/memory-view
Open the memory visualiser in the browser — a health check on the memory store: is it working, what is wrong, and what to run to fix it. Run manually via /memory-view.
$ npx -y skills add baodq97/tencentdb-agent-memory --skill memory-view --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/memory-view
Context preview
The summary Claude sees to decide when to auto-load this skill.
Open the memory visualiser in the browser — a health check on the memory store: is it working, what is wrong, and what to run to fix it. Run manually via /memory-view.
SKILL.md
memory-view.SKILL.mdname: memory-view
description: Open the memory visualiser in the browser — a health check on the memory store: is it working, what is wrong, and what to run to fix it. Run manually via /memory-view.
disable-model-invocation: true
Memory View
`tmem view` **is** the implementation. This skill only starts it, hands over the URL, and reads the feedback back. Never reimplement a metric, a route or a second way in — if this file and the CLI can disagree, trust the CLI and fix this file.
**User-triggered only.** The page renders raw auto-captured prompts from *every* project the user has opened. Do not open it on your own initiative.
1. Start the server
tmem view # live, opens on the health check
tmem view --query "<a prompt>" # opens on "Try a prompt", tracing that query
tmem view --static # pin the snapshot so numbers can't move mid-read
Run it with **`run_in_background: true`** — it is a long-running server and it must survive the turn. Flags: `tmem view --help`.
It prints:
tmem view — live mode, pid 363161, /home/dev/.memory-tencentdb
snapshot s2-12f246b66c7e650b (175.6 ms)
Opens on "Try a prompt", tracing: how do I like tests written
Open this URL verbatim — the session key is required:
http://localhost:45261/?key=3b7d1f4a9c2e05846d1fba37c9e0d215&view=trace&q=how+do+I+like+tests+written
Session dir: /home/dev/.memory-tencentdb/view
Stop with Ctrl-C, or: kill 363161 (auto-stops after 240m idle)
Give the user that URL **complete and verbatim**. The key gates every route: a reconstructed `localhost:<port>` gets a 403, and localhost alone was never the privacy boundary here. If you missed stdout, the same details are in `~/.memory-tencentdb/view/server-info.json`.
2. End your turn
They look and click. You cannot see the page.
3. Next turn: read their feedback
tail -20 ~/.memory-tencentdb/view/events.jsonl
One JSON object per line — anything posted to `/api/events`, each line carrying `kind`, its target, the `snapshotId` it was seen against, and `at`/`timestamp`:
{"kind":"gap.ack","gapId":"vectors_missing:global","snapshotId":"s2-27bb076f40017b87","at":"2026-08-03T03:45:00.097Z","timestamp":1785728700}Expect it to be thin or absent: the rebuilt screens read the live channel but post no interactions today, so **what they typed in the terminal is the feedback** and this file is at most a supplement. Don't infer silence from an empty file. Then act: fix the store, re-consolidate, adjust the persona. Be honest about the boundary of "live": the page updates within seconds, but you only act when your next turn runs.
Why open it
It answers *"is my memory healthy, and what do I do about it?"* — a verdict first, then the problems ranked, each one a sentence with its figure inside it and a command that fixes it. The numbers that matter are computed by the **same functions the recall hook uses**, so the page cannot flatter the system. Four screens, `?view=`:
- `health` — the verdict, the ranked problems, the totals (default)
- `memories` — search and read what was actually saved
- `about-you` — which standing rules reach the assistant, and which never do
- `trace` — try a prompt, see the literal block the assistant would receive
A real reading: 52 stores, 5 560 records, 219 scenes, 76 problems — of 47 always-apply rules only 13 reach the assistant each session and 33 never do, and 23 projects have an index that exists but is empty, so they silently fall back to keyword-only search while passing any file-exists check.
That is the point. A screen reading "5 560 records" while the assistant receives three lines per turn would win an argument it should lose, so **a total never renders without its gap** — don't report one without the other either.
Files
Session output lives in `~/.memory-tencentdb/view/` (`--root` moves it) — **never inside a repo**, so running the visualiser can't leave an untracked directory behind. `--snapshot` writes `snapshot-<id>.json` there and exits without serving, for a pinned baseline or a scripted diff.
Read more
name: memory-view description: Open the memory visualiser in the browser — a health check on the memory store: is it working, what is wrong, and what to run to fix it. Run manually via /memory-view. disable-model-invocation: true
Memory View
`tmem view` **is** the implementation. This skill only starts it, hands over the URL, and reads the feedback back. Never reimplement a metric, a route or a second way in — if this file and the CLI can disagree, trust the CLI and fix this file.
**User-triggered only.** The page renders raw auto-captured prompts from *every* project the user has opened. Do not open it on your own initiative.
