Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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Analyze Google Search Console exports or authorized API data to prioritize page improvements. Use for traffic changes, page/query opportunities, indexing evidence, and deciding when to leave recently edited or weakly supported pages alone.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Turn the user's own search evidence into a small, justified optimization batch. Default to read-only diagnosis. Execute changes only within the user's explicit authorization; existing approval for a concrete batch remains valid. An analysis request never authorizes publishing or spending.
Confirm the exact GSC property (domain property or URL prefix), target market, Web/other search type, and requested period. Verify the property returned by a connector matches the requested site before using any numbers. Do not choose the first accessible property. Infer from the user's site and supplied context when unambiguous; ask if multiple properties or markets remain plausible.
For ranking analysis use the user's target market, not a universal US default. Global performance totals may be useful separately; label them and never mix scopes in comparisons. If no market can be established, label aggregate findings and defer market-specific ranking claims.
Use the least-friction route already available:
Do not install packages/connectors or start OAuth unless needed and within the user's request. The CSV importer uses Python's standard library. API dependencies are optional and separate.
Read references/data-quality.md before drawing conclusions. Every run must record property, date range, search type, country/device/page filters, data source, and completeness limitations.
If query evidence is absent or too sparse, try a targeted page export/query or available UI. UI may also omit anonymized queries. If no stronger evidence is available, lower confidence and continue only with supported findings; never conclude that the page has no keywords.
Read references/roi-rubric.md when ranking opportunities. Prefer relevant business intent, a plausible small intervention, and adequate evidence. Call this an opportunity assessment, not measured ROI unless conversion and cost data are supplied.
Distinguish ranking gaps, snippet/intent mismatch, missing query coverage, and confirmed technical problems. Do not diagnose intent mismatch from CTR alone. Two pages appearing for a query do not by themselves prove cannibalization.
Check recent edits in supplied change notes or repository history. Usually observe for at least 7–14 complete post-change days before another overlapping edit; larger ranking effects may take longer. Do not promise a time-to-impact.
Recommend only as many pages as the evidence supports, often 1–5 and sometimes none. For each include URL, scope, clicks/impressions/CTR/position, supporting queries, diagnosis, proposed action, business relevance, confidence, and review interval. Explain important skips. Keep broad informational, irrelevant report, and very sparse pages out of the batch unless a strategic reason is explicit.
Lead with what changed and what to do next. State limitations that would change the decision. Use references/report-template.md to save a baseline when useful; analysis artifacts belong in the workspace and are not committed by default. Preserve private search data locally unless the user authorizes an external destination.
For approved edits, inspect the project's own instructions and content storage first. Follow existing code/CMS patterns and limit changes to approved pages. Back up changed CMS rows. Align copy with the page's actual functionality; add FAQ/schema only for visible supported content. Avoid mass year updates, keyword stuffing, speculative canonicals, or replacing useful conversion copy with generic SEO prose.
Verify the relevant rendered title, description, canonical, robots, schema, links, and visible content. Run the project's checks appropriate to the changes. Deploy only if authorized. Record changed URLs, edit/deployment dates, baseline, expected mechanism, and review interval. A later comparison can show association, not isolate the edit's causal effect.
Use scripts/import_gsc.py for local CSV/ZIP exports. The optional scripts/gsc_fetch.py requires scripts/requirements-api.txt and authorized Google access; setup is in the data-access reference. Python 3.9 or newer is required.
Run python3 -m unittest discover -s tests -v from this skill directory. tests/test_gsc.py checks parsing, scope, date windows, pagination and read-only request construction using synthetic fixtures. API responses are mocked; these tests do not prove live authorization or host compatibility.
Adapted from Tracetify Skills, MIT. See LICENSE.
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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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Pull the ads a competitor is running right now on Meta (Facebook/Instagram) and Google, then extract the hooks, offers, CTAs and landing pages worth modelling. Use when writing ad copy, planning creative tests, choosing a landing page angle, or when the user asks "what is X advertising", "what ads does X run", "find competitor ad hooks". Reads live Ad Library and Ads Transparency data through the Tracetify MCP; falls back to a manual Ad Library walkthrough when the connector is absent.
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Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
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Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
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Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
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Checks which private files (.env, keys, secrets) Claude Code and its subagents reached in this project, whether a value came into an agent's context, and which key to change, by running the open source agentwhy CLI over the session records Claude Code already keeps on disk. Use when the user asks whether the agent or a subagent read their .env or other secrets, which private files a session touched, whether a key leaked into the conversation, or which key to rotate. Runs locally and never prints a secret value.
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