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 commands/cbuntingde/kimi-memory/advisorgit clone --depth 1 https://github.com/cbuntingde/kimi-memoryWhat 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 | $0.00019 | $0.00284 |
| Opus 5 | $0.00010 | $0.00142 |
| Sonnet 5 | $0.00004 | $0.00057 |
| Haiku 4.5 | $0.00002 | $0.00028 |
Grade A, and why
advisor 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 2d 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
/kimi-memory:advisor
Trigger an advisor reflection on the active project.
Usage
/kimi-memory:advisor # full reflection sweep over the active project
/kimi-memory:advisor <topic> # focused reflection on a specific topic
What this does
Runs the advisor procedure defined in skill advisor:
memory_recall(scope: "all", ...)over the synthesised query — pulls global and project memories in one call.working_memory_getforcurrent_focusandactive_task.conversation_searchover recent turns for the same theme.- Live project context read (top-level
lsofcwd, plus relevant config files). - Produce a structured advisor response — verdict, numbered findings (each with Severity / Evidence / Action), negative space, follow-up question.
If <topic> is given, the query passed to memory_recall is the topic; without it, the advisor does a full sweep.
When not to use
If the user is asking for an action (rename this, write that), not a reflection, do not invoke /kimi-memory:advisor. The advisor skill is meant for reflection; action requests bypass it.
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.
- 2d ago First seen · 32 lines · 19 tokens per session scan A b7a4d7d57b1b
advisor is a command published in the GitHub repository cbuntingde/kimi-memory (0 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 284 once invoked, about $0.0001 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
implement-issue
Command "implement-issue" from outfitter-dev/blz, covering implement linear issue, context, important, preparation and workflow sequence.
tree-ring-update
Check for or install a verified Tree Ring Memory CLI update without changing installation scope.
tree-ring-certify
Generate Tree Ring harness or recall-quality evidence without confusing it with the full framework release suite.
tree-ring-recall
Recall durable Tree Ring Memory context before starting or resuming work.
tree-ring-status
Check receipt-backed Tree Ring harness readiness without claiming configuration is activation.
decide
Capture a strategic decision so it never gets lost or reopened accidentally.