1. Start the server
tmem view # live, opens on the health check tmem view --query "<a prompt>" # opens on "Try a prompt", tracing that query tmem view --static # pin the snapshot so numbers can't move mid-read
Run it with **`run_in_background: true`** — it is a long-running server and it must survive the turn. Flags: `tmem view --help`.
It prints:
tmem view — live mode, pid 363161, /home/dev/.memory-tencentdb snapshot s2-12f246b66c7e650b (175.6 ms) Opens on "Try a prompt", tracing: how do I like tests written Open this URL verbatim — the session key is required: http://localhost:45261/?key=3b7d1f4a9c2e05846d1fba37c9e0d215&view=trace&q=how+do+I+like+tests+written Session dir: /home/dev/.memory-tencentdb/view Stop with Ctrl-C, or: kill 363161 (auto-stops after 240m idle)
Give the user that URL **complete and verbatim**. The key gates every route: a reconstructed `localhost:<port>` gets a 403, and localhost alone was never the privacy boundary here. If you missed stdout, the same details are in `~/.memory-tencentdb/view/server-info.json`.
2. End your turn
They look and click. You cannot see the page.
3. Next turn: read their feedback
tail -20 ~/.memory-tencentdb/view/events.jsonl
One JSON object per line — anything posted to `/api/events`, each line carrying `kind`, its target, the `snapshotId` it was seen against, and `at`/`timestamp`:
{"kind":"gap.ack","gapId":"vectors_missing:global","snapshotId":"s2-27bb076f40017b87","at":"2026-08-03T03:45:00.097Z","timestamp":1785728700}Expect it to be thin or absent: the rebuilt screens read the live channel but post no interactions today, so **what they typed in the terminal is the feedback** and this file is at most a supplement. Don't infer silence from an empty file. Then act: fix the store, re-consolidate, adjust the persona. Be honest about the boundary of "live": the page updates within seconds, but you only act when your next turn runs.
Why open it
It answers *"is my memory healthy, and what do I do about it?"* — a verdict first, then the problems ranked, each one a sentence with its figure inside it and a command that fixes it. The numbers that matter are computed by the **same functions the recall hook uses**, so the page cannot flatter the system. Four screens, `?view=`:
- `health` — the verdict, the ranked problems, the totals (default)
- `memories` — search and read what was actually saved
- `about-you` — which standing rules reach the assistant, and which never do
- `trace` — try a prompt, see the literal block the assistant would receive
A real reading: 52 stores, 5 560 records, 219 scenes, 76 problems — of 47 always-apply rules only 13 reach the assistant each session and 33 never do, and 23 projects have an index that exists but is empty, so they silently fall back to keyword-only search while passing any file-exists check.
That is the point. A screen reading "5 560 records" while the assistant receives three lines per turn would win an argument it should lose, so **a total never renders without its gap** — don't report one without the other either.
Files
Session output lives in `~/.memory-tencentdb/view/` (`--root` moves it) — **never inside a repo**, so running the visualiser can't leave an untracked directory behind. `--snapshot` writes `snapshot-<id>.json` there and exits without serving, for a pinned baseline or a scripted diff.
Four-layer long-term memory (L0 Conversation → L1 Atom → L2 Scene → L3 Persona) for Claude Code, inspired by Tencent/TencentDB-Agent-Memory. Fully local — no external Gateway, no paid API, no Python.
Repo: baodq97/tencentdb-agent-memory
Other skills on tencentdb-agent-memory.
- /contrib-consolidate
Internal phase — consolidate a contributor's L1 atoms into an L3 persona (11 dimensions). Invoked by contrib-profile / `/contrib build`.
Open skill - /contrib-ingest
Internal phase — extract L1 contributor atoms from a GitHub subject's raw activity. Invoked by contrib-profile / `/contrib ingest`.
Open skill - /contrib-profile
Orchestrator for Contributor Intelligence. Trigger when the user pastes a GitHub profile/repo URL or handle and asks to profile, analyze, learn from, or study an engineer (EN/VI — "phân tích người này", "học từ người này"). Runs add→ingest→build→playbook end-to-end via the
Open skill - /contrib-synthesize
Internal phase — synthesize learnable playbooks, cross-engineer common capabilities, and you-vs-role-model comparisons from built personas. Invoked by contrib-profile / `/contrib playbook|compare`.
Open skill - /memory-consolidate
Consolidate L1 memory atoms into L2 scene blocks and L3 persona. Invoked by the memory-consolidator agent, or manually via /memory-consolidate.
Open skill - /memory-seed
Extract L1 memory atoms from Claude Code conversation history. Run manually via /memory-seed.
Open skill

