields.
local-validator
Validate a local report.json file with a deterministic check-only script and no network access.
artifact-publisher
Validate and publish report artifacts to a remote release endpoint.
report-publisher
Publish an already validated report to an external release destination.