content-gaps

content-gaps — discovery skill (Phase 7 Wave 2, paid-MCP DFS HEAVY)

10-step protocol. Steps map 1:1 to workflow_runner invocations + the spec §16.5 8-step MCP discipline + §16.8 budget pre-flight. Raw JSON drift recovery is mandatory: every DFS response is dropped into inbox/dfs/ before any transform runs, so a transform bug never costs us the upstream payload (which is paid; re-fetch costs credits).

This skill follows the convention authority of skills/ingestion/dfs-pull/SKILL.md (Phase 6 — staging-only D-003 pattern + REST/flat shape tolerance via _normalize_dfs_response) and skills/discovery/cannibalization/SKILL.md (Wave 1 structural shape). The 10-step protocol, raw JSON inbox discipline, budget pre-flight, and provenance event format are reused verbatim — only the domain content (target sources, transform script, gap_score heuristic) changes. Deviate only with an ADR.

Inputs (frontmatter contract)

Name	Type	Default	Notes
project_slug	string	—	Required. Resolves projects/{slug}/master.xlsx.
seed_keyword	string	—	Required. Drives DFS fetches; embedded in every staging row (cluster-map join key).
location_code	integer	2792	DFS Turkey.
language_code	string	"tr"	DFS Turkish.
keyword_cap	integer	5000	DoS guard per MCP tool.
use_content_analysis_search	boolean	false	Optional third source.

workspace_root is resolved via PSEO_WORKSPACE_ROOT env or explicit test override (mirrors workflow_runner / events_writer).

Outputs (artifacts produced)

• projects/{slug}/_state/staging/content_gaps_keyword_ideas_{date}_{slug}.json — keyword_ideas candidate staging table (schema-versioned, sha256 content_hash per row, ISO 8601 microsecond fetched_at). Columns from content_gaps_transform.CONTENT_GAPS_COLUMNS.
• projects/{slug}/_state/staging/content_gaps_related_keywords_{date}_{slug}.json — related_keywords candidate staging table.
• projects/{slug}/_state/staging/content_gaps_content_analysis_search_{date}_{slug}.json — optional content_analysis_search staging table (only when input use_content_analysis_search=true).
• projects/{slug}/outputs/reports/{date}-content-gaps.md — human-readable summary (top gap_score keywords, source breakdown).
• projects/{slug}/_state/events.jsonl — event_kind=provenance entries (source.kind=dataforseo_mcp, target_excel_sheet=null — staging-only).
• projects/{slug}/inbox/dfs/{date}-keyword_ideas-gaps-{slug}.json — raw labs payload (drift recovery).
• projects/{slug}/inbox/dfs/{date}-related_keywords-gaps-{slug}.json — raw labs payload.

Note: content-gaps does NOT write to master.xlsx. Phase 8 cluster-map and new-content-plan skills consume the staging tables and project to master.xlsx#cluster_keywords / new_content_plan downstream (F-09 shared writer disiplini orada uygulanır).

D-003 staging-only routing (paterni: dfs-pull)

The Phase 6 D-003 lesson is enforced verbatim here:

• Discovery skills that produce candidate inventory route to _state/staging/ only. No transaction.append. No master.xlsx write.
• Phase 8 planning skills (cluster-map, new-content-plan) consume staging tables and project the chosen subset into Excel — they own the F-09 shared-writer discipline for sheets with multiple writers.
• Re-running content-gaps does NOT touch master.xlsx; only the staging JSON is overwritten (idempotent: same input → byte-stable file).

Shape adapter (_normalize_dfs_response): runtime tolerates two DFS response shapes — REST envelope tasks[0].result[0].items AND wrapper- flattened items — by importing the function from scripts.ingestion.dfs_pull (not copying). Any future shape fix in dfs-pull is inherited automatically; KOPYA YASAK is enforced by the import-discipline test.

10-Step Body Protocol

Each step name must match the steps[*].name passed to workflow_runner.create_run. Names are stable identifiers across runs.

