Research

Research Orchestrator

Accepts a freeform research request, plans scope, manages a 7-phase claim-based pipeline with 4 human checkpoint gates, and produces a polished, citation-rich research document. Every claim is traceable to its source, every gap is detected and addressed, and every run is reproducible and auditable.

The orchestrator coordinates collection, claim extraction, graph relationship enrichment, section brief synthesis, report composition, and optional publishing. output/report.md is the canonical final report; HTML, PDF, and QMD are publishing products rendered from it.

Claim Pipeline Contract

New runs use manifest.pipeline_contract_version = "claim_pipeline_v1" and these phases:

planning
→ collection
→ claim_extraction
→ graph_relationships
→ section_brief_synthesis
→ formatting
→ publishing

synthesis/claim_bank.json is the canonical research state. synthesis/raw_research.md is deprecated; if a prose diagnostic is needed, write synthesis/research_notes.md and keep it out of the main handoff.

A global file is tiny only when it is under 20KB or under 300 lines. Downstream agents must not read full claim_bank.json, full inventory.json, full graph files, or all section briefs unless the file qualifies as tiny. Otherwise they consume per-section slices.

Dependency install policy

The research pipeline must NEVER auto-install or auto-download these runtime dependencies:

• crawl4ai (and its Playwright/browser runtime)
• playwright / Playwright browsers
• docling

If any of these are missing when the pipeline needs them, the skill/agent MUST:

1. Detect the missing dependency.
2. Emit a clear message naming the missing tool and the install command the user can run themselves (e.g., pipx install crawl4ai, pipx install docling, playwright install chromium).
3. Stop the run — halt the phase with a non-zero exit. Do NOT proceed with a fallback collection mode.

Never execute pip install, pipx install, npm install, playwright install, crawl4ai-setup, crawl4ai-doctor --install, or any equivalent install command for these tools from within the pipeline.

Exception (explicitly allowed): The Quarto quarto-ext/mermaid extension is auto-installed by scripts/publish.sh when PDF output is selected. This is the ONLY auto-download permitted in this workflow.

Run Mode Contract

Manifest mode fields are separate:

• run_mode: normal, resume, or inspect
• collection_mode: web_and_docs, docs_only, web_only, or metadata_only
• validation_mode: normal or strict

source_channels = {"web": bool, "documents": bool} is the source intent object. collection_mode=auto is accepted by the initializer but is never persisted; it resolves from source_channels before manifest.json is written. metadata_only means the collection phase is skipped and no extraction tools are required. Legacy manifests or CLI calls that say none are treated as metadata_only; new manifests must persist metadata_only.

Hard requirements:

• web_and_docs: Crawl4AI, Playwright browser runtime, and Docling
• docs_only: Docling
• web_only: Crawl4AI and Playwright browser runtime
• metadata_only: no extraction tools; inventory/resume metadata only

validation_mode=normal blocks on required phase artifacts and warns on nonessential audit artifacts. validation_mode=strict fails on missing audit files, invalid schemas, or contract violations.

Quick Start

1. User triggers with /research "topic or question"
2. Orchestrator checks for interrupted runs via find_interrupted_runs()
3. If interrupted runs exist: display them and offer to resume or start fresh
4. If new run: call init_run.py CLI to create run directory and manifest
5. Proceed through the 7 pipeline phases with 4 checkpoint gates

Budget shorthand: if the user starts a new request with /research --50,10,2 topic, the leading shorthand means max_pages=50, max_per_domain=10, and max_depth=2. Keep that leading token as a CLI budget override and do not include it in the research request text.

Scripts

• scripts/init_run.py -- Run initialization and resume detection
  • New run: python3 ~/.claude/skills/research/scripts/init_run.py "research request" --max-pages 75 --max-per-domain 15 --max-depth 3 --collection-mode auto
  • New run with budget shorthand: python3 ~/.claude/skills/research/scripts/init_run.py --50,10,2 "research request"
  • Resume list: python3 ~/.claude/skills/research/scripts/init_run.py --list-interrupted
  • Resume run: python3 ~/.claude/skills/research/scripts/init_run.py --resume RUN_ID
  • Machine-readable resume: python3 ~/.claude/skills/research/scripts/init_run.py --resume RUN_ID --json
  • Functions: next_run_id(), create_manifest(), update_phase_status(), find_interrupted_runs(), resume_run(), resolve_collection_mode()

Machine-readable resume output is the workflow control source of truth. The orchestrator must read next_phase, validate required_artifacts, and dispatch by this table:

Do not treat resume output as a status report only. It determines the next phase to execute.

