Topic Discovery Skill (Layer 0)

DATA LAYER RULING (2026-06-12). Every mcp__semrush__<kebab-name> tool named below is FICTIONAL (never existed on the live server), and DataForSEO is retired. Before making any data call, read ../keyword-research-pipeline/references/semrush-data-layer.md — it maps this layer's old calls to the real Semrush MCP pattern (get_report_schema -> execute_report). The logic below (filters, thresholds, output schema) remains binding; only the data calls changed.

Take brand-config.md and produce two artefacts that downstream layers consume as enrichment:

1. content-pipeline/0-keywords/topic-graph.json — the Semrush Topic Research idea-cluster tree rooted at the brand's category. Layer 1a (/seed-modifier-prompt) reads the top 5 cluster names so the seed agent grounds its output in Semrush's actual topic graph rather than brand-config alone. Layer 5 (/keyword-prioritization) reads cluster-level metrics to compute the cluster_authority_gap boost.
2. content-pipeline/0-keywords/trends.md — a short markdown summary of category-level trending queries, momentum direction, and competitor traffic shifts pulled from Semrush .Trends. Layer 4 (/keyword-redteam) reads this to ground its "is this keyword trending up or down today?" critique.

This layer is enrichment, not a gate. If Semrush returns nothing useful, downstream layers fall through to brand-config-only behavior. Layer 0 must never block the pipeline.

Input

/topic-discovery [--regen]

Reads:

• brand-config.md — brand category positioning, brand domain
• content-pipeline/0-keywords/topic-graph.json — previous output (for change detection)

--regen forces a fresh run even if the brand-config hash hasn't changed.

Process

1. Compute brand-config hash. SHA-256 of brand-config.md content. Compare against topic-graph.json's brand_snapshot field if it exists. If unchanged AND --regen not passed, exit cleanly with a one-line message saying "topic graph is current, skipping" — Layer 0 stays idempotent (mirrors the pattern in seed-modifier-prompt/SKILL.md step 1).

2. Read brand-config. Extract:

   • Brand category root — the noun-phrase that defines the brand's category for SEO purposes. Derived from brand-config.md's "Category positioning" line. If the line is verbose, condense to 1–3 words (e.g. for Pleasur.AI: "ai adult companions" → root seed ai companion). When in doubt, prefer the broader of two candidates — Topic Research returns richer trees from broader roots.
   • Brand domain — used as the .Trends root URL. Strip protocol and trailing slash.

3. Call mcp__semrush__topic-research with the brand category as the root keyword. Request the full idea-cluster tree: each cluster includes a name, headlines, questions, related searches, aggregated volume, and aggregated difficulty (KD%). Cache the raw response under content-pipeline/0-keywords/cache/topic-research-raw.json so re-runs without --regen don't refetch when only writing the structured tree.

4. Call mcp__semrush__trends-overview with the brand domain as the root URL. Pull:

   • Trending queries within the category (volume momentum up vs. down)
   • Competitor traffic shifts (domains gaining or losing share)
   • Audience overlap (which competitor audiences also visit which sites) Cache the raw response under content-pipeline/0-keywords/cache/trends-raw.json.

5. Assemble topic-graph.json as a structured tree:

   {
     "root_seed": "ai companion",
     "brand_domain": "pleasur.ai",
     "clusters": [
       {
         "id": "ksb-cluster-id-from-semrush",
         "name": "ai girlfriend",
         "volume": 246000,
         "kd_percent": 64,
         "headlines": ["...", "..."],
         "questions": ["what is an ai girlfriend?", "..."],
         "related_searches": ["...", "..."]
       }
     ],
     "generated_at": "ISO8601 UTC",
     "brand_snapshot": "sha256-hex-of-brand-config-md",
     "_meta": {
       "source": "Semrush Topic Research + .Trends",
       "tool_inventory_ref": ".claude/skills/keyword-research-pipeline/references/semrush-mcp-tool-inventory.md"
     }
   }
   

   Sort clusters by volume descending. Cap headlines / questions / related_searches at 10 entries each per cluster — Layer 1a only consumes the top 5 clusters anyway, but downstream tooling stays bounded.

6. Assemble trends.md as readable markdown for the redteam agent:

   # Category trends — {brand_domain} ({YYYY-MM-DD})
   
   ## Trending up (volume momentum +)
   - keyword (vol_now → vol_prev, change %)
   
   ## Trending down (volume momentum −)
   - keyword (vol_now → vol_prev, change %)
   
   ## Competitor traffic shifts
   - competitor.com — gaining/losing X% organic share over the last 90 days
   
   ## Audience overlap
   - competitor.com ↔ othercompetitor.com — Y% audience overlap
   
   _Source: Semrush .Trends, generated {ISO timestamp}_
   

   Keep it tight — under 60 lines. The redteam agent reads this as ambient context, not a primary input.

