Answer any question about the codebase, team, or organization using Unblocked context. Use when the user asks a question that code alone can't answer — why something exists, who worked on it, what was decided, how systems connect, or what happened during an incident. Synthesizes context from PRs, docs, chat, issues, incidents, and code into a cited answer. Renders diagrams for architecture questions, tables for tradeoffs, and always cites sources.
Context-aware code review that gathers organizational context before analyzing a diff. Use when reviewing a PR or branch where team conventions, prior art, and related changes matter. Hydrates the reviewer with team patterns via context_research, categorizes findings, and presents them for discussion.
Ticket enrichment that gathers organizational context and produces findings to attach to an issue before work begins. Use when enriching a Jira, Linear, GitHub, or any issue tracker ticket with related PRs, code paths, historical context, and prior attempts. Two modes: engineering (code-focused) and support (user/product-focused). Does not solve the ticket — only enriches it.
Evidence gathering that correlates signals from code, infrastructure, and organizational sources for an incident. Use when an alert, incident, or symptom needs cross-system evidence collection — building a correlation timeline, evaluating hypotheses against evidence, and presenting findings in plain language. Presents the evidence report to the user. Does not plan the investigation or recommend fixes.
Headless code review that gathers organizational context before analyzing a diff. Use when an agent performs a PR or branch review autonomously — in CI/CD, as a sub-agent, or via `claude -p`. Hydrates the reviewer with team conventions, prior art, and related PRs via context_research, then categorizes findings as pattern mismatches, reinvented wheels, convention drift, or missing context.
Headless ticket enrichment that gathers organizational context and attaches findings to an issue before work begins. Use when an agent should autonomously enrich a Jira, Linear, GitHub, or any issue tracker ticket with related PRs, code paths, historical context, and prior attempts. Two modes: engineering (code-focused) and support (user/product-focused). Does not solve the ticket — only enriches it so whoever picks it up starts with full context.
Headless evidence gathering that correlates signals from code, infrastructure, and organizational sources for an incident. Use when an agent should autonomously gather cross-system evidence — building a correlation timeline, evaluating hypotheses against evidence, and synthesizing findings. Output is structured for AI agent consumption. Does not plan the investigation or recommend fixes.
Headless incident investigation planning that formulates multiple hypotheses from organizational context. Use when an agent should autonomously plan an investigation for an alert, incident ticket, or symptom description — correlating the affected system's architecture, recent changes, and historical incidents to produce ranked hypotheses with parallel investigation tracks. Output is structured for AI agent consumption. Does not gather evidence or recommend fixes.