Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add TrueClicks/claude-plugins --skill skill-136-geographic-competitive-intelligencegit clone --depth 1 https://github.com/TrueClicks/claude-pluginsWrote 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/trueclicks/claude-plugins/skill-136-geographic-competitive-intelligence)<a href="https://agentmods.dev/skills/trueclicks/claude-plugins/skill-136-geographic-competitive-intelligence"><img src="https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-136-geographic-competitive-intelligence/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/trueclicks/claude-plugins/skill-136-geographic-competitive-intelligence"><img src="https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-136-geographic-competitive-intelligence.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 | $0.00025 | $0.00648 |
| Opus 5.5 | $0.00010 | $0.00259 |
| Sonnet 5.5 | $0.00005 | $0.00130 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
skill-136-geographic-competitive-intelligence 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 21d 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.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill 136: Geographic Competitive Intelligence
Purpose
Analyze competitive dynamics by geography to identify regions with strong positioning (defend), underserved markets (expand), and areas with poor ROI (evaluate). Geographic insights enable location-based bid adjustments and budget allocation.
Data Requirements
Data Source: Custom GAQL Required
Geographic performance data requires custom GAQL queries.
GAQL Query:
SELECT
campaign.name,
geographic_view.country_criterion_id,
segments.geo_target_region,
metrics.impressions,
metrics.clicks,
metrics.cost_micros,
metrics.conversions
FROM geographic_view
WHERE segments.date DURING LAST_30_DAYS
AND metrics.impressions > 0
Run via /google-ads:get-custom with query name geo_performance.
Analysis Steps
- Fetch geographic data: Run GAQL query for location-level metrics
- Map performance by region: Calculate IS, CPA, and conversion rate per location
- Identify competition patterns: Low IS indicates high competition; high IS suggests dominance
- Create strategy matrix: Categorize regions (Defend, Expand, Optimize, Evaluate)
- Recommend bid adjustments: Suggest location modifiers based on efficiency
Thresholds
| Condition | Severity |
|---|---|
| High-converting region with IS < 40% | Critical |
| Region with CPA > 2x account average | Warning |
| Strong IS region with declining conversion rate | Warning |
| Low IS + low conversion rate region | Info |
Output
Short format (default):
## Geographic Competitive Audit
**Account:** [Name] | **Regions Analyzed:** [X] | **Opportunities:** [Y]
### Geographic Performance Overview
| Region | Impressions | IS | Conv Rate | CPA | Strategy |
|--------|-------------|-----|-----------|-----|----------|
| California | [X] | [Y]% | [Z]% | $[A] | Expand |
| Texas | [X] | [Y]% | [Z]% | $[A] | Defend |
| New York | [X] | [Y]% | [Z]% | $[A] | Optimize |
### Expansion Opportunities (Low IS + Good Performance)
- **[Region]**: [X]% IS, [Y]% conv rate → Increase bids +[Z]%
### Defensive Priorities (High IS + Good Performance)
- **[Region]**: Maintain current strategy
### Optimization Needed (High IS + Poor Performance)
- **[Region]**: [X]% IS but poor CPA → Improve targeting/copy
### Recommendations
1. Increase bid modifier +[X]% for [Region]
2. Reduce spend in [Region] (poor ROI)
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.
- 21d ago First seen · 85 lines · 25 tokens per session scan A 26e351dbc5a9
skill-136-geographic-competitive-intelligence is a skill published in the GitHub repository TrueClicks/claude-plugins (2 stars, last pushed 22d ago), licensed MIT. It adds 25 tokens to every session and 648 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-08.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…