• scripts/validate_artifact.py -- Runtime artifact validation

  • Usage: python3 ~/.claude/skills/research/scripts/validate_artifact.py <artifact_path> <schema_path>
  • Returns JSON: {"status": "pass"|"warn"|"error", "errors": [], "warnings": []}
  • Used at checkpoint gates to surface validation results as warnings, not hard stops

• scripts/check_content_rules.py -- Report content-rules scanner

  • Usage: python3 ~/.claude/skills/research/scripts/check_content_rules.py --target=report <report_md_path>
  • Returns JSON stdout: {"status": "pass"|"warn"|"error", "violations": [...], "summary": {"total": N, "by_rule": {...}}}
  • Exit codes: 0=pass, 1=warn (violations found), 2=error (file missing, path traversal, oversized)
  • Checks: RULE-02 (URL cited >3x/section), CONS-01 (empty headers), CONS-02 (<2 sentences or >800 words/section), HIER-04 (bare code fences)
  • Violations are advisory WARNINGS ONLY — never blocks synthesis or Gate 3 (D-21)

• references/architecture_execution_plan.md -- Maintainer checklist for dependency modes, gate boundaries, resume dispatch, depth taxonomy, and README promise discipline

Run Logging

The orchestrator maintains <run_dir>/logs/run_log.md throughout the pipeline. Every significant action gets one row.

Format (per D-10):

## Run Log

| Timestamp | Phase | Action | Status | Detail |
|---|---|---|---|---|
| 2026-04-11T14:23:01Z | planning | scope_written | ok | 8 subtopics, 3 source types |

Writing pattern (inline Python helper):

Define this helper once at the start of the run (after init_run.py creates the run directory). Do NOT create a separate script file -- use inline Python file I/O per D-12.

import datetime
from pathlib import Path

def append_log(run_dir, phase, action, status, detail):
    log_path = Path(run_dir) / "logs" / "run_log.md"
    ts = datetime.datetime.now(datetime.timezone.utc).strftime('%Y-%m-%dT%H:%M:%SZ')
    row = f"| {ts} | {phase} | {action} | {status} | {detail} |"
    if not log_path.exists():
        log_path.parent.mkdir(parents=True, exist_ok=True)
        header = "## Run Log\n\n| Timestamp | Phase | Action | Status | Detail |\n|---|---|---|---|---|\n"
        log_path.write_text(header + row + "\n")
    else:
        with open(log_path, 'a') as f:
            f.write(row + "\n")

Pipeline Phases

Phase 1: Planning

Objective: Transform a freeform research request into a structured scope and collection plan.

Steps:

1. Accept research request. Capture the user's freeform text. This becomes user_request in manifest.json. If the request starts with a budget shorthand token like --50,10,2, treat it as max_pages,max_per_domain,max_depth and exclude it from user_request. The shorthand is valid only at the start of a new /research request.

2. Check for interrupted runs. If the user invoked /research with no topic, call discovery mode:

   python3 ~/.claude/skills/research/scripts/init_run.py
   

   For an explicit list request, call:

   python3 ~/.claude/skills/research/scripts/init_run.py --list-interrupted
   

   If interrupted runs exist, display them with their problem phases and completed phases. Ask the user whether to resume an existing run or start fresh. To resume, call:

   python3 ~/.claude/skills/research/scripts/init_run.py --resume RUN_ID
   

3. Initialize run directory. For a new run, call init_run.py:

   python3 ~/.claude/skills/research/scripts/init_run.py "user request text" --max-pages 75 --max-per-domain 15 --max-depth 3
   

   If the user supplied leading budget shorthand, preserve it before the request:

   python3 ~/.claude/skills/research/scripts/init_run.py --50,10,2 "user request text"
   

   This creates research/run-NNN-TIMESTAMP/ with manifest.json. Record the run directory path for all subsequent operations.

   Log: append_log(run_dir, 'planning', 'run_initialized', 'ok', f'Run {run_id} created')

4. Inspect workspace context (local context — ALWAYS runs). Local context search MUST scope to the project directory (git repo root or cwd). NEVER search ~/.claude/, home directory, or any path outside the project. This step ALWAYS runs — on empty result, note "no local artifacts found" and proceed.

   Determine the project root:

   PROJECT_ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)
   

   Scan $PROJECT_ROOT (only) for:

   • Existing research/ runs (previous research on related topics)
   • PDF files, markdown documents, or other local sources relevant to the request
   • Any files the user explicitly referenced in their request

   Record findings for scope planning. If nothing found, record "no local artifacts found" and continue — do NOT skip remaining planning steps.

