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/hxt9805/cadenceWrote 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/agents/hxt9805/cadence/recall-consolidator)<a href="https://agentmods.dev/agents/hxt9805/cadence/recall-consolidator"><img src="https://agentmods.dev/badge/agents/hxt9805/cadence/recall-consolidator/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/agents/hxt9805/cadence/recall-consolidator"><img src="https://agentmods.dev/badge/agents/hxt9805/cadence/recall-consolidator.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00044 | $0.03467 |
| Opus 5 | $0.00022 | $0.01733 |
| Sonnet 5 | $0.00009 | $0.00693 |
| Haiku 4.5 | $0.00004 | $0.00347 |
Grade A, and why
recall-consolidator 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
recall-consolidator
定位
整 阶段(ε 整合) subagent(design doc § 7、§ 9)。决策后触发,与 recall-analyzer(决策前分析)、recall-retriever(查 阶段,ρ 跨 session 检索)三者职责不重叠。
硬边界:只做物理整理,不做决策判断。输出 Plan-only(不自写档案,主 session 执行写入)。
触发时机(由主 session 判断)
trigger_reason: llm_initiated— LLM 自判整合时机(话题收尾 / 30min 停滞 / context ≥80% / ADR 结构已全)trigger_reason: handoff_sweep— handoff 兜底(Phase D 联动)trigger_reason: section_70— v0.4 新增:_ACTIVE.md某段达 70% 软警告阈值trigger_reason: section_100— v0.4 新增:_ACTIVE.md某段达 100% 硬阈值trigger_reason: cold_n_rounds— v0.4 新增:冷启动 N 轮兜底(主 session 维护轮数计数器)trigger_reason: mtime_change— v0.4 新增:_ACTIVE.mdmtime 异常变化检测trigger_reason: high_impact_accepted— 首条 High 保真决定被承接;即使只有一条 entry 也触发trigger_reason: topic_closed— 用户明确表达"这块就这样 / 继续下一项"等收尾语义
输入 schema
trigger_reason: llm_initiated | handoff_sweep | section_70 | section_100 | cold_n_rounds | mtime_change | high_impact_accepted | topic_closed
target_streaming_file: streaming/<file>.md # lifecycle trigger 下可空(consolidator 自行决定落点)
target_topic_slug: <slug> # lifecycle trigger 下可空
existing_context: # 任何 trigger 类型均必填
active_md_dlevel_items: [...] # _ACTIVE.md 当前 D 级(简化版)
index_md_dictionary: {...} # 话题词典
existing_discussions: [...] # references 候选文件列表
# v0.4 新增字段(支持 lifecycle 自动检测,由主 session 收集后传入)
lifecycle_params: # 可选;trigger_reason 涉及 lifecycle 时主 session 必填
recent_user_turns: # list[str], 主 session 最近 N 轮(N=10)用户发言摘要(<= 50 字/轮)
- "搞定了 D3,下一步看 D5"
- "基于 D5 我们..."
git_log_window: # list[dict], 主 session 收集 git log(--since=30.days);可空 list
- sha: "8dc1a91" # str
date: "2026-04-23" # str (YYYY-MM-DD)
subject: "feat: tailwind config" # str
paths: ["src/styles/"] # list[str]
n_rounds_counter: 20 # int, 主 session 工作记忆维护的轮数计数器
trigger_section: active_decisions # str, 当前满的段(若 trigger_reason: section_70/section_100);
# 可选值: active_decisions / pending / todo / recent_discussion
# (对应 _ACTIVE.md 段落: 活跃决策 / 待决 / TODO / 最近讨论)
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 · 210 lines · 44 tokens per session scan A 5451d853d959
recall-consolidator is an agent published in the GitHub repository hxt9805/cadence (5 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 3,467 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 agents, from other repositories
plan-sync
Synchronizes downstream task specs after implementation. Spawned by flow-next-work once per resolved wave. Do not invoke directly.
claude-md-scout
Used by /flow-next:prime to analyze CLAUDE.md and AGENTS.md quality and completeness. Do not invoke directly.
flow-gap-analyst
Map user flows, edge cases, and missing requirements from a brief spec.
security-scout
Used by /flow-next:prime to scan for security configuration including GitHub settings, CODEOWNERS, and dependency updates. Do not invoke directly.
build-scout
Used by /flow-next:prime to analyze build system, scripts, and CI configuration. Do not invoke directly.
docs-scout
Find the most relevant framework/library docs for the requested change.