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 agentmods add skills/hashgraph-online/awesome-codex-plugins/error-experience-librarynpx skills add hashgraph-online/awesome-codex-plugins --skill error-experience-librarygit clone --depth 1 https://github.com/hashgraph-online/awesome-codex-pluginsWrote 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/hashgraph-online/awesome-codex-plugins/error-experience-library)<a href="https://agentmods.dev/skills/hashgraph-online/awesome-codex-plugins/error-experience-library"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/error-experience-library.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00046 | $0.00627 |
| Opus 5 | $0.00023 | $0.00313 |
| Sonnet 5 | $0.00009 | $0.00125 |
| Haiku 4.5 | $0.00005 | $0.00063 |
Grade A, and why
error-experience-library 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 today.
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.
What it actually says
Error Experience Library
用途
- 自动从错误中学习：每次错误解决后，将根因-解决方案对沉淀到经验库
- 历史查询：遇到错误时，先查询经验库是否有已知模式
- 反馈更新：根据实际解决结果更新经验库的成功/失败计数
- 持续优化：高成功率的模式优先推荐，低成功率的模式标记为警示
核心操作
1. 记录错误模式 (record)
当成功解决一个错误后，将经验沉淀：
错误类型: [如: build_error, runtime_error, type_error]
错误信息片段: [可匹配的关键词]
根因: [为什么会发生]
解决方案: [如何修复]
语言/框架: [如: python, typescript, react]
文件模式: [可选，如: **/*.ts]
标签: [如: null-check, async, import]
2. 查询错误模式 (search)
遇到错误时，先查询经验库：
查询关键词: [错误信息或类型]
返回: 按成功率排序的匹配模式
3. 提供反馈 (feedback)
解决方案验证后，更新成功率：
pattern_id: 模式ID
success: true/false
存储位置
- 本地：
~/.claude/memory/error_experience/patterns/ - 每个模式一个 JSON 文件，包含 success_count 和 failure_count
触发信号
- 遇到 build error 或 runtime error 时
- 成功解决错误后，应该记录到经验库
- 使用
/error-lookup命令快速查询
配合约束
- 记录时尽量包含具体的错误信息片段，便于后续匹配
- 根因要具体，不要只写"代码有 bug"
- 解决方案要包含具体的修改内容
- 标签要准确，便于按领域查询
- 验证结果后务必提供反馈，更新成功率
相关工具
scripts/lib/memory_store.py- 底层存储接口search_error_patterns()- 查询模式save_error_pattern()- 保存新模式record_pattern_feedback()- 记录反馈
命令接入
/error-lookup <关键词>- 查询历史错误模式/error-record- 记录当前错误的解决方案/error-feedback <pattern_id> <success|failure>- 更新反馈
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today First seen · 82 lines · 46 tokens per session scan A 487b124d6a86
error-experience-library is a skill published in the GitHub repository hashgraph-online/awesome-codex-plugins (924 stars, last pushed today), licensed Apache-2.0. It adds 46 tokens to every session and 627 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 2026-09-05.
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