   Log: append_log(run_dir, 'planning', 'workspace_scanned', 'ok', f'Found {N} prior runs, {M} local docs')

5. Determine task type. Classify the request as one of:

   • new -- Fresh research on an unfamiliar topic
   • update -- Refresh stale sections of existing research
   • expansion -- Add depth or breadth to existing research
   • re-audit -- Re-verify sources and claims in existing research Record in manifest.json by adding a task_type field.

   Log: append_log(run_dir, 'planning', 'task_type_set', 'ok', f'Type: {task_type}')

6. Plan scope (7-layer decomposition). Break the research request into:

   • Subtopics -- Distinct areas to investigate (5-15 typical)
   • Source types -- Expected source categories (official docs, academic papers, blog posts, etc.)
   • Key questions -- Specific questions each subtopic should answer
   • Coverage areas -- What the final document should cover
   • Priority ranking -- Which subtopics are most critical

   Decompose the request using the 7-layer methodology (INV-01): identity, purpose, mechanics, relations, comparison, evidence, open questions. Every layer that has applicable sub-questions must populate at least one L1 question. The resulting question tree must populate ≥3 distinct layers (D-16); a flat plan (all questions under one layer) will be rejected at Gate 1.

   For the relations layer, generate bridge questions using:

   from scope_paths import ensure_scope_dir
   from question_tree import build_question_tree, select_bridge_entities, write_question_tree
   
   ensure_scope_dir(run_dir)
   entities, source = select_bridge_entities(run_dir, top_n=5)  # graph_centrality → subtopic_fallback (D-10/D-11)
   tree = build_question_tree(
       topic=user_request,
       subtopics=[s["name"] for s in subtopics],
       bridge_entities=entities,
       generation_method=source,
       top_n=5,
   )
   

   Bridge questions use the canonical phrasing "What is the relationship between X and Y?" for every pair among the top-N entities (REL-09). top_n is hard-clamped to ≤10 to prevent combinatorial blowup.

   Log: append_log(run_dir, 'planning', 'scope_planned', 'ok', f'{N} subtopics, {M} source types')

6b. LaTeX/TinyTeX pre-flight (advisory, non-blocking). Before displaying Gate 1, detect PDF rendering availability so the user can install TinyTeX before investing research effort. This is advisory, not blocking (D-07, D-08) — a missing TinyTeX must never stop Gate 1 from proceeding. The user retains agency; graceful render fallback (D-09) handles any PDF render failure at Phase 6.

# Security note: DO NOT pass `-shell-escape` to `quarto render` anywhere in this pipeline.
# Defaults-safe is required — `\write18`/shell-escape enables arbitrary code execution during PDF render.
quarto check > /tmp/quarto_check_${run_id}.log 2>&1 || true
if grep -iq "tinytex" /tmp/quarto_check_${run_id}.log; then
  tinytex_available=true
else
  tinytex_available=false
fi

Record the result in manifest.json under environment.tinytex_available (boolean). Example:

import json
manifest = json.loads(Path(manifest_path).read_text())
manifest.setdefault("environment", {})["tinytex_available"] = tinytex_available
Path(manifest_path).write_text(json.dumps(manifest, indent=2))

If tinytex_available is false, surface this advisory warning in the Gate 1 presentation (prepended to the summary table, not as a blocker):

⚠️  TinyTeX not detected. PDF output will be unavailable at Gate 3 (or will render-fail gracefully).
    Install with: quarto install tinytex

Gate 1 output-target review uses manifest.environment.tinytex_available to annotate PDF-inclusive targets with a "(requires TinyTeX — not detected)" caveat so the user retains agency. Phase 6 graceful render fallback handles any render failure.

6c. Gate 1 defaulted controls. Do not ask separate Gate 1 questions for implementation knobs. The initializer and planner set these defaults before the scope review:

• depth = "standard"
• audience = "external"
• tone = "professional"
• render_targets = ["md", "html"]
• section_depth_overrides = {}
• performance_mode = "auto"
• validation_mode = "normal"

If the user wants changes, they use the single Gate 1 "Edit scope/depth/output" option. Per-section depth inherits from global depth unless section_depth_overrides explicitly names a section.

7. CHECKPOINT GATE 1 (Post-Planning). Present the proposed scope to the user. Tables MUST be printed to chat via normal output BEFORE AskUserQuestion. Do NOT embed tables inside the AskUserQuestion question/header/options. Never ask two questions at Gate 1 — one combined confirm/adjust/abort only.

