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-debuggit 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-debug)<a href="https://agentmods.dev/skills/shiwenwen/hope-agent/ha-debug"><img src="https://agentmods.dev/badge/skills/shiwenwen/hope-agent/ha-debug.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.00731 |
| Opus 5 | $0.00022 | $0.00365 |
| Sonnet 5 | $0.00009 | $0.00146 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
ha-debug 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 8d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hope Debug
Debug from evidence, not from the first plausible explanation. This is a decision process, not a mandatory four-stage ceremony.
1. Characterize The Failure
Capture the strongest available evidence:
- Exact symptom, expected behavior, and observed behavior.
- Reproduction steps, failing command, stack trace, log range, or persisted state.
- Whether it is deterministic, intermittent, platform-specific, data-specific, or timing-sensitive.
- Recent relevant diffs, dependency/config changes, and known-good boundary.
If reproduction is unsafe or requires unavailable credentials, characterize it from logs, fixtures, state, and code paths. State the evidence gap explicitly.
2. Bound The Fault
Trace the smallest credible path through inputs, state transitions, persistence, concurrency boundaries, and outputs. For multi-component systems, compare what crosses each boundary rather than adding broad instrumentation everywhere.
Common high-value checks:
- Stale or duplicated persisted state.
- Error swallowing, fallback, retry, cancellation, and timeout paths.
- Async ordering, locks, process boundaries, and late results.
- Platform, locale, permission, path, and environment assumptions.
- Mismatch between source-of-truth data and UI projection.
3. Rank Falsifiable Hypotheses
Keep one or two active hypotheses. For each, write:
- Why it explains the evidence.
- What observation would disprove it.
- The cheapest discriminating check.
Run the discriminating check before editing when practical. If a tiny, obvious fix is itself the cheapest safe experiment, keep it reversible and inspect the result before broadening scope.
4. Fix The Root Cause
- Patch the smallest ownership boundary that restores the contract.
- Avoid subsystem rewrites before the fault is proven.
- Preserve unrelated user work and existing public behavior.
- Add defense-in-depth only when it covers a demonstrated adjacent failure, not as speculative cleanup.
After two failed fix attempts, stop patching variants. Re-read the original evidence, challenge the shared assumption, and narrow the boundary again.
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.
- 8d ago First seen · 87 lines · 43 tokens per session scan A 619d49e11a9a
ha-debug is a skill published in the GitHub repository shiwenwen/hope-agent (1,553 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 731 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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