Step 1 — preflight_budget (§16.8, MANDATORY for paid-MCP skills)

Subprocess wrapper around scripts/budget/check_budget.py (W-A3/W-A4 paterni). DURUR #2 if exceeded — never silently downgrade.

from scripts.discovery import content_gaps_transform as cgt

envelope = cgt.preflight_budget(
    project_config_path=project_root / "project.config.json",
    events_path=project_root / "_state" / "events.jsonl",
)
# envelope == {"budget_per_day": 500, "used_24h": 15, "remaining": 485, "exceeded": false}

The wrapper raises BudgetExceededError when the underlying script exits non-zero. The error inherits from ContentGapsError so a single except handles every DURUR class this skill emits.

Step 2 — create_run

Open a workflow run shell. The state file lives at projects/{slug}/_state/workflows/{run_id}.json (ADR-021).

from scripts.state import workflow_runner
handle = workflow_runner.create_run(
    skill="content-gaps",
    project_slug=project_slug,
    steps=[
        {"name": "preflight_budget"},
        {"name": "fetch_keyword_ideas"},
        {"name": "fetch_related_keywords"},
        {"name": "fetch_content_analysis_search"},  # optional
        {"name": "transform"},
        {"name": "request_approval"},
        {"name": "write_staging"},
        {"name": "render_report"},
    ],
)

Step 3 — fetch_keyword_ideas (MCP §16.5 step 3 — raw inbox first)

workflow_runner.start_step(handle.run_id, 1, project_slug=project_slug)
raw_ideas = mcp__dataforseo__dataforseo_labs_google_keyword_ideas(
    keywords=[seed_keyword],
    location_code=location_code,    # 2792 = Turkey
    language_code=language_code,    # "tr"
    limit=keyword_cap,
)
inbox_path = (
    workspace_root / "projects" / project_slug
    / "inbox" / "dfs"
    / f"{today.isoformat()}-keyword_ideas-gaps-{project_slug}.json"
)
inbox_path.parent.mkdir(parents=True, exist_ok=True)
inbox_path.write_text(json.dumps(raw_ideas, ensure_ascii=False, indent=2))
workflow_runner.finish_step(handle.run_id, 1, project_slug=project_slug,
                            output_ref=str(inbox_path))

Step 4 — fetch_related_keywords

raw_related = mcp__dataforseo__dataforseo_labs_google_related_keywords(
    keyword=seed_keyword,
    location_code=location_code,
    language_code=language_code,
    depth=2,
    limit=keyword_cap,
)
related_inbox = (
    workspace_root / "projects" / project_slug
    / "inbox" / "dfs"
    / f"{today.isoformat()}-related_keywords-gaps-{project_slug}.json"
)
related_inbox.write_text(json.dumps(raw_related, ensure_ascii=False, indent=2))

Step 5 — fetch_content_analysis_search (OPTIONAL)

Only when the caller passes use_content_analysis_search=true.

raw_cas = None
if use_content_analysis_search:
    raw_cas = mcp__dataforseo__content_analysis_search(
        keyword=seed_keyword,
        location_code=location_code,
        language_code=language_code,
    )
    cas_inbox = (
        workspace_root / "projects" / project_slug / "inbox" / "dfs"
        / f"{today.isoformat()}-content_analysis_search-gaps-{project_slug}.json"
    )
    cas_inbox.write_text(json.dumps(raw_cas, ensure_ascii=False, indent=2))

Step 6 — transform

Pure compute via scripts/discovery/content_gaps_transform.py (staging-only output). The transform calls the imported _normalize_dfs_response() first to flatten REST or wrapper shapes uniformly, then builds one staging payload per source:

python3 scripts/discovery/content_gaps_transform.py \
    --raw-keyword-ideas    inbox/dfs/{date}-keyword_ideas-gaps-{slug}.json \
    --raw-related-keywords inbox/dfs/{date}-related_keywords-gaps-{slug}.json \
    --seed-keyword         "{seed}" \
    --keyword-cap          5000 \
    --staging-dir          _state/staging/ \
    --project-slug         {slug}

Produces 2-3 staging JSON files (content_gaps_keyword_ideas_*.json, content_gaps_related_keywords_*.json, optionally content_gaps_content_analysis_search_*.json) — each with schema_version, tool, seed_keyword, fetched_at (ISO 8601 µs UTC), row_count, columns, rows[] (1-indexed id + sha256 content_hash).