   Step A — Print tables to chat (normal output, NOT inside AskUserQuestion):

   Print the following as plain markdown tables in regular chat output:

   Field	Value
   Research question	{user_request}
   Task type	{task_type}
   Subtopics	{count} (see table below)
   Source channels	web={true/false}, documents={true/false}
   Source types	{comma-separated list}
   Collection mode	{web_and_docs / docs_only / web_only / metadata_only}
   Depth	{summary / standard / comprehensive / audit}
   Audience	{internal / external / technical / executive}
   Tone	{concise / professional / explanatory}
   Render targets	{md/qmd/html/pdf list}
   Validation mode	{normal / strict}
   Performance mode	{auto or resolved override}
   Coverage areas	{comma-separated list}
   Budget	max_pages={N}, max_per_domain={N}, max_depth={N}
   TinyTeX	{available / not detected — see advisory above if false}

   #	Subtopic	Priority
   1	{name}	{priority number}
   …	…	…

   (If tinytex_available is false, also print the advisory warning text here before the tables.)

   Step B — ONE combined AskUserQuestion call:

   scope_choice = AskUserQuestion(
       question="Review the scope above. How would you like to proceed?",
       options=[
           {"label": "Approve plan — proceed with current scope, depth, and output", "value": "confirm"},
           {"label": "Edit scope/depth/output — revise plan settings", "value": "adjust"},
           {"label": "Abort — cancel this run", "value": "abort"},
       ],
       multiSelect=False,
   )
   

   This is the ONLY AskUserQuestion call for scope confirmation at Gate 1. Do NOT follow it with a second "any adjustments?" question. If user selects "adjust", process changes and re-print tables + re-ask this same single question.

   See references/checkpoint_protocol.md Gate 1 for full specification.

   Tool resolution check: After init_run.py runs, verify manifest.collection_mode and manifest.environment.tools. Stop if the resolved mode's required tools are missing. metadata_only means collection is skipped and no extraction tools are required.

   Log: append_log(run_dir, 'gate_1', 'checkpoint_shown', 'ok', 'Gate 1 displayed') Log (after user responds): append_log(run_dir, 'gate_1', 'checkpoint_response', 'ok', f'User chose: {choice}')

8. Write scope and plan artifacts (all under scope/, D-05/D-06/D-07). On confirmation:

   • Create the scope/ subdirectory via ensure_scope_dir(run_dir)
   • Write scope/scope.md (format per references/scope.md.contract.md)
   • Write scope/plan.json (format per references/plan.json.contract.md)
   • Write scope/question_tree.json via write_question_tree(run_dir, tree) (format per references/question_tree.json.contract.md)
   • Validate plan.json:

     python3 ~/.claude/skills/research/scripts/validate_artifact.py research/run-NNN/scope/plan.json ~/.claude/skills/research/references/plan.schema.json
     

   • Gate 1 layered-plan validator (D-16..D-19): run the question tree validator with auto-regenerate loop.

     from gate1_validator import run_gate1_validator
     from scope_paths import question_tree_path
     
     result = run_gate1_validator(
         tree_path=question_tree_path(run_dir),
         run_dir=run_dir,
         regenerate=regenerate_layered_plan,   # rewrites scope/question_tree.json with a layered plan
         max_attempts=2,                       # D-19 cap; prevents infinite loops
     )
     if result["status"] == "warn":
         # D-17/D-19: flag the checkpoint banner so the user sees the downgrade.
         banner = "⚠️ Question tree validation downgraded to manual review after 2 auto-regenerate attempts"
     

     Display only per-layer question counts to the user at Gate 1 (not the full tree) so the checkpoint stays legible (RESEARCH Open Question 2).

   Log: append_log(run_dir, 'planning', 'scope_written', 'ok', 'scope/scope.md + scope/plan.json + scope/question_tree.json written') Log: append_log(run_dir, 'planning', 'plan_validated', 'ok', f'plan.json: {validation_status}') Log (by run_gate1_validator internally): question_tree_validated (ok|warn|error) and question_tree_regenerated per attempt.

9. Update manifest. Mark planning complete:

   update_phase_status(manifest_path, "planning", "running")
   # ... after scope/plan written ...
   update_phase_status(manifest_path, "planning", "complete")
   

Phase 2: Collection

Objective: Gather evidence from web and document sources according to the plan.

