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/keyword-research/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/keyword-research)<a href="https://agentmods.dev/skills/jwuthri/tracely-ai/keyword-research"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/keyword-research/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/keyword-research"><img src="https://agentmods.dev/badge/skills/jwuthri/tracely-ai/keyword-research.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 | $0.00020 | $0.00981 |
| Opus 5 | $0.00010 | $0.00491 |
| Sonnet 5 | $0.00004 | $0.00196 |
| Haiku 4.5 | $0.00002 | $0.00098 |
Grade A, and why
keyword-research 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 11d 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:
- keyword-research — 97% identical, 8 lines differ
- conqueror-keyword-research — 89% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenSEO Keyword Research
Goal
Turn seed topics into a prioritized keyword opportunity set using OpenSEO MCP data. The output should help the user decide what to target, what to save, and what to research next.
Required inputs
projectId- One or more seed topics, products, pages, competitors, or audience problems
- Optional market/location/language
If projectId is missing, use list_projects first. If the target market/location/language is unclear and would materially affect keyword metrics, ask the user; otherwise use the MCP tool defaults.
OpenSEO MCP tools
research_keywords: primary discovery tool. Use 1-5 seeds per call and prefer 150 results unless the user asks for exhaustive research.get_keyword_metrics: hydrate up to 700 known keywords with volume, keyword difficulty (KD), search intent, CPC, and monthly trends in one call. Use it to score candidate or known terms — including the Search Console striking-distance queries from step 1.get_ranked_keywords: pull exact ranking keyword rows when a target domain or page is part of the research brief.get_search_console_performance: when Search Console is connected, start from the project's real first-party demand — queries already earning impressions and near-ranking ("striking distance") terms. Request a highrowLimitand filter average position 5-20 client-side, since the API sorts by clicks and can't filter by position. Then hydrate those striking-distance queries withget_keyword_metricsto attach difficulty and intent.get_serp_results: inspect SERPs for the top candidate terms, especially when intent is ambiguous.search_local_businesses,get_local_serp_results, andget_google_business_questions: use for local SEO topics when a business/location radius matters.list_saved_keywords: avoid duplicating already-saved work or use existing tags as context.save_keywords: save selected keywords only after explicit user confirmation.
Workflow
- Normalize seeds into a small set of distinct research angles. If Search Console is connected for the project, first pull
get_search_console_performance(highrowLimit, default lookback), filter to striking-distance positions (~5–20) client-side, and hydrate those queries withget_keyword_metricsto attach KD and intent. That ranked, hydrated list is your fastest opportunity set — work it before broad discovery. - If the request is local SEO, identify the business, location/coordinates or service area, and local categories. Use
search_local_businessesandget_local_serp_resultsfor the most important location/keyword set instead of relying only on national keyword/SERP data. - Call
research_keywordsfor exploratory seeds. Use bulk calls when possible. - Use
get_keyword_metricsto hydrate a fixed keyword list — or the striking-distance queries from step 1 — with volume, KD, and intent before prioritizing. - Use
get_ranked_keywordswhen the user provides a domain/page and wants opportunities based on current rankings, near-misses, or competitor-owned terms. - Remove irrelevant, duplicate, branded-only, and off-intent terms.
- Prioritize by practical opportunity, not volume alone:
- Strong match to the user's product/page/topic
- Clear search intent
- Reasonable difficulty
- Useful volume/CPC signal
- SERP where the user can plausibly compete
- For local SEO, local-pack/Maps visibility and proximity fit
- Use
get_serp_resultsfor high-potential or ambiguous keywords when SERP intent would change the recommendation; keep the default check small. - Present a shortlist and a longer opportunity table.
- Ask before saving keywords. When saving, suggest concise tags such as
topic:<topic>,intent:<intent>, orpage:<slug>.
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.
- 11d ago First seen · 71 lines · 20 tokens per session scan A 1f742749a796
keyword-research is a skill published in the GitHub repository Jwuthri/Tracely-ai (1,404 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 981 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
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-validator
Validate local report.json files without publishing or modifying them.
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.