Gap score formula (transform domain)

gap_score = (search_volume / (1 + keyword_difficulty)) * (1 - competition_norm)

competition_norm:
    numeric  → clipped to [0, 1] (DFS competition_index sometimes 0..100;
                                  collapsed by /100)
    LOW      → 0.20
    MEDIUM   → 0.55
    HIGH     → 0.90
    unknown  → 0.50  (mid-band default)

Coarse but deterministic. Higher volume → higher score; higher difficulty / higher competition → lower score. Pluggable: callers may substitute a custom scorer; the staging column stays gap_score regardless. Result is rounded to 4 decimals for staging idempotency.

Rows are sorted by gap_score desc, ties broken by keyword asc.

Step 7 — request_approval (skill EXIT awaiting_approval)

workflow_runner.request_approval(
    handle.run_id, project_slug=project_slug,
    approver="user",
    subject=f"{seed_keyword} için {total_candidates} aday keyword bulundu, _state/staging/'e yazalım mı?",
    step_index=5,
)
# Skill exits here. The user replies in a fresh session; resume below.

Step 8 — write_staging (atomic, schema-validated)

content_gaps_transform.write_staging() writes one self-contained staging JSON per source (overwrite-idempotent) under projects/{slug}/_state/staging/. Schema validated by _validate_staging_payload() before write — StagingSchemaError (IDENTITY-shared with dfs-pull) on drift. NO transaction.append here; staging-only.

from scripts.discovery import content_gaps_transform as cgt
written = cgt.write_staging(
    result,
    staging_dir=workspace_root / "projects" / project_slug / "_state" / "staging",
    project_slug=project_slug,
    date=today.isoformat(),
)
# written == {"keyword_ideas": "...", "related_keywords": "...", ["content_analysis_search": "..."]}

Step 9 — Provenance event + report

from scripts.state import events_writer
events_writer.append_provenance(
    project_id=project_slug,
    source={"kind": "dataforseo_mcp", "mcp_server": "dataforseo",
            "mcp_tool": "dataforseo__dataforseo_labs_google_keyword_ideas",
            "response_bytes": len(json.dumps(raw_ideas))},
    operation="ingest",
    target_table="content_gaps_keyword_ideas",
    target_excel_sheet=None,    # staging-only; Phase 8 cluster-map projects to Excel
    rows_written=0,             # staging-only: no Excel rows yet
    cost={"provider": "dataforseo",
          "credits": float(estimated_credits),
          "budget_key": "project.config.dataforseo.budget_credits_per_day"},
)
# Repeat for related_keywords (and content_analysis_search when used).

render_template.py templates/reports/content-gaps.template.md data.json → outputs/reports/{date}-content-gaps.md. Variables: $project_slug, $date, $seed_keyword, $location_code, $language_code, $total_candidates, $top_keyword, $top_gap_score, $source_breakdown, $top1_* … $top3_*, $estimated_credits, $budget_preflight_status, $staging_path_ideas, $staging_path_related, $run_id.

Step 10 — complete

workflow_runner.complete(handle.run_id, project_slug=project_slug, outputs={
    # F5: outputs.* must be STRING-TYPED.
    "ideas_staging_rows":   str(result["keyword_ideas"]["row_count"]),
    "related_staging_rows": str(result["related_keywords"]["row_count"]),
    "staging_files":        ";".join(written.values()),
    "seed_keyword":         seed_keyword,
    "credits_used":         str(estimated_credits),
    "report_path":          str(report_path),
    "raw_jsons":            ";".join([str(inbox_path), str(related_inbox)]),
})

DURUR conditions (9)

Stop and flag the manager — do not patch, do not fall back.

