docs(subagents): research now verifies; document Dolores

Canonical prompts had drifted from the deployed code. research gained a third
fan-out member (doc_lookup / mnemosyne) and its synthesizer was promoted from a
merge step into the researcher: it holds dolores and time, and escalates to a
real browser when a source comes back blocked, a URL looks guessed, or a
load-bearing claim needs a live check.

All three variants updated — personal, work, engineering — preserving each
team's memory framing and the personal variant's node-schema and Cypher
sections. The engineering variant was a near-stub; it now carries the same
structure as the others.

Dolores had no canonical prompt anywhere in koios despite running on all three
teams. Added prompts/{personal,work,engineering}/subagents/dolores.md from the
deployed instruction, unchanged — this closes a documentation gap rather than
altering her behaviour. Her prompt stays deliberately narrow: she is handed one
page and one question at a time, and browser tradecraft is what she is for.

docs/*/subagents.md gain a dolores entry with delegation guidance, including
the task-sizing rule that keeps callers from handing her multi-site errands.
Work's research Runtime line said "TBD, port to be assigned" — mentor's
research has been live on 24250 for some time.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

docs/work/subagents.md

@@ -8,30 +8,55 @@ Subagents are runtime processes exposed as MCP tools. The canonical prompt text
 
 ### research
 
-**Purpose:** Answer a question by querying both the public web and Robert's Neo4j memory in parallel, then synthesizing one integrated response with conflicts flagged and suggested memory updates surfaced.
+**Purpose:** Answer a question by searching the public web, Robert's Neo4j memory, and the Mnemosyne document library in parallel — then judging what came back and verifying it against live pages where it matters.
 
-**Composition:** `fast.parallel` of three sub-agents:
-- `web_search` — argos
-- `memory_lookup` — neo4j (read-only); framed around work-team node types (clients, opportunities, engagements, decisions, technologies, contacts)
-- `synthesizer` — merges the two reports, flags conflicts, suggests which node type a memory update would belong on
+**Composition:** `fast.parallel` of four sub-agents:
+- `web_search` — argos; reports blocked/empty/JS-shell results rather than papering over them
+- `memory_lookup` — neo4j (read-only)
+- `doc_lookup` — mnemosyne (read-only); passes `library_type` when the domain is clear
+- `synthesizer` — the researcher: reads all three reports, escalates to **dolores** for a real browser when a source is blocked, a URL looks guessed, or a load-bearing claim needs a live check
 
-**Tools:** argos, neo4j_cypher
+**Tools:** argos, neo4j_cypher, mnemosyne, dolores, time
 
 **When to delegate:**
-- Pre-meeting prep on a client or contact — what's already in the graph, plus current public information (recent news, funding, leadership changes)
-- Opportunity qualification where the answer might exist in prior `Decision` or `Technology` nodes AND on the public web
-- "What do we already know about X, and what's the current public information on it?" against the work pipeline
-- When a lead wants memory-aware research without burning its own context on parallel queries
+- A question where being wrong has a cost — the answer gets verified, not just retrieved
+- "What do I already know about X, and what's actually true about it now?"
+- Anything whose answer goes stale: prices, availability, dates, current status
+- When the lead wants memory-aware research without burning its own context
 
 **When NOT to delegate:**
-- Quick web lookups where memory isn't relevant — use argos directly
-- Pure graph queries on a known client or opportunity — query Neo4j directly
-- AWS architecture design questions — use `aws-sa`
-- Deep library/framework/API research — use the work-team `tech_research` subagent (below).
+- Quick web lookups where memory isn't relevant — use Argos directly
+- Pure graph queries where the web isn't needed — query Neo4j directly
+- Operating a browser as the goal itself (a form, a flow, a screenshot) — use `dolores`
 
 **Prompt:** [prompts/work/subagents/research.md](../../prompts/work/subagents/research.md)
 
-**Runtime:** TBD — copy of engineering's `kottos/agents/research.py` with the work-team memory framing applied. Port to be assigned when wired up.
+**Runtime:** `mentor/agents/research.py` — port 24250
+
+---
+
+### dolores
+
+**Purpose:** Operate a real web browser. Dolores drives a headed Chromium on the RDP desktop host via Playwright — she reads live pages, fills forms, works through multi-step flows, and returns screenshots.
+
+**Composition:** single `fast.agent`.
+
+**Tools:** playwright
+
+**When to delegate:**
+- A task that genuinely requires a browser: a form to fill, a login flow, a multi-step navigation
+- A page Argos can't render — JS-heavy, client-rendered, cookie-walled
+- When the *visual itself* is the deliverable ("what does this page look like")
+
+**When NOT to delegate:**
+- Finding an answer — that's `research`, which will call Dolores itself when a source needs verifying
+- Anything the cached snippet already answers
+
+**Sizing the task:** one page and one question at a time. She has no view of the caller's goal, so a task spanning several sites — or one whose later steps depend on what earlier ones turn up — comes back thin or wrong. Chain the steps from the calling side instead.
+
+**Prompt:** [prompts/work/subagents/dolores.md](../../prompts/work/subagents/dolores.md)
+
+**Runtime:** `mentor/agents/dolores.py` — port 24253
 
 ---