ha-multi-agent-coding
Hope-native multi-agent coding orchestration: fan out only independent valuable work, enforce bounded scope and isolation, consume structured results progressively, steer or cancel, and keep synthesis with the main Agent.
git clone --depth 1 https://github.com/shiwenwen/hope-agent /tmp/ha-multi-agent-coding && cp -r /tmp/ha-multi-agent-coding/skills/ha-multi-agent-coding ~/.claude/skills/ha-multi-agent-codingSKILL.md
# Hope Multi-Agent Coding Use multiple Agents when parallel evidence or implementation meaningfully outweighs coordination cost. Do not make delegation the default for complex-looking work. ## Fan-Out Decision Good candidates: - Similar independent investigations across modules. - Distinct review angles with a shared structured output. - Independent implementations with non-overlapping ownership. - A bounded set of alternatives that the main Agent will compare. Keep work with one Agent when: - The task is small or one search path is likely sufficient. - Steps depend on prior results. - Agents would edit the same files or shared generated state. - A single broad investigation needs coherent context. - Coordination, token, or merge cost exceeds expected parallel gain. ## Define Each Child Contract Provide: - One concrete objective and bounded scope. - Relevant context already known by the parent. - Allowed and forbidden actions. - File ownership or read-only isolation. - Required output schema, evidence, and stop condition. - Verification expected from the child. Do not re-delegate the entire parent assignment to one child. Children do not own final user communication or Goal closure. ## Isolation - Prefer `shared_read_only` for research, discovery, and verification. - Use separate worktrees for independent writes. - If writes cannot be isolated, serialize them or assign mutually exclusive file ownership. - Permission mode, protected paths, approval surfaces, and tool restrictions remain runtime-enforced. A child prompt cannot grant access. ## Bounded Execution Set explicit limits for fan-out count, depth, turns, tokens, and time. Respect runtime queues and backpressure. Never recursively create Workflow runs or an unbounded Agent tree. ## Progressive Control The main Agent may choose based on task needs: - Consume the first useful results and adapt (`waitAny` / checkpoint). - Query status without consuming output. - Read one structured result, then steer or cancel remaining work. - Add a follow-up child when new evidence changes the decomposition. - Wait for all children only when a true barrier is required. Background work must not block the user's conversation. Use runtime completion or checkpoint injection rather than polling loops. ## Synthesis The main Agent must: 1. Check which children completed, failed, timed out, or returned no evidence. 2. Resolve conflicts using source evidence, not majority vote. 3. Preserve partial failures and uncertainty. 4. Integrate or review writes in the parent worktree deliberately. 5. Run parent-level verification for the combined outcome. "All Agents completed" is orchestration state, not a user result. Do not finish until the parent has synthesized and answered the actual task. ## Workflow Boundary This skill decides delegation strategy. Use `ha-workflow-script` when execution must be durable, replayable, observable, or script-controlled. Simple bounded subagent work does not require a Workflow; a Workflow may use this strategy without surrendering its runtime contracts. ## Smoke Prompts - "Investigate these six independent modules in parallel and synthesize." - "Use staged child results; do not wait for every slow reviewer." - "Decide whether this implementation should stay single-Agent or use worktrees."
>
Use when the user asks to draft, polish, translate, or reply to an email. Produces a clean draft with subject line, greeting, body, and sign-off, plus a pre-send self-check.
Use when the user mentions 飞书 / Feishu / Lark workspace operations: docx (云文档) read/write, bitable (多维表格) records / views / dashboards, drive (云盘) upload/download, wiki (知识库) link resolution, approval (审批) instance create/cancel/query, calendar (日历) event create/list/update + attendees, contact (联系人) user/department lookup, hire (招聘) job/talent/application listing. Trigger on phrases like 'OKR 周报', '把这份文档发到飞书云盘', '给团队拉个评审会议', '查 [姓名] 的联系方式', '撤销那条审批', '/wiki 链接', or any request that mentions a feishu / lark URL / token (doxcn.../bascn.../wikcn.../boxcn.../om_...).
Hope Agent browser automation — the standard `status → tabs → snapshot → act` loop, stale-ref recovery rules, and what to do when login / 2FA / captcha / camera-prompt / dialog blocks progress. Load this skill whenever you reach for the `browser` tool. Trigger on: user asks the agent to open / control / click / scrape / log into / verify something in a web app ('open X and click Y', '打开 X 然后点击 Y', 'log into my Gmail', 'scrape this page', 'fill out the form on X'); user reports a flow that requires real browser context (cookies, JS-rendered content, OAuth).
Hope-native review of uncommitted, staged, commit, branch, or PR changes: discover concrete regressions, independently verify candidates, and report actionable findings first without speculative noise.
Hope-native baseline for implementing, fixing, refactoring, and maintaining code: inspect the repository first, protect user changes, keep scope narrow, and finish with direct evidence.
Hope-native implementation planning for non-trivial code changes: ground the plan in repository evidence, order dependencies, name critical files and risks, define verification, then continue execution when allowed.
Hope-native local-first data analysis and Artifact reporting. Use for CSV/XLSX analysis, KPI readouts, metric diagnosis, product/business analysis, data-quality review, dashboards, charts, analytical reports, 数据分析, 指标诊断, 数据质量, 分析报告, or when the user wants a shareable offline HTML/ZIP/Markdown/PDF result. Produces the versioned AnalysisArtifactV1 contract and registers it with the artifact tool; never guesses missing data or requires public web deployment.