7. Write both files atomically (write to .tmp then move). Print a one-line summary: cluster count, top 3 cluster names with volumes, trending-up count, trending-down count, top 3 competitors by traffic shift.

Output

• content-pipeline/0-keywords/topic-graph.json — structured idea-cluster tree
• content-pipeline/0-keywords/trends.md — markdown trends summary
• content-pipeline/0-keywords/cache/topic-research-raw.json — raw MCP response (gitignored)
• content-pipeline/0-keywords/cache/trends-raw.json — raw MCP response (gitignored)

Idempotency

Same contract as seed-modifier-prompt: re-runs without --regen exit cleanly when the brand-config SHA-256 hasn't changed. Two MCP calls per actual run; zero on the no-op path. Safe for the orchestrator to call on every keyword-research-pipeline invocation without worrying about cost.

Failure handling

Layer 0 never blocks the pipeline. It's enrichment, not a gate. Specific failure modes:

• mcp__semrush__topic-research returns empty (root seed too narrow / not indexed): write a stub topic-graph.json with clusters: [], _meta.degraded: true, and the root_seed used. Append a one-line entry to content-pipeline/0-keywords/cache/topic-discovery-failed.log with the timestamp and reason. Exit code 0. Layer 1a reads the empty clusters array and falls through to brand-config-only seed generation (current behavior preserved).
• mcp__semrush__topic-research errors (auth, network, server): write the same stub graph with _meta.degraded: true and _meta.error: "<reason>". Log to cache/topic-discovery-failed.log. Exit 0.
• mcp__semrush__trends-overview returns empty / errors: write a trends.md containing only the heading and a single line _Source: Semrush .Trends — no data returned for {brand_domain} ({YYYY-MM-DD})_. Log to cache/topic-discovery-failed.log. Exit 0. The redteam agent reads the absence as "no trend signal available" and reasons without it.
• Both calls error with the same auth code (401): Semrush MCP isn't connected or consent expired. Surface a clear one-line error to stderr ("Semrush MCP unauthenticated — re-run consent or check SEMRUSH_API_KEY_BLOG_AGENT") and exit 0 anyway. The orchestrator continues.
• Rate-limit (429): persist whatever responses we did get; if neither call succeeded, write the stub artefacts and log the quota event. Exit 0 (do not propagate exit 75 — Layer 0 is non-blocking). The orchestrator will pick up the cached stubs on the next run.

The discipline here is the user's call-out: "the pipeline never blocks on Layer 0 because it's enrichment, not a gate."

Quality checklist

• topic-graph.json exists and parses as JSON
• brand_snapshot matches current brand-config.md SHA-256
• clusters array is sorted by volume descending (or empty + _meta.degraded: true)
• Each non-degraded cluster has at least: id, name, volume, headlines, questions
• trends.md exists and has a section heading even if empty
• Failure paths log to cache/topic-discovery-failed.log rather than crashing
• Two MCP calls maximum per actual run; zero on no-op
• Cache files are under content-pipeline/0-keywords/cache/ (gitignored)

Why this exists

Ryan Law's pre-Semrush method had no equivalent of Topic Research or .Trends — Ahrefs surfaced individual keywords but not the cluster topology of a category, and not the time-axis momentum signal that tells you whether a topic is rising or fading. Both are first-class signals in Semrush.

• The cluster topology lets Layer 1a generate seeds that match how the brand's category is actually organized in Google's view (rather than a brand-config-trained generative guess).
• The momentum signal lets Layer 4's redteam reject keywords that look attractive but are on a 12-month decline — an opportunity-cost critique the legacy pipeline couldn't make.

Adding Layer 0 doesn't disturb the existing chain — Layers 1–5 still run if Layer 0 produces nothing. It's pure enrichment, gated on a cheap idempotency check.

When the brand-config has no clear category

(Edge case: a personal blog or generic agency site without a sharp category positioning.) The root seed will be too broad and Topic Research returns a shallow cluster tree. The skill still runs — it writes whatever it gets, and downstream layers behave as if Layer 0 returned an empty tree. No special-casing required; the failure-handling path covers it.

Invocation from the master orchestrator

/keyword-research-pipeline calls this skill first, before Layer 1a. Because of the brand-config hash check, the master can call it on every run without worrying about waste. On a typical run with stable brand-config, Layer 0 is a < 1-second no-op.

References

• .claude/skills/research/references/semrush-mcp-cheatsheet.md — Topic Research playbook (sections "Topic Research playbook" and ".Trends quick-reference")
• .claude/skills/keyword-research-pipeline/references/semrush-mcp-tool-inventory.md — source of truth for the actual mcp__semrush__* tool names exposed by the connected MCP. Verify mcp__semrush__topic-research and mcp__semrush__trends-overview resolve to live tools before assuming the placeholders are correct.
• .claude/skills/keyword-research-pipeline/references/semrush-metric-translation.md — KSB cluster id is the "parent topic" replacement; reference for downstream layers that read topic-graph.json.