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team-discovery
Discovery + naming convention reference for typed dev/SME/QA/devops team members in any workspace folder. Primary consumer: `tech-lead` (org-tier).
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Discovery + naming convention reference for typed dev/SME/QA/devops team members in any workspace folder. Primary consumer: `tech-lead` (org-tier).
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Track agent decisions, metrics, token costs, and generate reports. Use when logging task execution, tracking agent performance, or generating observability reports.
Unified ai-brain skill combining memory operations and KB query operations with promotion flow.
Bootstraps a Jira hierarchy snapshot before any multi-issue create / edit session. Disambiguates the parent input (issue key, spec URL, Confluence page URL, or `greenfield`), walks parents up (depth-cap 4), walks children down via JQL recursion (depth-cap 4, max 50 children per node), and returns a structured tree alongside the issue-type scheme and link-type metadata the caller needs to render a draft. Used primarily by `atlassian-pm`; secondary consumers (after the G2.5 retrofit) are the upstream Atlassian plugin skills `spec-to-backlog` and `capture-tasks-from-meeting-notes`. The skill **does not render**; rendering belongs to the caller (typically `atlassian-pm` rendering a markdown tree to chat).
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full, ultra. Defaults: main chat = lite, subagents = ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Generate → execute → fail → analyze → fix → repeat cycle with failure pattern recognition. Use when implementing changes that need verification and automatic recovery.
Heuristic context budget checks using file-size and count proxies — integrates with context-cache-discipline; no tokenizer introspection claims.
| name | team-discovery |
| description | Discovery + naming convention reference for typed dev/SME/QA/devops team members in any workspace folder. Primary consumer: `tech-lead` (org-tier). |
tech-lead about to assign work to project agents.discover(workspace_roots) → map<root, roles[]>For each directory root in workspace_roots, run discovery against that root’s agent pool (multi-folder workspaces repeat this per root).
Before assigning any work (for both direct tasks and plan-driven phases), for each root:
{root}/.opencode/agents/ that match team patterns (dev-*, reviewer-*, sme-*, qa-*, devops).parallelizable flag.| Agent | Scope | Parallelizable |) that you use to decide assignments and execution strategy.Output: map<root, roles[]> where roles[] is the ordered list of discovered typed agents (and their metadata from the table) for that root.
classify(touches[]) → map<root, touches_subset[]>Longest-prefix-match: For each path in touches[], assign it to exactly one root in workspace_roots by choosing the longest root path such that the touch path is contained under that root (normalized path-prefix containment). Ties or unclear containment must not be broken by arbitrary choice.
on_ambiguous: ask_user invariant: If longest-prefix-match yields a tie (two or more roots equally qualify), no qualifying root, or ambiguity after path normalization, do not infer — ask the user which root owns the touch before proceeding to dispatch.
Output: map<root, touches_subset[]> aggregating, per root, the subset of touches[] assigned to that root. Every classified touch appears under exactly one root once disambiguation is complete.
dispatch(map<root, touches[]>)Apply assignment rules when turning classified touches into agent work:
dev-1 owns frontend, frontend tasks go to dev-1.cto, or trigger team refresh (vp-onboarding) — never implement it yourself.sme-*.qa-* agents and assign test creation/update as a follow-up.Dispatch phase output schema — emit, for each (root, role) tuple the orchestrator dispatches to:
fanout_hint:
recommended_instances: <int>
partition_basis: <"path-prefix" | "module" | "service" | "single">
disjoint_groups: <int>
fanout_hint calculation rule:
classify, consider the touch set planned for a single (root, role). Partition it into disjoint touch groups (non-overlapping file sets; use path-prefix or plan-supplied module/service boundaries when applicable). Let disjoint_groups be that count.disjoint_groups >= 2: set recommended_instances = min(disjoint_groups, 8). Set partition_basis to "path-prefix", "module", or "service" according to how those groups were formed.disjoint_groups < 2: set recommended_instances: 1, partition_basis: "single", and disjoint_groups to the actual count (0 or 1).tech-lead: org-tier orchestrator; this checklist applies when tech-lead invokes project agents inside a workspace root.dev-<scope>:
dev-frontend, dev-backend, dev-infra, dev-tests.sme-<domain>:
sme-payments, sme-ml, sme-data.qa-<scope>:
qa-unit, qa-e2e, qa-manual.devops:
brain-memory-kb (mode: memory) following brain-conventions.project.<name>, project.<name>.<domain>, org.global.vp-research rather than calling docs MCPs directly.tech-lead passes each agent: