ha-code-review
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.
git clone --depth 1 https://github.com/shiwenwen/hope-agent /tmp/ha-code-review && cp -r /tmp/ha-code-review/skills/ha-code-review ~/.claude/skills/ha-code-reviewSKILL.md
# Hope Code Review Review as a maintainer. The default action is to inspect and report, not to edit. Only repair findings when the user asks for fixes. ## Establish The Target Identify exactly what is under review: - Staged, unstaged, and untracked changes. - A commit or commit range. - Branch or PR diff against the correct base. - A named file or subsystem. Read the changed files and enough surrounding code, tests, architecture, and callers to understand behavior. Do not report unrelated pre-existing issues as findings introduced by the change. ## Two-Phase Reasoning ### Discovery Search from multiple relevant angles: - Correctness, state transitions, error paths, and data loss. - Security, privacy, permissions, and unsafe trust boundaries. - Concurrency, cancellation, retry, persistence, and recovery. - Performance on realistic hot paths. - Cross-module contracts, compatibility, and missing regression coverage. Generate candidate issues without committing to them. ### Verification For each candidate: 1. Trace a concrete scenario that reaches the changed behavior. 2. Confirm the issue was introduced or exposed by the review target. 3. Check whether surrounding guards or tests already prevent it. 4. Keep it only if the author would likely fix it once informed. Prefer no finding over a speculative or stylistic finding. For a small, low-risk diff, one reviewer can perform both phases. For a broad or high-risk diff, an independent read-only reviewer may verify candidates. Do not mandate a fixed number of Agents or two review passes for every change. ## Finding Bar A finding must be discrete, actionable, and materially affect correctness, security, privacy, performance, maintainability of a shared contract, or regression protection. It must explain the triggering scenario and impact. Do not report: - Personal naming or formatting preferences. - Broad rewrites without a concrete failure. - Problems outside the changed behavior. - Test requests that do not protect a meaningful contract. ## Output - Lead with findings ordered by severity. - Use the smallest useful file/line or function reference. - State scenario, impact, and fix direction concisely. - Emit inline review directives only when requested and only for actionable changed-line findings. - If there are no actionable findings, say so directly and mention only real residual verification gaps. Do not bury findings under a summary. Do not modify code in review-only mode. ## Verification Cost Use cheap targeted checks only when they materially improve confidence. Follow repository instructions and do not run broad suites solely to make the review look thorough. ## Smoke Prompts - "Review all my uncommitted changes." - "Check this commit for behavioral regressions." - "Review this PR and leave inline comments only for real issues."
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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 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.
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger phrases: data stores, database path, sessions.db, memory.db, logs.db, background_jobs.db, knowledge index, where is data stored, query app data, 查数据库, 数据存储, 会话记录在哪, 记忆库在哪.