Research

research workflow

Multi-source research workflow shipped with harnessed v2.0 (Stage ① Discuss); workflow.yaml schema bumped to harnessed.workflow.v3 in Phase v3.0-3.4 W1.1 (T3.4.W1.1 — D-09 L0 Discipline Substrate + D-05 tools_available cross-validate); phases content (01-fan-out + 02-synth) verbatim reused from v2 SHIPPED unchanged.

Phases (2)

#	Phase	Upstream	Capability	Model	Description
01	01-fan-out	web-search	(route-by-subtask)	sonnet	3 source fan-out (Tavily MCP / Exa MCP / ctx7 CLI per bundled web-search + context7 routing)
02	02-synth	gsd	gsd-discuss-phase	opus	GSD discuss-phase aggregate + dedup + reconcile

Capability refs

Sister workflows/capabilities.yaml entries:

• tavily-mcp (impl: mcp-server, default 关键词)
• exa-mcp (impl: mcp-server, 描述式 / 学术)
• ctx7 (impl: cli-tool, 库 API 文档)
• gsd-discuss-phase (synth aggregate)

Invocation

• Slash command: /research <topic> (after harnessed setup)

Routing rules (bundled web-search routing — workflows/judgments/web-search-routing.yaml)

• 描述式查询 ("找一篇对比 X 和 Y 的博客") → Exa MCP
• 学术 / 论文 → Exa MCP
• 库 / API 文档 → ctx7 CLI
• 关键词 / 时效内容 → Tavily MCP (默认)
• 抓整站 / 站点结构 → Tavily crawl/map

How to invoke

!harnessed checkpoint intent research

The banner above (when present) means this invocation is REGISTERED with the engine (an intent marker) — not yet compliant: the steps below (prompt → spawn → checkpoint complete) resolve it, and a per-turn <workflow-intent> reminder persists until they run.

The numbered sequence below is the state machine — execute it with Bash. Do NOT improvise an equivalent flow from the Overview above: freelancing bypasses the engine (no ledger, no evidence guard). harnessed gives you the spawn-ready prompt; YOU spawn the subagent with a CC-native Task / Agent tool (keeps the session responsive + lets clarification round-trips reach the user).

Do NOT pipe to harnessed run research — that is the CI/headless path (in-process SDK spawn that blocks the session inside Claude Code).

1. Bash: harnessed prompt research --task "$ARGUMENTS" --json → parse {prompt, max_iterations, model}.
2. Spawn a CC-native subagent (Task / Agent tool) with that prompt + model, wrapped in the ralph-loop plugin: /ralph-loop "<prompt>" --max-iterations <max_iterations> --completion-promise "COMPLETE". If the plugin is absent, use the native goal gate instead (Claude Code 2.1.139+ / Codex): /goal "this subtask is delivered: the subagent's final output contains verbatim <promise>COMPLETE</promise>; or stop after <max_iterations> turns" then spawn the subagent and let the goal evaluator drive re-spawns until it clears. If /goal is unavailable too, self-loop: spawn → check output for <promise>COMPLETE</promise> → re-spawn with prior output appended (up to max_iterations). Set the goal only at the leaf subtask level — /goal is single-slot per session and a nested goal overwrites the outer one. Delivery contract: use a BLOCKING Agent/Task call — only a blocking call returns the subagent's final text as your tool result. A named/background teammate's final message is DISCARDED by the platform; if you must run it that way, instruct the agent to write its findings to a file (and read it back) or SendMessage them to the main session — otherwise the COMPLETE promise and the findings never reach you.
3. If the output contains STATUS: NEEDS_CLARIFICATION + a question list: STOP, relay them verbatim via AskUserQuestion, append the answers to the spec, then re-spawn the same sub.
4. On <promise>COMPLETE</promise>: Bash harnessed checkpoint complete research --summary "<one-line>". The evidence guard runs here (fail-CLOSED): if a declared artifacts_expected file is missing it exits non-zero — re-spawn to produce it before treating the sub as done.

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