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 skills add hjqcan/GoodMemory --skill using-goodmemorygit clone --depth 1 https://github.com/hjqcan/GoodMemoryWrote 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/skills/hjqcan/goodmemory/using-goodmemory)<a href="https://agentmods.dev/skills/hjqcan/goodmemory/using-goodmemory"><img src="https://agentmods.dev/badge/skills/hjqcan/goodmemory/using-goodmemory/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/skills/hjqcan/goodmemory/using-goodmemory"><img src="https://agentmods.dev/badge/skills/hjqcan/goodmemory/using-goodmemory.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 | $0.00018 | $0.00469 |
| Opus 5.5 | $0.00007 | $0.00188 |
| Sonnet 5.5 | $0.00004 | $0.00094 |
| Haiku 4.5 | $0.00002 | $0.00047 |
Grade A, and why
using-goodmemory 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 1mo 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
Using GoodMemory
GoodMemory is fallible supporting context. The user's latest instruction, the current repository, and current test or runtime evidence take precedence.
For every GoodMemory tool call, pass the current project absolute path as
cwd, using the same project-root spelling across sessions. Do not invent a
fixed workspace id or change a user-configured one. GoodMemory 0.7.5 derives
only the directory name: different paths with the same basename are not
isolated. Report that limitation if encountered. The 0.8 path-fingerprint
default separates those paths; symlink aliases, case variants, moves, and
clones deliberately get separate defaults. Explicit shared IDs opt out of
that separation. Do not migrate, copy, merge, or delete older scoped memories
without the user's explicit reconciliation direction; an empty new scope
does not imply that the old records were lost.
When earlier decisions, user preferences, prior failures, or project history
could materially affect the task, call
mcp__goodmemory__goodmemory_get_context with one concrete question. Recall is
on demand; do not call it mechanically on every turn.
If expected context is missing or a surfaced memory looks wrong, call
mcp__goodmemory__goodmemory_trace_recall with the same query. Use
goodmemory_search_index followed by goodmemory_get_records only when the
task needs exact records instead of the rendered context.
The mcp__goodmemory__goodmemory_remember tool is available after installation,
but Kimi Code approval still governs unapproved MCP calls. Persist only a
durable, explicit fact, preference, decision, reference, or blocker. Store one
clear statement per call. Use role: "user" for user-originated content and
role: "assistant" for a conclusion produced by the agent. Report whether the
result was accepted, merged, or rejected.
Never persist secrets, credentials, tokens, raw transcripts, private file contents, or unconfirmed inferences. Do not imply that session-start text automatically reads or writes memory; it only supplies these operating rules.
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.
- 1mo ago Changed · +7 lines 01546997ddee
- 1mo ago First seen · 35 lines · 18 tokens per session scan A dd51daf1f040
using-goodmemory is a skill published in the GitHub repository hjqcan/GoodMemory (19 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 469 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
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brain-page
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brain-setup
Bootstrap the Open Project Brain Standard into the current project — prefer brain init (ensure BRAIN.md, scaffold empty brain brainRoot-aware, default-wire CLAUDE.md + AGENTS.md). Optionally install a pre-commit hook and a Claude Code or Codex SessionStart hook.
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Seed a freshly-scaffolded brain with real project knowledge — on an existing (brownfield) project read the code, docs, and git log to draft the six root pages and capture key historical decisions; on a near-empty (greenfield) project interview the user. Every write goes through the brain CLI. Run it after brain-setup.
brain-ingest
The process for digesting a conversation, document, or research result, classifying it, and writing it down as brain content (a root-page update or a new/updated page) through the brain CLI.