Steps:

1. Update manifest:

   update_phase_status(manifest_path, "collection", "running")
   

2. Spawn collector agent. Use the Agent tool to spawn the collection subagent:

   Agent(
     prompt="Collect evidence for the research run at <run_dir_path>.
       Read scope/scope.md and scope/plan.json from the run directory for collection targets.
       Use scripts/parallel_crawl.py (Crawl4AI arun_many + MemoryAdaptiveDispatcher) for concurrent web crawling
       and scripts/parallel_docling.py for parallel document parsing.
       Write outputs to <run_dir_path>/collect/.
       Budget: max_pages=<N>, max_per_domain=<N>, max_depth=<N>.
       Use `manifest.runtime_profile.resolved.max_concurrent` and `manifest.runtime_profile.resolved.per_domain_cap` for crawl concurrency knobs, and `manifest.runtime_profile.resolved.docling_parallelism`, `docling_device`, and `docling_threads` for Docling SDK flags.
       Follow the research-collect skill instructions for all collection procedures.",
     subagent_type="research-collector",
     model="sonnet",
     description="Collect evidence for: <user_request summary>"
   )
   

   Log: append_log(run_dir, 'collection', 'agent_spawned', 'ok', 'research-collector dispatched')

3. Collector outputs. The collector produces these artifacts in <run_dir>/collect/:

   • evidence/*.md -- Individual evidence files with YAML provenance headers
   • inventory.json -- Full source catalog with metadata, tiers, and quality scores
   • collection_log.md -- Operations log with budget usage and decisions
   • coverage_matrix.md -- Topic-to-source coverage assessment
   • quarantine/*.md -- Quarantined items (suspicious, low-quality, or potentially harmful)

4. Validate inventory. On collector completion:

   python3 ~/.claude/skills/research/scripts/validate_artifact.py <run_dir>/collect/inventory.json ~/.claude/skills/research-collect/references/inventory.schema.json
   

   Log: append_log(run_dir, 'collection', 'inventory_validated', 'ok', f'inventory.json: {validation_status}')

5. CHECKPOINT GATE 2 (Post-Collection). Present coverage summary via AskUserQuestion:

   • Total sources collected
   • Sources by tier (1-5)
   • Topic coverage (Strong/Moderate/Weak/None per topic)
   • Quarantined item count
   • Budget usage (pages_used / max_pages)
   • Weak areas flagged

   User options:

   1. Proceed -- Continue to graph and synthesis
   2. Flag issues -- Note concerns (logged to run_log.md), then proceed
   3. Abort -- Cancel the run

   See references/checkpoint_protocol.md Gate 2 for full specification.

   Collection quality warnings: Check manifest.collection_warnings and stderr logs for:

   • BACKOFF_LOCK: concurrency frozen due to excessive rate-limit backoff
   • DOMAIN_CONCENTRATION: one domain > 40% top-1 share
   • DEVICE_FALLBACK: Docling fell back from MPS/CUDA to CPU for > 10% of docs
   • DOCLING_THIN_OUTPUT: one or more Docling docs returned thin_success class
   • DOCLING_PARTIAL: one or more Docling docs routed to quarantine as partial
   • DOCLING_CACHE_HIT_RATE: logged by parallel_docling.py (informational)
   • BACKOFF_THROTTLE_APPLIED: active backoff mutated dispatcher concurrency mid-run

   Surface any warnings before proceeding. Then present the quality summary table:

   Metric	Value	Flag
   Top-domain share	<N>%	⚠️ if > 40%
   Challenge / soft-fail pages	<N>	⚠️ if > 0
   thin_success (crawl)	<N>	info
   thin_success (Docling)	<N>	info
   Per-domain success-rate delta	worst: <domain> −<N>%	⚠️ if any domain > 20% worse than expected

   Populate from collection_log.md domain stats and docling_out.jsonl quality_class counts.

   Log: append_log(run_dir, 'gate_2', 'checkpoint_shown', 'ok', f'Gate 2: {N} sources, {M} quarantined') Log (after user responds): append_log(run_dir, 'gate_2', 'checkpoint_response', 'ok', f'User chose: {choice}')

6. Update manifest:

   update_phase_status(manifest_path, "collection", "complete")
   

Phase 3: Claim Extraction

Objective: Extract stable, atomic claims from collected evidence and write canonical claim state.

Canonical outputs:

• synthesis/global_id_registry.json
• synthesis/claim_bank.json
• synthesis/entity_index.json

Contract notes: Claims are the primary unit. categorized_evidence.json is not canonical. Every claim has exactly one primary_section_id, stable id, normalized content_hash, source_ids, confidence, salience, and include_in_report.

Steps:

1. Update manifest:

   update_phase_status(manifest_path, "claim_extraction", "running")
   

2. Initialize stable IDs. Run the claim helper before spawning extraction:

   python3 ~/.claude/skills/research-synthesize/scripts/claim_pipeline.py init-registry --run-dir "$run_dir"
   

   This creates or preserves synthesis/global_id_registry.json. IDs are generated once and never regenerated on resume.

