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/adamwstauffer/shidlerWrote 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/adamwstauffer/shidler/memory-hygiene)<a href="https://agentmods.dev/commands/adamwstauffer/shidler/memory-hygiene"><img src="https://agentmods.dev/badge/commands/adamwstauffer/shidler/memory-hygiene.svg" alt="Measured on agentmods" 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 | $0.00322 |
| Opus 5 | $0.00000 | $0.00161 |
| Sonnet 5 | $0.00000 | $0.00064 |
| Haiku 4.5 | $0.00000 | $0.00032 |
Grade A, and why
memory-hygiene 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 8d 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
/memory-hygiene — Validate + organize the memory store
Run periodically (every ~10 sessions), before a /compact, or after a session that wrote new memories. Keeps the Claude auto-memory store (MEMORY.md index + single-fact *.md files) coherent as it grows.
What this command does
Follow the methodology in .claude/skills/memory-hygiene/SKILL.md end-to-end:
- Run
.claude/skills/memory-hygiene/validate_memory.pyagainst~/.claude/projects/C--GitHub-shidler/memory. - Triage findings — errors (broken index links, missing
name:frontmatter) get fixed; warnings (orphans, dangling[[refs]], missing description/type) get fixed opportunistically or explained. - If
MEMORY.mdis crowded (>~25 lines), group the index by type (feedback_*together,project_*together) without editing fact content.
Guardrails
- MEMORY.md is an index — one line per memory, pointer only, never fact content.
- A dangling
[[ref]]is only a bug if it's a typo or points at a non-memory doc; deliberate forward-refs are allowed. - Delete memories that are wrong; don't store what the repo already records.
Output
The validator's findings, triaged, ending with:
Memory hygiene: <N> memories / <E> errors / <W> warnings — <fixed / routed>.
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.
- 8d ago First seen · 23 lines · 0 tokens per session scan A a6182d99101c
memory-hygiene is a command published in the GitHub repository adamwstauffer/shidler (10 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 322 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 2026-08-31.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.