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 3awny/qship --skill qmemorygit clone --depth 1 https://github.com/3awny/qshipWrote 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/3awny/qship/qmemory)<a href="https://agentmods.dev/skills/3awny/qship/qmemory"><img src="https://agentmods.dev/badge/skills/3awny/qship/qmemory/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/3awny/qship/qmemory"><img src="https://agentmods.dev/badge/skills/3awny/qship/qmemory.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 | $0.00064 | $0.01827 |
| Opus 5.5 | $0.00026 | $0.00731 |
| Sonnet 5.5 | $0.00013 | $0.00365 |
| Haiku 4.5 | $0.00006 | $0.00183 |
Grade A, and why
qmemory 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.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Item Memory Storage
Review the current conversation and extract generic, reusable lessons worth carrying into future sessions. Save them through Claude Code's auto-memory system so they show up automatically next time.
This skill is the manual end-of-conversation companion to the auto-memory system already described in your system prompt. Defer to that system as the source of truth — this skill just walks you through running it deliberately at session end. If anything here conflicts with the auto-memory rules in the system prompt, the system prompt wins.
Where memories live
Use the auto-memory directory for the current project — do not hardcode a path. It's the directory referenced as the memory location in the system prompt's auto-memory section (typically ~/.claude/projects/<project-slug>/memory/). The index file is MEMORY.md in that same directory.
If you cannot determine the project's memory directory from the system prompt, ask the user before writing anything.
What to extract (and what to skip)
Worth saving — anything that will help a future session that doesn't have this conversation's context:
- Corrections — the user told you to stop doing X, or to do Y instead. Save the rule + the why.
- Validated non-obvious successes — you took an unusual approach, the user confirmed it ("yes, exactly", "perfect, keep doing that"), and a future Claude wouldn't guess this from the code alone. These are easy to miss because they're quiet — watch for them.
- Project facts with a deadline or owner — merge freezes, who's driving an initiative, the reason behind a rewrite. Convert relative dates to absolute (
Thursday→2026-05-07). - External system pointers — "bugs live in Linear project X", "oncall watches dashboard Y".
- User profile signals — role, expertise, what framings they prefer.
Do NOT save (these are derivable or ephemeral, even if the user asks):
- Code patterns, file paths, conventions, architecture —
grep/Readthe codebase next time. - Git history, recent commits, who-changed-what —
git log/git blameis authoritative. - Debug solutions or fix recipes — the fix is in the commit, the reasoning in the message.
- Anything already in
CLAUDE.md. - In-progress task state, current conversation context, today's TODO list.
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 First seen · 145 lines · 64 tokens per session scan A 21d3b076239e
qmemory is a skill published in the GitHub repository 3awny/qship (2 stars, last pushed 3mo ago), licensed MIT. It adds 64 tokens to every session and 1,827 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
memory
Activate this skill when starting a new session, beginning a new task, saving knowledge, recalling past decisions, or after completing significant work. Also activate on errors to log them for pattern analysis. Relevant when the user asks about memory, past context, lessons learned, decisions history, project…
maintaining-context
Use at the start of any substantial task and whenever you make a non-obvious decision or learn a reusable fact — keeps .yoke/context/ (PROJECT, DECISIONS, KNOWLEDGE) the durable source of truth so fresh-context work never drifts.
alive:session-history
Revive sessions (quick or heavy), browse, and search — 'what happened recently?', 'find the session where we discussed X', 'revive yesterday's session'. For single-session recall and multi-session browsing. If the human needs to merge multiple sessions into one working context or detect conflicts between parallel…
knowledge-priming-refiner
Facilitate a structured conversation to create a project-specific knowledge base document. Produces a knowledge-base.md that primes AI with the project's tech stack, architecture, trusted sources, and project structure. Use when the user says 'set up knowledge base', 'prime the project', 'onboard AI', 'create…
context-optimizer
A context-management assistant for long conversations with AI agents. It helps organize the information an agent needs when conversations become large or contain conflicting details.
alive-inbox
Scan 03Inbox/ for unrouted files, present routing suggestions.