agentleFS
loglylogly/mureo/skills/competitive-scan/SKILL.md

competitive-scan

Skill64 starsChanged 7 months agoApache-2.0View on GitHub

Scan competitor activity using auction insights and market signals. Use when the user asks about competitors, market dynamics, impression share changes, competitor moves, or competitive positioning. Also use when the user asks in Japanese (競合の動きを調べて / 競合分析 / インプレッションシェアの変化を確認 / 競合にシェアを取られていないか).

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Before you start: Run the Diagnostic preamble from ../_mureo-shared/SKILL.md — load learning insights (mureo_learning_insights_get) and …

    line 21
  2. Very specific:

    Load context: Read STRATEGY.md (Market Context, USP, Data Sources) and STATE.json.

    line 24
  3. Distinctive:

    Noise guard: Look at trends over 4+ weeks, not week-to-week changes.

    line 47
  4. Distinctive:

    Ask for my approval: before updating any files or making changes.

    line 54

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Competitive Scan

PREREQUISITE: Read ../_mureo-shared/SKILL.md for auth, security rules, output format, and Tool Selection (Read/Write on Code, mureo_strategy_* / mureo_state_* MCP on Desktop / Cowork).

Tool names below refer to the built-in platforms; a namespaced variant (see _mureo-shared → Namespaced built-in variants) is used with its prefix substituted.

Scan the competitive landscape and suggest strategic responses across all channels.

Prerequisites

  • STRATEGY.md and STATE.json must exist (run the onboard skill first)

Steps

Before you start: Run the Diagnostic preamble from ../_mureo-shared/SKILL.md — load learning insights (mureo_learning_insights_get) and consult advisors (mureo_consult_advisor) before drawing conclusions.

  1. Load context: Read STRATEGY.md (Market Context, USP, Data Sources) and STATE.json.
  2. Discover platforms: Identify all configured platforms from STATE.json platforms. Also include any hosted official-MCP connector present in the session (e.g. TikTok, key tiktok_ads) where it exposes competitive/auction data — drive it via its own tools and skip mureo-only value-adds; see ../_mureo-shared/SKILL.md → Hosted-connector platforms.
  3. Paid competitive analysis: For each ad platform that provides competitive/auction data:
    • Google Ads: prefer mureo native — call google_ads_auction_insights_get (raw impression-share / overlap rows) and google_ads_auction_insights_analyze (rule-based summary), then google_ads_cpc_detect_trend (CPC drift) and google_ads_device_analyze (device-level breakdown). BYOD limitation: Apps Script does not expose auction_insight_domain (GAQL) — auction insight tools return [] in BYOD mode. The remaining tools (CPC trend, device breakdown) work in BYOD against the bundle. If mureo's Google Ads tools are unavailable (e.g. MUREO_DISABLE_GOOGLE_ADS=1 after mureo providers add google-ads-official), fall back to the official google-ads-official MCP for auction insights and per-device performance queries (the official MCP exposes the GAQL surface, so raw auction-insights rows are obtainable when not blocked by the Apps Script limitation), then skip the mureo-only analysis layers (google_ads_auction_insights_analyze, google_ads_cpc_detect_trend, google_ads_device_analyze); perform the rule-based summary / CPC trend regression / device CPA-gap analysis yourself from the raw rows, and note: "mureo's automated competitive-analysis layers (rule-based auction summary, CPC trend regression, device CPA-gap detection) require the native MCP — install via mureo setup claude-code for full coverage."
    • Meta Ads: No direct competitor-share API on either side. Prefer mureo native — surface meta_ads_analysis_placements and meta_ads_analysis_cost to detect placement-level cost shifts that indicate competitive pressure. If mureo's Meta Ads tools are unavailable, fall back to the official meta-ads-official hosted MCP's insights breakdown (e.g. by publisher_platform / platform_position) for the raw placement numbers, then skip the mureo-only analysis tools and infer placement-level competitive pressure yourself from cost-per-result trends; note that the mureo-specific placement and cost-trend analyses are not available in the fallback path.
    • mureo BYOD data is centralized in the workspace byod/ directory (or ~/.mureo/byod/ for legacy CLI users) and is only accessible through mureo MCP tools — do not look for raw CSVs in the project directory.
    • Compare impression share, overlap rate, CPC trends, and device landscape across the returned data.
  4. Organic competitive landscape (if Search Console is available):
    • Compare organic search performance trends (impressions, clicks, CTR, average position)
    • Identify queries where organic position is declining (potential competitor content gains)
    • Cross-reference paid auction competitors with organic ranking competitors
    • Present a unified paid + organic competitive picture
  5. On-site competitive signals (if GA4 is available): Check referral traffic and direct/brand traffic trends. Declining brand search or direct traffic may indicate competitors capturing mind share.
  6. Compare with Market Context from STRATEGY.md:
    • Are known competitors gaining share?
    • Are new competitors appearing?
    • Has the competitive landscape changed since STRATEGY.md was last updated?
  7. Noise guard: Look at trends over 4+ weeks, not week-to-week changes. A single week's impression share dip is not a competitive threat — it may be seasonality or auction noise. Only flag competitive changes that are consistent over multiple weeks.
  8. Strategic recommendations: Based on findings and USP, suggest responses across all channels:
    • Paid adjustments (bid, budget, targeting) per platform
    • Organic content gaps to address (from Search Console data)
    • Cross-channel coordination opportunities
  9. Ask for my approval before updating any files or making changes.
  10. Update STRATEGY.md Market Context section if approved.
  11. Update STATE.json with competitive insights in campaign notes.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.