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/competitor-analysis/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/competitor-analysis)<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/competitor-analysis"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/competitor-analysis.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 5.1 | $0.00022 | $0.00901 |
| Opus 5 | $0.00011 | $0.00451 |
| Sonnet 5 | $0.00004 | $0.00180 |
| Haiku 4.5 | $0.00002 | $0.00090 |
Grade A, and why
competitor-analysis 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.
This is a copy
89% identical to competitor-analysis — 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO Competitor Analysis
Goal
Analyze one competitor deeply enough to decide what to learn from, avoid, counter-position against, or outrank.
Use this for a named competitor. For identifying the market leaders first, use competitive-landscape.
Required inputs
projectId- Competitor domain
- User's domain when comparison is requested
- Optional topic/category/location/language
OpenSEO MCP tools
get_domain_overview: baseline organic traffic and keyword count.get_search_console_performance: when comparing to the user's own domain and Search Console is connected, use it as the first-party baseline (real clicks/impressions/CTR/position) instead of estimating the user's own performance from third-party data.get_ranked_keywords: exact keyword, URL, rank, intent, traffic, CPC, and SERP-type rows for the competitor domain or page.get_backlinks_overview: backlink/referring-domain profile.find_serp_competitors: validate whether the named competitor is a real search competitor across the target keyword set.search_local_businesses,get_local_serp_results, andget_google_business_questions: use for local SEO competitors when Maps/local-pack visibility, nearby businesses, categories, or Google Q&A matter.get_serp_results: validate direct head-to-head SERPs for important keywords.research_keywords: expand gaps or category terms when needed.
Workflow
- Call
get_domain_overviewfor the competitor, passing provided location/language when supported. - If comparing to the user, call
get_domain_overviewfor the user's domain too — and if Search Console is connected,get_search_console_performancefor the user's real baseline. - Call
get_ranked_keywordsfor the competitor. Use filters likemaxRank,minSearchVolume,excludeBrandTerms, andresultTypesto keep rows relevant. - If comparing to the user, call
get_ranked_keywordsfor the user's domain/page too, or useget_serp_resultsfor the shared terms when a lighter check is enough. - For local SEO, use
search_local_businessesandget_local_serp_resultsaround the relevant business location(s) before drawing local-pack conclusions. Addget_google_business_questionsonly when Q&A evidence matters. - Use
find_serp_competitorswhen the competitor was supplied by the user but its search overlap is unclear. - Group competitor keywords into themes:
- Product/category terms
- Alternatives/comparisons
- Templates/tools/calculators
- Educational guides
- Branded demand
- Local/neighborhood terms when relevant
- Call
get_backlinks_overviewfor the competitor, especially if authority appears to explain rankings. Continue without backlink evidence if it is unavailable. - Use
get_serp_resultsfor important shared or target keywords to compare positioning, passing provided location/language when supported. - Produce an actionable plan:
- What they are doing well
- Where they are vulnerable
- Which pages/keywords to pursue
- What to avoid copying
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 · 84 lines · 22 tokens per session scan A 66db5609ef03
competitor-analysis is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,193 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 901 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to competitor-analysis, 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-publisher
Publish an already validated report to an external release destination.
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.