Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
中文简介正在准备中。当前显示原文说明。
Periodic trust sweep of persistent memory and durable knowledge notes - re-verifies environment-dependent claims against the live environment, stamps last_verified + confidence, and proposes archiving drifted entries
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Prevent the stale-but-confident failure mode: a memory or knowledge note that was correct when written ("the webhook lives at X", "the board ID is Y") silently drifts after the environment changes, yet still ranks high at recall and gets acted on.
The system move (adapted from "From Model Scaling to System Scaling: Scaling the Harness in Agentic AI", Gu, UC Berkeley, arXiv:2605.26112): make trust a runtime decision, not a property of the stored item. Re-verify against the live environment, and keep per-entry last_verified and confidence as first-class fields so future recalls can weigh trust.
/memory-hygieneSweep two stores:
05-knowledge/** files whose claims reference the environment (paths, URLs, IDs, tool names).A partial sweep ("just the reference entries") is fine when asked.
For each entry, split its claims into two buckets:
| Bucket | Examples | Action |
|---|---|---|
| Environment-dependent | file/dir paths, repo names, branch names, channel IDs, board IDs, URLs, API endpoints, cron/routine IDs, CLI names, version numbers, "X lives at Y" | Verify against the live environment |
| Preference / judgment | tone rules, formatting rules, "never do X", people facts, strategy context | No environment check possible; verify only for internal contradiction with newer entries |
ls / test -e. Skills, commands, and agents named in an entry must still exist at the stated path.curl -sI); flag 404 or redirect-to-login.gh repo view, git ls-remote when cheap.unverifiable-cheaply and leave confidence untouched.Never spend more than ~1 minute per entry. This is hygiene, not an investigation. Unverifiable ≠ drifted.
After checking an entry, update its frontmatter metadata: block in place (do not touch body text unless fixing a verified-wrong fact):
metadata:
type: reference
last_verified: 2026-07-10
confidence: high # high = verified now | medium = unverifiable cheaply | low = partially drifted
confidence: high, stamp date.medium), stamp date.confidence: low only if unsure the fix is complete.Scan-until-done over the entry list with a per-entry budget guard (~1 min). The deterministic verifier is the environment itself (test -e, curl, gh) — never the agent's own recollection of whether something "should" still exist. Human escalation: all deletions/archives.
Write one report per sweep to 01-daily/YYYY-MM-DD-memory-hygiene.md, structured around four evolution questions:
verified / unverifiable / drifted / propose-archive counts, plus deltas vs the previous sweep report (the drift trend is the longitudinal signal one-shot checks miss).End the report with a Propose archive section (explicit list, one line of evidence each) and a Waiting on you line if anything needs a decision.
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
中文简介正在准备中。当前显示原文说明。
Quick capture of raw thoughts with intelligent domain classification and competitive intelligence extraction
中文简介正在准备中。当前显示原文说明。
Run one task through the V-model verification loop: CP-2 plan → CP-3 build → CP-3v component verify → CP-4 integration verify (full lane) → CP-5 acceptance. The worker never grades its own homework; evidence rows trace back to AC-n. Opt-in: invoke with /closed-loop or by asking for the closed loop, proper verification, or an evidence trail. Ordinary work does not run this.
中文简介正在准备中。当前显示原文说明。
Deep-dive 7-day analysis across all data sources for weekly reviews, board prep, and strategic planning
中文简介正在准备中。当前显示原文说明。
Autonomous content pipeline - scout announcements in your field, triage by trend momentum and personal angle, produce posts/blogs/videos in your voice with ledger-based dedup, hard volume caps, and screenshot-verified publishing
中文简介正在准备中。当前显示原文说明。
Create user stories with duplicate checking across any project tracker (Linear, GitHub Issues, Jira)
中文简介正在准备中。当前显示原文说明。