Hope Agent is a cross-device personal AI assistant that remembers context, uses tools, and continues working toward goals through dynamically organized workflows. Individuals use it from desktop, web, server, or messaging environments to manage projects, knowledge, designs, and long-running tasks. The catalogue add-ons extend its agent workflows and capabilities.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add shiwenwen/hope-agent --skill ha-code-reviewgit clone --depth 1 https://github.com/shiwenwen/hope-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/shiwenwen/hope-agent/ha-code-review)<a href="https://agentmods.dev/skills/shiwenwen/hope-agent/ha-code-review"><img src="https://agentmods.dev/badge/skills/shiwenwen/hope-agent/ha-code-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shiwenwen/hope-agent/ha-code-review"><img src="https://agentmods.dev/badge/skills/shiwenwen/hope-agent/ha-code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.00669 |
| Opus 5 | $0.00020 | $0.00334 |
| Sonnet 5 | $0.00008 | $0.00134 |
| Haiku 4.5 | $0.00004 | $0.00067 |
Grade A, and why
ha-code-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Trace a concrete scenario that reaches the changed behavior.
- Confirm the issue was introduced or exposed by the review target.
- Check whether surrounding guards or tests already prevent it.
- 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 90 lines · 40 tokens per session scan A e5ddc68f0919
ha-code-review is a skill published in the GitHub repository shiwenwen/hope-agent (1,561 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 669 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
code-review
A code-review workflow that sends local changes to a separate reviewer for checks on correctness, style, performance, security, tests, and design.
memory
Use this skill when Pioneer should proactively use durable memory or recalled context: decide whether memory can improve a turn, answer from remembered user/project facts, request memory tools, search/list/get stored memories, save durable preferences or project decisions, forget memories, audit or clean up memory, or…
subagents
Use this skill when Pioneer should delegate current-turn work to attached task-backed subagents, coordinate parallel child agents, wait for child results, review/revise/accept/cancel/detach attached work, or synthesize accepted child results into the parent answer.
tasks
Use this skill when Pioneer should create, inspect, update, reschedule, pause, resume, troubleshoot, or configure delivery for durable tasks, scheduled tasks, recurring tasks, interval tasks, cron jobs, background work, or existing task state.
code-review
A code-review guide for inspecting changes in the current workspace. It defines how to choose the review scope, obtain the code diff, delegate review work, and return the findings.
aetox-code-review
รีวิวโค้ดที่เปลี่ยน หา bug จริงก่อน merge, ความปลอดภัย (injection, auth, path traversal), ประสิทธิภาพ (N+1, ความซับซ้อน, index), ความถูกต้อง, edge case, concurrency, error handling รับ PR/diff/ไฟล์ ทริกด้วย "รีวิวก่อนเมิร์จ" หรือ "โค้ดนี้ปลอดภัยไหม".