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.
git clone --depth 1 https://github.com/varienos/agentic-workflowWrote 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/commands/varienos/agentic-workflow/memorize.skeleton)<a href="https://agentmods.dev/commands/varienos/agentic-workflow/memorize.skeleton"><img src="https://agentmods.dev/badge/commands/varienos/agentic-workflow/memorize.skeleton/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/commands/varienos/agentic-workflow/memorize.skeleton"><img src="https://agentmods.dev/badge/commands/varienos/agentic-workflow/memorize.skeleton.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 | $0.00000 | $0.01303 |
| Opus 5.5 | $0.00000 | $0.00521 |
| Sonnet 5.5 | $0.00000 | $0.00261 |
| Haiku 4.5 | $0.00000 | $0.00130 |
Grade A, and why
memorize.skeleton 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 27d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memorize — Session Learning Recorder
Analyzes important learnings from the current session and records them to memory. Usage:
/memorize,/memorize "learnings for the auth module"
Philosophy
Not everything is worth recording. Memory stores information that will be ENCOUNTERED AGAIN later. Pass the "I would have been faster if I had known this" test: will an agent facing the same situation later benefit from this?
Step 1 — Session Analysis
Analyze all interactions in the current conversation:
- Which files were worked on?
- Which problems were encountered?
- How were they solved?
- Were there unexpected situations?
- Did the user state special preferences?
Step 2 — Classify Learnings
Categories Worth Recording
| Category | Description | Example |
|---|---|---|
| Solution Pattern | Solution found for a specific problem | "Prisma N+1 solved with include" |
| Project Convention | Hidden rule discovered in the codebase | "Services always return Result<T>" |
| Pitfall | Easy to fall into, hard to find | "Memory leak when useEffect cleanup is missing" |
| Architecture Decision | Why it is structured this way | "Auth is per service, not at the gateway" |
| User Preference | Working preference stated by the user | "PR descriptions should be in English" |
| Tool Usage | Usage detail for a specific tool | "backlog CLI --set flag needs quotes" |
Things NOT Worth Recording
- General programming knowledge (everyone knows)
- One-off operations (will not repeat)
- Personal information (not project-specific about the user)
- Overly specific debug steps (meaningless without context)
- Information already in documentation
Step 3 — Create Memory Files
3.1 — File Format
Create a separate memory file for each learning:
Invariant Path Rule:
- Write into the Agentbase .claude/memory/ directory
- Do not write memory files into Codebase
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.
- 27d ago Changed · -1 lines a79334f749af
- 1mo ago First seen · 156 lines · 0 tokens per session scan A 728ae697b937
memorize.skeleton is a command published in the GitHub repository varienos/agentic-workflow (59 stars, last pushed 28d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,303 tokens. 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 commands, from other repositories
memory
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ingest
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memory
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review
A command for reviewing code with security checks and a check for compliance with MX tags. The available description does not explain what MX tags are.
pr-enhance
Command "pr-enhance" from ruvnet/ruflo, covering pr-enhance, usage, options, examples and enhance pr.
review-code
Read ALL memory bank code rules + best practices, check the files that changed, and APPLY fixes so they adhere. The active counterpart to /scan (which is read-only). Use after an AI session, before commit, to make changed files compliant.