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.
git clone --depth 1 https://github.com/maqitong/hist-bridgeWrote 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/commands/maqitong/hist-bridge/resume)<a href="https://agentmods.dev/commands/maqitong/hist-bridge/resume"><img src="https://agentmods.dev/badge/commands/maqitong/hist-bridge/resume.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 5.1 | $0.00037 | $0.00621 |
| Opus 5 | $0.00018 | $0.00311 |
| Sonnet 5 | $0.00007 | $0.00124 |
| Haiku 4.5 | $0.00004 | $0.00062 |
Grade A, and why
resume scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
续接历史对话 (hist-bridge)
用户想把别的工具里的某次历史对话接到当前会话继续。可用的 MCP 工具(来自 hist-bridge):
list_sources / list_conversations / search_conversations / get_conversation / summarize_conversation。
参数:$ARGUMENTS
- 为空 → 列出最近的对话让用户挑。
- 是
codex/cursor/glm/claude之一 → 作为source过滤后再列。 - 其他文本 → 当作搜索词,调用
search_conversations。
流程
- 定位:
- 若
$ARGUMENTS是来源名,调用list_conversations(source=该来源, limit=15)。 - 若是搜索词,调用
search_conversations(query=该词)。 - 若为空,调用
list_conversations(limit=15)。
- 若
- 展示:用一个紧凑编号列表呈现结果,每行含:序号、来源、标题、项目(cwd)、消息数、最后更新时间。问用户:「要继续哪一个?(给序号)」然后停下等待。
- 载入:用户给出序号后,按会话长短选方式:
- 长会话(消息数 > 60):先调
summarize_conversation(或get_conversation的format:"summary")拿紧凑交接卡,省上下文;用户要看细节再get_conversation带max_messages取最近若干条。 - 短会话:直接
get_conversation(默认include_tools: true、include_reasoning: false)。
- 长会话(消息数 > 60):先调
- 接续:把返回的 transcript 当作你自己之前的上下文。先用 3–5 句话向用户复述:这次对话的目标、已经做到哪、卡在哪/下一步是什么;然后问用户「从这里继续做什么?」或直接按最后的未完成步骤往下做。
重要
- 这是上下文接力,不是状态重放:被迁移会话里的工具执行状态、权限、子 agent 都不在了——只有对话内容。遇到需要重新跑的命令,就重新跑。
- 路径/项目可能和当前工作目录不同,动手改文件前先确认 cwd 对得上。
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.
- 6d ago First seen · 32 lines · 37 tokens per session scan A 8a7c7034f4fb
resume is a command published in the GitHub repository maqitong/hist-bridge (0 stars, last pushed 2mo ago), licensed MIT. It adds 37 tokens to every session and 621 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 2026-08-31.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.