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-coding-plangit 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-coding-plan)<a href="https://agentmods.dev/skills/shiwenwen/hope-agent/ha-coding-plan"><img src="https://agentmods.dev/badge/skills/shiwenwen/hope-agent/ha-coding-plan/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-coding-plan"><img src="https://agentmods.dev/badge/skills/shiwenwen/hope-agent/ha-coding-plan.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 | $0.00041 | $0.00655 |
| Opus 5 | $0.00020 | $0.00328 |
| Sonnet 5 | $0.00008 | $0.00131 |
| Haiku 4.5 | $0.00004 | $0.00065 |
Grade A, and why
ha-coding-plan 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hope Coding Plan
Plan only when planning reduces real uncertainty. A plan is an implementation map, not a ritual and not a substitute for doing the work.
When A Formal Plan Helps
Use a plan when the task has one or more of:
- Multiple ownership boundaries or dependent steps.
- Schema, migration, persistence, compatibility, or recovery risk.
- Ambiguous architecture with several credible approaches.
- Parallel work that needs explicit isolation and synthesis.
- Named completion criteria or a long-running Goal.
Skip a formal plan for a small, obvious, reversible edit with a direct check.
Evidence First
Before planning:
- Read the user request and explicit completion criteria.
- Read
AGENTS.md, relevant architecture, current diff, and critical code. - Find a similar implementation and trace the owning path.
- Identify unknowns that materially change the design.
Do not invent files, APIs, tests, or migrations from naming alone.
Plan Contents
Each step should name:
- Outcome and behavior changed.
- Critical files or subsystem, without pretending the exact line is known when it is not.
- Dependencies and why the order matters.
- Data, compatibility, permission, concurrency, or rollback risk.
- Direct verification and completion signal.
Keep steps sized for review and progress tracking, not artificial five-minute chunks. Separate must-have work from optional follow-up.
Modes And Execution
- In Plan Mode, remain read-only and return the implementation plan.
- Outside Plan Mode, when the user asked for implementation and the next step is clear, update task progress and continue. Do not ask "shall I proceed?" merely because a plan exists.
- Create a plan document only when the user asked for one or the repository requires a durable design artifact.
/goaldefines the outcome and criteria; this skill designs the current route.- Use
ha-workflow-scriptonly when a durable dynamic script is justified.
Parallelism Decision
Mark steps parallel only when they are independent and have non-overlapping
writes or explicit worktree isolation. Use ha-multi-agent-coding for execution
strategy. A single broad investigation is not a batch fan-out.
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 · 81 lines · 41 tokens per session scan A 67832d955219
ha-coding-plan is a skill published in the GitHub repository shiwenwen/hope-agent (1,561 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 655 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.
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develop-web-game
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