3. Determine extraction granularity. Count non-quarantined evidence files and record manifest.evidence_count.

   • Small runs may use one mode=full synthesizer call if evidence fits context.
   • Medium or large runs must use mode=claim_batch calls per source, per planned section, or per fixed evidence batch.
   • No extraction agent may read all evidence when the corpus exceeds the tiny-file rule or the orchestrator batch threshold.

4. Spawn synthesizer for claim extraction. The synthesizer writes claim deltas to synthesis/claim_deltas/*.json, then merges them into claim_bank.json.

   Agent(
     subagent_type="research-synthesizer",
     model="sonnet",
     description="Extract claim state for: <user_request summary>",
     prompt="""
     mode: full
     run_dir: <run_dir_path>
   
     Execute claim extraction only.
     Read scope/plan.json, scope/question_tree.json, collect/inventory.json, and selected collect/evidence/*.md batches.
     For large runs, write one synthesis/claim_deltas/*.json file per source, section, or evidence batch, then merge.
     Do not write raw_research.md.
     Do not create planner sections.
     Produce synthesis/global_id_registry.json, synthesis/claim_bank.json, and synthesis/entity_index.json.
     Follow research-synthesize/SKILL.md Stage 1.
     """
   )
   

   Log: append_log(run_dir, 'claim_extraction', 'agent_spawned', 'ok', 'research-synthesizer dispatched for claim extraction')

5. Validate claim artifacts.

python3 ~/.claude/skills/research/scripts/validate_artifact.py "$run_dir/synthesis/global_id_registry.json" ~/.claude/skills/research-synthesize/references/global_id_registry.schema.json python3 ~/.claude/skills/research/scripts/validate_artifact.py "$run_dir/synthesis/claim_bank.json" ~/.claude/skills/research-synthesize/references/claim_bank.schema.json python3 ~/.claude/skills/research/scripts/validate_artifact.py "$run_dir/synthesis/entity_index.json" ~/.claude/skills/research-synthesize/references/entity_index.schema.json

Log validation results. Validation warnings surface at Gate 3.

