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 l33tdawg/sage --skill sage-memorygit clone --depth 1 https://github.com/l33tdawg/sageWrote 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/l33tdawg/sage/sage-memory)<a href="https://agentmods.dev/skills/l33tdawg/sage/sage-memory"><img src="https://agentmods.dev/badge/skills/l33tdawg/sage/sage-memory/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/l33tdawg/sage/sage-memory"><img src="https://agentmods.dev/badge/skills/l33tdawg/sage/sage-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Agent Snooping · line 145 Skill accesses MCP server configuration files (mcp.json). MCP configs contain server URLs, authentication tokens, and tool definitions — reading them allows the skill to discover and potentially abuse other tool integrations.Fix: Remove all code or instructions that read MCP configuration files (mcp.json). MCP server details should be managed by the agent runtime, not read by individual skills.
- medium Rogue Agent · line 31 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- low Excessive Agency · line 116 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
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 | $0.00097 | $0.01793 |
| Opus 5 | $0.00048 | $0.00897 |
| Sonnet 5 | $0.00019 | $0.00359 |
| Haiku 4.5 | $0.00010 | $0.00179 |
Grade A, and why
sage-memory 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 10d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SAGE Memory — Persistent Brain for AI Agents
You have persistent institutional memory via SAGE — a governed, consensus-validated knowledge layer. Your memories are not a flat file. They go through BFT consensus, have confidence scores, and decay over time. Only committed memories are returned to you.
Data & Privacy Disclosure
This skill enables persistent memory storage for AI agents. By design:
- What is stored: Observations you write via
sage_turnandsage_remember— typically summaries of conversation topics, task outcomes, and user preferences. The agent decides what to store; raw conversation transcripts are NOT automatically captured. - Where data lives: All data is stored locally on your machine in
~/.sage/data/sage.db(SQLite). Nothing is sent to any cloud service. SAGE runs entirely on localhost. - Persistence: Memories survive across conversations. This is the core purpose of the skill.
Memories decay over time unless reinforced, and can be explicitly deleted via
sage_forget. - Configuration: The
.mcp.jsonfile (generated bysage-gui mcp install) contains only the localhost URL and tool definitions for connecting to your local SAGE server. It contains no credentials, API keys, or sensitive tokens. - Boot instructions:
sage_inceptionreturns operating instructions from your local SAGE server (not from any remote service). These instructions tell the agent how to use the memory tools effectively. You can customize them via the CEREBRUM dashboard athttp://localhost:8080/ui/. - Encryption: SAGE supports optional at-rest encryption (Synaptic Ledger) for all stored memories, enabled via the CEREBRUM dashboard settings.
- Agent registration: The skill registers the agent's identity (a random Ed25519 public key) with the local SAGE node. This is used for memory attribution, not for tracking.
Boot Sequence (MANDATORY)
On the first message of every new conversation, before responding to the user:
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.
- 10d ago First seen · 157 lines · 97 tokens per session scan A d6f504294a92
sage-memory is a skill published in the GitHub repository l33tdawg/sage (245 stars, last pushed 3d ago), licensed Apache-2.0. It adds 97 tokens to every session and 1,793 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
memory-audit
An entry point for reviewing and maintaining an AI agent's stored memories. It describes how to remove repetition, preserve useful reasoning, and update memories when old conclusions no longer fit.
memory-audit-belief-duel
A guided review process for conflicting beliefs or memories. It examines cases where two conclusions cannot both be true, including conflicts between a general rule and a more specific memory.
memory-audit-discoverability
A review guide for checking whether stored memories can be found at the right time. It focuses on where memories are attached, when they are triggered, whether aliases are missing, and whether a parent has too many children.
memory-audit-node-decomposition
A method for splitting an oversized knowledge note into smaller notes, each focused on one independent idea. It also explains how to keep useful core information in the original note.
memory-audit-pattern-extraction
A method for investigating repeated mistakes by comparing related memories and checking whether an earlier reminder failed. It looks at where the reminder was stored, when it was created, and whether it was strong enough to prevent the mistake.
memory-audit-dead-data-purge
A review process for identifying memories that do not change future actions. It tests whether a note contains useful, experience-based guidance or only sounds meaningful without affecting decisions.