1. mcp__dataforseo__dataforseo_labs_google_keyword_ideas OR …_related_keywords returns auth/network/scope error.
2. scripts/budget/check_budget.py exits non-zero → subprocess wrapper raises BudgetExceededError. STOP, await manager approval to lift the cap (W-A3/W-A4 paterni).
3. REST/flat shape both fail — imported _normalize_dfs_response raises ValueError("Unrecognized DFS response shape"). STOP, the wrapper version drift is an ADR-level event.
4. _state/staging/ path cannot be created under projects/{slug}/ (workspace path missing or read-only).
5. Keyword count from MCP > keyword_cap (default 5000) → KeywordCapExceededError. DoS prevention; never project a runaway payload.
6. inbox/dfs/ path cannot be created (workspace path missing or non-writable).
7. PSEO_WORKSPACE_ROOT env var unset and no explicit workspace_root arg passed to workflow_runner / events_writer. STOP, surface to manager.
8. workflow_runner.create_run fails schema validation (schemas/workflow-run.schema.json).
9. Both required MCPs return 0 candidate rows for the seed → NoCandidatesError. The skill refuses to silently emit empty staging — surfaces "no content gaps found for seed=…" so the manager can pick a different seed.

(Defensive #5 mirror: StagingSchemaError from imported dfs-pull re-validation also DURURs the run if a row drifts before disk write.)

Cross-references

• Schemas: schemas/dataforseo-endpoint-mapping.schema.json (DFS contract; cost.credits_per_call), schemas/events.schema.json (source.kind=dataforseo_mcp, target_excel_sheet=null for staging-only ops), schemas/skill-frontmatter.schema.json (this frontmatter; budget.additionalProperties:false enforced).
• Cross-modules (IMPORT-only): scripts/state/workflow_runner.py, scripts/state/events_writer.py, scripts/budget/check_budget.py (subprocess wrapper). From scripts/ingestion/dfs_pull.py: _normalize_dfs_response AND StagingSchemaError are imported — KOPYA YASAK; identity preserved by the import-discipline test. Note: scripts/excel/transaction.py is NOT imported (staging-only).
• Transform: scripts/discovery/content_gaps_transform.py (gap_score, transform, write_staging, staging_filename, _staging_table_dict, _validate_staging_payload, CONTENT_GAPS_COLUMNS, preflight_budget).
• Tests: tests/skills/test_content_gaps.py (≥6 cases incl. import- discipline identity check).
• Template: templates/reports/content-gaps.template.md.
• Phase 8 cluster-map skill consumes _state/staging/content_gaps_*.json and applies F-09 shared-writer discipline when projecting to master.xlsx#cluster_keywords / new_content_plan alongside dfs-pull staging.

Discipline checklist

• TODO/fallback YASAK — every DURUR raises, none silently downgrade.
• Schema-first — frontmatter validates against schemas/skill-frontmatter.schema.json Draft 7. Budget block conforms to additionalProperties:false (Q-W-A4-01: only uses_paid_mcp + estimated_credits; no _per_call / _per_url leakage).
• Plugin-agnostik — no slug literals; project_slug flows through every path; transform has 0 hardcoded slug words.
• ADR-013: Use when/Also use when/Do not use when are STRING content inside description, not separate fields.
• Budget pre-flight integration — scripts/budget/check_budget.py invoked at Step 1 (subprocess wrapper) BEFORE any paid call.
• Append-only — all events via events_writer; staging JSON is overwrite-idempotent (re-run produces stable id/content_hash); Phase 8 owns the F-09 shared-writer discipline downstream.
• Cross-module IMPORT discipline — workflow_runner / events_writer are imported, never modified. From dfs_pull: _normalize_dfs_response AND StagingSchemaError are imported (KOPYA YASAK enforced by identity test). scripts.excel.transaction is NOT imported (staging-only — D-003 resolution).
• DoS guard — keyword_cap default 5000 (configurable per-run); MCP responses larger than the cap → KeywordCapExceededError.
• F1: write target is _state/staging/ (no master.xlsx from this skill — Phase 8 cluster-map / new-content-plan owns the projection).
• F5: outputs.* values are STRING-TYPED (artifact paths or stringified counts), never raw ints.