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 Stijnman/grok-custom-skills --skill memory-sanitizergit clone --depth 1 https://github.com/Stijnman/grok-custom-skillsWrote 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/stijnman/grok-custom-skills/memory-sanitizer)<a href="https://agentmods.dev/skills/stijnman/grok-custom-skills/memory-sanitizer"><img src="https://agentmods.dev/badge/skills/stijnman/grok-custom-skills/memory-sanitizer/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/stijnman/grok-custom-skills/memory-sanitizer"><img src="https://agentmods.dev/badge/skills/stijnman/grok-custom-skills/memory-sanitizer.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 | $0.00035 | $0.00354 |
| Opus 5 | $0.00017 | $0.00177 |
| Sonnet 5 | $0.00007 | $0.00071 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
memory-sanitizer 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 11d 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 Sanitizer
When to Use
- User says sanitize memory or task matches this capability
- User says trust score retrieval or task matches this capability
- User says clean knowledge graph or task matches this capability
Workflow
- For each memory entry, score trust 0-10 (source, recency, user-confirmed).
- Discard entries below 4 unless user explicitly references them.
- Prefix 4-6 scores with [unverified memory] when citing.
- Flag contradictions between entries; prefer user-confirmed.
Integrations
semantic-memory-managerknowledge-graph-builderagentic-uncertainty-quantifier
Error Handling
| Failure | Response |
|---|---|
| All entries low trust | Ask user to confirm facts before proceeding. |
| Contradictory memories | Present both; ask user to resolve. |
Gotchas
- Delegate retrieval to semantic-memory-manager when installed.
Example
Input: User request matching triggers above. Output: Structured result per workflow with integrations invoked as needed.
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.
- 11d ago First seen · 48 lines · 35 tokens per session scan A b4ddf2c43ddc
memory-sanitizer is a skill published in the GitHub repository Stijnman/grok-custom-skills (9 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 354 once invoked, about $0.0002 per session on Opus 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.
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faf-expert
Expert in .faf (Foundational AI-context Format) files for persistent project context. Use when working with .faf files, project DNA, CLAUDE.md bi-sync, faf-cli commands, MCP server configuration, or AI-readiness scoring (0-100%). Updated for v2.8.0 Tool Visibility System.
goal-meta-skill
A guide for turning an unclear or complex task into a reviewable /goal instruction for coding agents.
cordis-plugin-sofagent-evolve
A sofagent add-on for recording lessons from completed work and refining future agent instructions.
knowledge-base
Retrieves and updates project-specific prompt knowledge from comparison evidence and user feedback. Use only for prompt analysis or post-comparison learning within a Rashomon evaluation.