6. **Update manifest:**
```python
update_phase_status(manifest_path, "claim_extraction", "complete")

Phase 4: Graph Relationships

Objective: Build relationship metadata from extracted claims and entities.

Canonical outputs:

• synthesis/claim_graph_map.json
• synthesis/section_graph_hints.json

Graph rules: Graph hints are advisory. They may enrich relationships inside planned sections, but may not create sections, reorder sections, override source quality, or force claim inclusion by centrality.

Steps:

1. Update manifest:

   update_phase_status(manifest_path, "graph_relationships", "running")
   

2. Build compact graph artifacts from claim/entity state.

   python3 ~/.claude/skills/research-synthesize/scripts/claim_pipeline.py build-entity-index --run-dir "$run_dir"
   python3 ~/.claude/skills/research-synthesize/scripts/claim_pipeline.py build-graph-artifacts --run-dir "$run_dir"
   python3 ~/.claude/skills/research/scripts/validate_artifact.py "$run_dir/synthesis/entity_index.json" ~/.claude/skills/research-synthesize/references/entity_index.schema.json
   python3 ~/.claude/skills/research/scripts/validate_artifact.py "$run_dir/synthesis/claim_graph_map.json" ~/.claude/skills/research-synthesize/references/claim_graph_map.schema.json
   python3 ~/.claude/skills/research/scripts/validate_artifact.py "$run_dir/synthesis/section_graph_hints.json" ~/.claude/skills/research-synthesize/references/section_graph_hints.schema.json
   

   section_graph_hints.json must list only planner-defined section IDs. Graph centrality is advisory and must not create or reorder sections.

3. Update manifest:

   update_phase_status(manifest_path, "graph_relationships", "complete")
   

Phase 5: Section Brief Synthesis

Objective: Produce compact per-section memory and slices for report composition.

Canonical outputs:

• synthesis/section_briefs/<section_id>.json
• synthesis/claim_slices/<section_id>.json
• synthesis/citation_audit.md
• synthesis/gap_analysis.md
• Optional diagnostics: synthesis/research_notes.md

Slicing rules: The report composer parent reads only section indexes, claim IDs per section, source IDs per section, graph hint summaries, and normalized output preferences. Section agents receive one brief, referenced claims, referenced sources, relevant graph hints, and boundary rules.

Steps:

1. Update manifest:

   update_phase_status(manifest_path, "section_brief_synthesis", "running")
   

2. Spawn synthesizer for section briefs and audits.

   Agent(
     subagent_type="research-synthesizer",
     model="sonnet",
     description="Build section briefs for: <user_request summary>",
     prompt="""
     mode: section_briefs
     run_dir: <run_dir_path>
   
     Build compact section briefs, per-section claim slices, citation_audit.md, and gap_analysis.md.
     Read claim_bank.json, section_graph_hints.json, scope/plan.json, and source metadata only as needed.
     Do not write raw_research.md.
     Do not read all evidence.
     Every planned section must have claims or an explicit missing-evidence reason.
     Follow research-synthesize/SKILL.md Stage 3 and Gate 3 readiness rules.
     """
   )
   

   Log: append_log(run_dir, 'section_brief_synthesis', 'agent_spawned', 'ok', 'research-synthesizer dispatched for section briefs')

3. Normalize section artifacts.

   python3 ~/.claude/skills/research-synthesize/scripts/claim_pipeline.py build-section-artifacts --run-dir "$run_dir"
   

4. Synthesizer outputs. Written to <run_dir>/synthesis/:

   • claim_bank.json -- Canonical claim state (format per references/claim_bank.contract.md in research-synthesize)
   • section_briefs/*.json -- Compact per-section briefs
   • claim_slices/*.json -- Per-section claim/source slices
   • claim_graph_map.json -- Compact claim relationship map
   • section_graph_hints.json -- Compact advisory section graph hints
   • citation_audit.md -- Citation verification results (format per references/citation_audit.contract.md)
   • gap_analysis.md -- Coverage gaps and weak areas (format per references/gap_analysis.contract.md)

5. Validate all Slice 2 artifacts.

   python3 ~/.claude/skills/research/scripts/validate_artifact.py <run_dir>/synthesis/claim_bank.json ~/.claude/skills/research-synthesize/references/claim_bank.schema.json
   python3 ~/.claude/skills/research/scripts/validate_artifact.py <run_dir>/synthesis/claim_graph_map.json ~/.claude/skills/research-synthesize/references/claim_graph_map.schema.json
   python3 ~/.claude/skills/research/scripts/validate_artifact.py <run_dir>/synthesis/section_graph_hints.json ~/.claude/skills/research-synthesize/references/section_graph_hints.schema.json
   for f in <run_dir>/synthesis/section_briefs/*.json; do python3 ~/.claude/skills/research/scripts/validate_artifact.py "$f" ~/.claude/skills/research-synthesize/references/section_brief.schema.json; done
   for f in <run_dir>/synthesis/claim_slices/*.json; do python3 ~/.claude/skills/research/scripts/validate_artifact.py "$f" ~/.claude/skills/research-synthesize/references/claim_slice.schema.json; done
   

   Log: append_log(run_dir, 'section_brief_synthesis', 'slice2_artifacts_validated', 'ok', f'Slice 2 validation: {validation_status}')

6. Run Gate 3 readiness check.

   python3 ~/.claude/skills/research-synthesize/scripts/claim_pipeline.py validate-readiness --run-dir "$run_dir"
   

   Gate 3 is blocked if readiness returns status=fail, including the failed-slice condition: any planned section has no claims and no explicit missing-evidence reason.

7. Optional diagnostics content-rules check. If diagnostics write synthesis/research_notes.md, the scanner may be run in raw mode for advisory warnings. This is not part of the canonical handoff.

   import subprocess, json
   script = Path.home() / ".claude/skills/research/scripts/check_content_rules.py"
   target = run_dir / "synthesis/research_notes.md"
   result = subprocess.run(["python3", str(script), "--target=raw", str(target)], capture_output=True, text=True)
