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/competitive-landscape/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/competitive-landscape)<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/competitive-landscape"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/competitive-landscape/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/competitive-landscape"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/competitive-landscape.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00883 |
| Opus 5 | $0.00011 | $0.00441 |
| Sonnet 5 | $0.00004 | $0.00177 |
| Haiku 4.5 | $0.00002 | $0.00088 |
Grade A, and why
competitive-landscape 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 10d 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
2 near-identical copies found in the catalogue:
- competitive-landscape — 95% identical, 6 lines differ
- conqueror-competitive-landscape — 88% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO Competitive Landscape
Goal
Answer: "Who is winning this SEO market, what content is working for them, and where are the openings?"
Use this when the user wants a market-level view across several competitors. For a deep dive on one domain, use competitor-analysis.
Required inputs
projectId- Topic, seed keywords, market/category, or user's domain
- Optional known competitors
- Optional location/language
OpenSEO MCP tools
research_keywords: discover representative market queries.get_keyword_metrics: validate known query sets with volume, difficulty, intent, and trends.get_serp_results: identify recurring ranking domains across target queries.find_serp_competitors: compare domains competing across supplied keywords; use this before manual SERP counting when a keyword set is available.get_domain_overview: size organic footprint for candidate leaders.get_search_console_performance: when the user's own domain is in the comparison and Search Console is connected, anchor their position with first-party clicks/impressions/CTR rather than third-party estimates.get_ranked_keywords: find exact ranking keywords, URLs, ranks, intents, and SERP result types for leaders.get_backlinks_overview: compare backlink/referring-domain strength where relevant.search_local_businesses,get_local_serp_results, andget_google_business_questions: use for local SEO markets where proximity, Maps rankings, business categories, reviews, or Google Q&A affect who is winning.
Workflow
- Define the market query set:
- Use provided keywords, or call
research_keywordsto build 5-10 representative queries. - Include mixed intent: informational, commercial, comparison, and tool/software terms when applicable.
- For local SEO, include neighborhood/city/service-area queries and identify the priority locations or coordinates.
- Use provided keywords, or call
- If the query set is already known, use
get_keyword_metricsto validate relative demand and difficulty andfind_serp_competitorsto identify recurring domains at scale. - For local SEO, call
search_local_businessesandget_local_serp_resultsfor the highest-priority location(s) before synthesizing winners. Useget_serp_resultsas a complement for organic pages, not as the only local evidence. - Call
get_serp_resultsfor representative queries when live SERP composition, ranking URLs, or SERP features need inspection. Send at most 10 queries per call. - Identify recurring domains and group them by type:
- Direct product competitors
- Publishers/media
- Marketplaces/directories
- Communities/forums
- Documentation/resources
- For the strongest recurring domains, call
get_domain_overview; default to the top 3-5 domains before expanding. - For direct competitors and relevant publishers, call
get_ranked_keywords. - Use
get_backlinks_overviewwhen backlink authority appears important or the user asks why a domain is winning. Backlinks may be unavailable if the account has not enabled that data; continue with SERP/domain evidence if it fails. - Synthesize patterns: content types, themes, SERP formats, local-pack signals, authority advantages, and underserved angles.
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.
- 10d ago First seen · 81 lines · 22 tokens per session scan A e13aed0245e0
competitive-landscape is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,221 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 883 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 2026-08-30.
Other skills, from other repositories
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.
create-task
Create a new Harbor task for evaluating agents. Use when the user wants to scaffold, build, or design a new task, benchmark problem, or eval. Guides through instruction writing, environment setup, verifier design (pytest vs Reward Kit vs custom), and solution scripting.
ci
Configure Ginkgo for continuous integration — the recommended CLI flag set and the rationale for each flag (-r -p --randomize-all --randomize-suites --fail-on-pending --fail-on-empty --keep-going --cover --race --trace --json-report --timeout --poll-progress-after/-interval), invoking via go run to pin the CLI to…
playwright-ci
Production-ready CI/CD configurations for Playwright — GitHub Actions, GitLab CI, CircleCI, Azure DevOps, Jenkins, Docker, parallel sharding, reporting, code coverage, and global setup/teardown.
playwright-testing
E2E testing with Playwright - Page Objects, cross-browser, CI/CD.
ci-maintenance-workflow
CI and GitHub Actions maintenance workflows — fix a failing test from a CI URL, fix a failing smoke test, add @pytest.mark.slow markers to slow tests, or review a PR against agent-checkable standards. Use when user asks to fix a failing test, fix a smoke test, mark slow tests, or review a PR. Trigger when the user…