Litreview — Academic Literature Orientation

Portability: Requires a Consensus MCP connection, Node.js with docx package for document generation, and (in CLI) bash_tool. Works in Claude Code CLI natively. In Claude.ai with Consensus MCP + Code Execution, the workflow is supported.

Produce a launching pad — not a finished literature review, but an orientation document that gives a researcher entering an unfamiliar field everything they need to start reading and searching with confidence. Think: what a generous colleague who knows the field would tell you over coffee.

Agent Integrity Rules (Research-Pack Convention)

Inherited from the research-pack convention; locked verbatim per PR #657's cross-skill consistency audit.

• Source discipline. Only cite Consensus-returned papers from THIS session. Training knowledge labeled [Not from Consensus — model knowledge] and excluded from cited count. Sparse results stated explicitly, never silently filled.
• Counting discipline. Three numbers tracked: searches executed / unique papers received (deduplicated) / papers cited. Every cited paper has a retrievable Consensus URL from this session. Use scripts/citation_tracker.py for deterministic counts.
• Tool constraints. Consensus per-query cap depends on plan tier. Detect at first search, report at checkpoint. Rate limit is 1 query/sec — sequential execution mandatory.
• Retry policy. On failure → wait 3s → retry once → log. After 3 consecutive failures: stop, alert user, share what was collected.
• Plan-tier detection. Parse first-search response for "Showing top 10" / "upgrade" → free tier (10/search). 20 returned → Pro (20/search). Calculate theoretical ceiling and surface at checkpoint so user can recalibrate.

See references/search_budget_allocation.md for the sequential-execution rationale + plan-tier signals.

Error Handling