Tracely-ai: Skill for Claude Code

.claude/skills/keyword-research/SKILL.md

keyword-research is a skill for Claude Code from Jwuthri/Tracely-ai. It costs 20 tokens per session (981 once invoked), scanned A, original, MIT.

A keyword-research workflow for finding search terms people use, checking their search data and results pages, and saving promising terms.

In plain words
What is it for?
Use it to research keywords from topics, pages, competitors, or audience problems, then compare search volume, difficulty, intent, cost-per-click, and trends.
Why use it?
It helps turn a broad topic or product idea into a prioritized set of terms to consider for search-focused content.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is Jwuthri/Tracely-ai's own configuration. It tells Claude Code how to work on Tracely-ai itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Tracely-ai configures →

About the project

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.

Jwuthri/Tracely-ai · 1,404 stars · on GitHub · tracely-ai.com

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/Jwuthri/Tracely-ai/master/.claude/skills/keyword-research/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Jwuthri/Tracely-ai

Made for: Claude Code.

Wrote 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.

agentmods badge for keyword-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/jwuthri/tracely-ai/keyword-research/github.svg)](https://agentmods.dev/skills/jwuthri/tracely-ai/keyword-research)
Your own site
<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.

agentmods 80×15 button for keyword-research

Your own site · 80×15
<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>
Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 981 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash 1f742749a796, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

.claude/skills/keyword-research/SKILL.md · 71 lines

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 high rowLimit and 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 with get_keyword_metrics to 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, and get_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

  1. Normalize seeds into a small set of distinct research angles. If Search Console is connected for the project, first pull get_search_console_performance (high rowLimit, default lookback), filter to striking-distance positions (~5–20) client-side, and hydrate those queries with get_keyword_metrics to attach KD and intent. That ranked, hydrated list is your fastest opportunity set — work it before broad discovery.
  2. If the request is local SEO, identify the business, location/coordinates or service area, and local categories. Use search_local_businesses and get_local_serp_results for the most important location/keyword set instead of relying only on national keyword/SERP data.
  3. Call research_keywords for exploratory seeds. Use bulk calls when possible.
  4. Use get_keyword_metrics to hydrate a fixed keyword list — or the striking-distance queries from step 1 — with volume, KD, and intent before prioritizing.
  5. Use get_ranked_keywords when the user provides a domain/page and wants opportunities based on current rankings, near-misses, or competitor-owned terms.
  6. Remove irrelevant, duplicate, branded-only, and off-intent terms.
  7. 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
  8. Use get_serp_results for high-potential or ambiguous keywords when SERP intent would change the recommendation; keep the default check small.
  9. Present a shortlist and a longer opportunity table.
  10. Ask before saving keywords. When saving, suggest concise tags such as topic:<topic>, intent:<intent>, or page:<slug>.

Read the full file on GitHub · 71 lines

Changes

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.

  1. 11d ago First seen · 71 lines · 20 tokens per session scan A 1f742749a796

Subscribe to this mod's changes

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.