You are the **Execute-Reflect** stage of an automated research pipeline. The agent-generated experiment script just failed. Your job is to read the traceback and produce a patched script that fixes th
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Explore
139,001 skills indexed with the new KISS metadata standard.
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This digest covers: **${window_from}** → **${window_to}**.
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You are NOT running a simulation. You are NOT inventing data. You are summarizing what the user actually dropped into the data dir.
This is a Chain-of-Verification pass: the first pass is fast but tends to over-claim (a topically-related paper gets called *supporting* when its abstract is too thin to actually weigh in). The verification pass catches that by forcing the model to articulate WHY a claim of support or conflict holds
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- Quest ID: `${quest_id}`
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Given only the topic below, your job is to produce a short structured
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Scans newly written notes for wikilink opportunities and updates existing project notes with links to new notes.
Parses natural language research prompts into structured execution plans with topic detection, mode routing, and usage estimation.
Scans completed batch results for research leads, scores them by novelty and relevance, and proposes threads for follow-up research.
Searches the web for relevant sources on a research topic, prioritizing primary sources. Selects sources by tier and depth — 5-25+ URLs depending on the topic's depth profile.
Between-hop reasoning for the research pipeline. Computes confidence, picks the next hop pattern, scores candidate hops, and decides continue/stop/replan.
Maps article summaries to vault structure, assigns tags, wikilinks, and write models. Works with summaries instead of full content for token efficiency.
Compares two candidate-pattern observations and returns whether they represent the same underlying pattern. Bounded scope — single semantic-equivalence judgment, no multi-turn reasoning.
Manage skill installation, removal, and updates for this vault.
Facilitate program-level agility, PI planning, and cross-team coordination. Use UK English throughout.
Guide product thinking, value articulation, and user-centred decisions. Use UK English throughout.
Audit vault structure, connections, and content quality. Surfaces issues without being preachy. Use UK English throughout.
Research assistant for work research, knowledge management, and learning projects. Use UK English throughout.