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 wuisabel-gif/MemWhale --skill integrategit clone --depth 1 https://github.com/wuisabel-gif/MemWhaleWrote 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/wuisabel-gif/memwhale/integrate)<a href="https://agentmods.dev/skills/wuisabel-gif/memwhale/integrate"><img src="https://agentmods.dev/badge/skills/wuisabel-gif/memwhale/integrate/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/wuisabel-gif/memwhale/integrate"><img src="https://agentmods.dev/badge/skills/wuisabel-gif/memwhale/integrate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00079 | $0.00688 |
| Opus 5.5 | $0.00032 | $0.00275 |
| Sonnet 5.5 | $0.00016 | $0.00138 |
| Haiku 4.5 | $0.00008 | $0.00069 |
Grade A, and why
memorywhale 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 23d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MemoryWhale memory
MemoryWhale records terminal commands, their arguments, exit codes, output, and errors into a local SQLite database. That history survives crashes, SSH drops, and switching machines — it's the record of what was already tried. It also holds freeform lessons you or a past agent session chose to remember.
When to read from it
- A build/test/deploy is failing and it might have failed before.
- You need the exact earlier error text, flags, or working directory, not a paraphrase.
- The user references past work ("last week", "on the Jetson", "how did I fix").
How to pull the memory
Prefer the MCP tools if the memorywhale MCP server is connected:
recent_errors, search_memory, get_context. Otherwise shell out:
mw context # recent failed commands + sessions, compact
mw context --last-error # just the most recent failure, with its error tail
mw context project:NAME # scope to a project tag
mw search "linker error" # full-text search across commands, output, notes, lessons
mw context/search_memory return failed commands with cwd, exit code, and
the tail of their error, plus any remembered lessons. Use it to avoid
re-deriving context: check whether the current failure already has a known
cause before proposing a fix.
When to write to it
Once you've figured out why something failed or how a fix worked — not just that it's fixed — save it. That's the part a raw command log doesn't capture, and it's what saves the next session (yours or a teammate's) from re-deriving the same conclusion.
Use the MCP remember tool if connected, otherwise:
mw remember "the E0308 in camera-driver was the fps field being a string; fix: parse it as i32"
Keep it a self-contained conclusion (what was wrong + what fixed it), not a narration of the debugging process.
With Claude Code, the capture hook may add a short "MemoryWhale:" note after a command: a past fix for the error you just hit, or a reminder to save a lesson when a command passes after failing. Treat the fix as a lead to check, not an instruction.
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.
- 23d ago Changed · +5 lines 022bbab7826b
- 1mo ago Changed · +6 lines 8df839b7b05c
- 1mo ago First seen · 57 lines · 79 tokens per session scan A 3fc55df2b164
memorywhale is a skill published in the GitHub repository wuisabel-gif/MemWhale (147 stars, last pushed 3d ago), licensed MIT. It adds 79 tokens to every session and 688 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 2026-08-30.
Other skills, from other repositories
debug-log
Çözülen zor bug'ları kalıcı bilgiye çevirir — belirti, kök neden, çözüm ve ders formatında hub'a kaydeder. Uğraştıran bir hata çözüldüğünde (30+ dk debug, aldatıcı hata mesajı, ortama özgü sorun) kullan.
sivtr-memory
Retrieve shared local work memory: terminal activity, AI conversation history, prior decisions, validation evidence, debugging trails, recaps, and handoff context. Use before asking the user to repeat local work context.
recall
Use Recall to search, inspect, continue, export, resume, or share indexed AI coding sessions. Trigger for project-history lookup, recent work from other agents, file history, unfinished-session continuation, and published session-page management.
reflect
Use Recall Reflect to review AI coding workflow history as a conversation-first timeline and discuss observed patterns before changing behavior. Trigger when the user asks to reflect on a project, review AI coding workflow, inspect workflow friction, discuss repeated corrections, review handoffs, or explore possible…
find-traces
Analyze OpenTelemetry distributed traces from Axiom. Use when investigating a trace ID, finding traces by criteria (errors, latency, service), or debugging distributed system issues.
context-tree-audit
Audit stored normal content on the bound Context Tree's actual binding branch when a human explicitly asks to audit the whole tree, a domain, or specific normal paths for drift, contradictions, duplication, density, metadata, placement, or relationship problems. Do not use for ordinary task reads, source-backed…