   try:
       payload = json.loads(result.stdout)
   except json.JSONDecodeError:
       payload = {"status": "error", "violations": [], "summary": {"total": 0}, "detail": result.stderr[:500]}
   status = payload.get("status", "error")          # 'pass' | 'warn' | 'error'
   violations = payload.get("violations", [])
   total = payload.get("summary", {}).get("total", len(violations))
   log_status = 'ok' if status == 'pass' else 'warn'  # D-21: warnings never escalate to 'fail'
   detail = f"violations={total} status={status} rules=" + ",".join(sorted({v.get('rule','?') for v in violations}))
   append_log(run_dir, 'synthesis', 'content_rules_check', log_status, detail)
   # Store for Gate 3 presentation — do NOT block, do NOT raise, do NOT exit the orchestrator on violations.
   content_rules_summary = {"status": status, "total": total, "violations": violations}
   

   Scanner error handling: If the scanner exits 2 (error — file missing, path traversal, oversized), log with status='warn' (not 'fail'), record detail, and proceed. Missing research_notes.md is acceptable because diagnostics are optional.

   Non-goal (QA-04, Phase 16): QA-04 will later BLOCK Gate 3 on certain error-severity issues. Phase 11 is warn-only. Do NOT add blocking logic here.

Gate 3: Claim State Review

Objective: Detect coverage gaps and fill them with targeted collection and re-synthesis.

Steps:

1. Read gap analysis. Parse <run_dir>/synthesis/gap_analysis.md for threshold checks.

2. Evaluate gap-fill triggers. Gap-fill is triggered when ANY of these thresholds are exceeded:

   • Uncovered topics > 25% of planned subtopics
   • Isolated nodes > 20% of total graph nodes
   • Low-confidence claims (tier 4-5 sources only) > 30% of total claims

3. If gap-fill triggered: a. Keep section_brief_synthesis running while the gap-fill loop executes. b. Defer to the synthesizer — the canonical gap-fill execution path lives in the synthesizer skill, not the orchestrator.

   Note (SYNTH-11 canonical path): Gap-fill is orchestrated by the synthesizer — see research-synthesize SKILL.md § Step: Gap-Fill Loop (SYNTH-11) for the canonical execution path. The orchestrator does NOT spawn the collector directly for gap-fill; it only evaluates thresholds and updates manifest state.

   c. Maximum 1 gap-fill iteration (no infinite loops) — enforced inside the synthesizer loop.

4. If gap-fill NOT triggered: Continue to Gate 3 and mark section_brief_synthesis complete after approval.

5. CHECKPOINT GATE 3 (Post-Synthesis). Present synthesis results via one AskUserQuestion. Gate 3 reviews claim state only; it does not repeat Gate 1's output settings interview.

   Part A — Synthesis review (summary table):

   • Strongest areas (sections with most tier-1/2 citations)
   • Weakest areas (sections with fewest citations or only tier-4/5)
   • Gap-fill status ("Not triggered" or "Triggered: N additional pages, M new claims")
   • Total claims from claim_bank.json
   • Citation coverage percentage
   • Average sources per claim
   • Citation audit pass/fail summary
   • Validation warnings from validate_artifact.py
   • Content-rules violations: {total} ({status}). Rules: {comma-separated rule codes}. Advisory only — see logs/run_log.md for full detail. (from content_rules_summary computed in Phase 4 Step 4b; D-21: Gate 3 approval is NOT blocked by any violation count)

   Part A — User options:

   1. Proceed to format -- Continue to formatting with the normalized output settings approved at Gate 1
   2. Request gap-fill — Defer to the synthesizer's gap-fill loop (see research-synthesize SKILL.md § Step: Gap-Fill Loop (SYNTH-11)). The synthesizer re-invokes collection internally against gap_analysis.md targets, capped at 20 additional pages and max 1 iteration.
   3. Abort -- Cancel the run

   Output settings: Verify these normalized fields exist in manifest.json before Phase 6. For legacy or resumed runs where they are absent, write defaults:

   {
     "depth": "standard",
     "audience": "external",
     "tone": "professional",
     "render_targets": ["md", "html"]
   }
   

   section_depth_overrides is optional and defaults to {}. Per-section depth must use the same enum as global depth: summary, standard, comprehensive, or audit.

   See references/checkpoint_protocol.md Gate 3 for full specification.

   Log: append_log(run_dir, 'gate_3', 'checkpoint_shown', 'ok', f'Gate 3: {N} claims, {coverage}% coverage') Log (after user responds): append_log(run_dir, 'gate_3', 'checkpoint_response', 'ok', f'User chose: {choice}')

6. Update manifest:

   update_phase_status(manifest_path, "section_brief_synthesis", "complete")
   

Phase 6: Formatting / Report Composition

Objective: Compose the canonical Markdown report from section briefs, claim slices, and formatter-owned presentation rules.

Canonical outputs:

• output/assembly_plan.json
• output/sections/<section_id>.md
• output/sections/<section_id>.meta.json
• output/report.md
• output/formatter_audit.json

output/report.md must be useful by itself and must exist before publishing starts.

Steps:

1. Update manifest:

   update_phase_status(manifest_path, "formatting", "running")
   

2. Read output preferences & derive Quarto conditional (D-04, D-05). Read normalized output fields from manifest.json. If fields are absent (e.g., resumed run), apply defaults: depth=standard, audience=external, tone=professional, render_targets=["md", "html"]. Derive the conditional flag:

   import json
   m = json.loads(Path(manifest_path).read_text())
   depth = m.get("depth", "standard")
   audience = m.get("audience", "external")
   tone = m.get("tone", "professional")
   render_targets = m.get("render_targets", ["md", "html"])
   produce_qmd = any(target in render_targets for target in ("qmd", "html", "pdf"))
   quarto_output = (
       "both" if "html" in render_targets and "pdf" in render_targets
   

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