Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/jmagly/aiwgnpx agentmods add skills/jmagly/aiwg/ralph-memoryWrote 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/jmagly/aiwg/ralph-memory)<a href="https://agentmods.dev/skills/jmagly/aiwg/ralph-memory"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/ralph-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/jmagly/aiwg/ralph-memory"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/ralph-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Tool Misuse · line 218 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
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 | $0.00021 | $0.02559 |
| Opus 5.5 | $0.00008 | $0.01024 |
| Sonnet 5.5 | $0.00004 | $0.00512 |
| Haiku 4.5 | $0.00002 | $0.00256 |
Grade A, and why
ralph-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 today.
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 — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill access pattern (post-kernel-pivot, 2026.5+)
Skill names referenced in this document are AIWG skills, not slash commands. Most are not kernel-listed and cannot be invoked as
/skill-nameby the platform. Reach them via:aiwg discover "<capability>" aiwg show skill <name> --bodyOnly kernel-listed skills (
aiwg-doctor,aiwg-refresh,aiwg-status,aiwg-help,use,steward) are directly invokable as slash commands. See skill-discovery rule.
Al Memory
Inspect and manage the semantic memory that Al accumulates across loop iterations. Memory entries record lessons learned, failure patterns, and success patterns so that later iterations — and future loops on similar tasks — benefit from what earlier attempts discovered.
Natural Language Triggers
Users may say:
- "ralph memory"
- "ralph memories"
- "list ralph memory"
- "query ralph memory"
- "what has ralph learned"
- "show ralph lessons"
- "clear ralph memory"
Parameters
Subcommand (required)
One of list, query, or clear.
--loop-id (optional)
Scope the operation to a specific loop's memory. If omitted, operates on the global memory store (.aiwg/ralph/memory.json).
/ralph-memory list --loop-id abc123
--query (optional, used with query subcommand)
Text to search for in memory entries. Matches against lesson, pattern, context, and tags fields.
/ralph-memory query --query "auth mocks"
--since (optional)
Filter entries created on or after this date. Accepts ISO 8601 date (2026-04-01) or relative expressions (7d, 2w).
/ralph-memory list --since 7d
Memory Entry Schema
Each entry in .aiwg/ralph/memory.json follows this structure:
{
"id": "mem_a1b2c3",
"loopId": "abc123",
"iteration": 2,
"createdAt": "2026-04-01T10:42:00Z",
"type": "failure_pattern",
"lesson": "Auth mocks must be initialized inside beforeEach, not at module scope",
"context": "src/auth/auth.test.ts iteration 2 failure",
"tags": ["auth", "mocks", "jest"],
"confidence": 0.9
}
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.
- today Changed 7db733ae4e6c
- 1mo ago Changed d63b466fce26
- 1mo ago First seen · 298 lines · 21 tokens per session scan A c7a19937a5eb
ralph-memory is a skill published in the GitHub repository jmagly/aiwg (225 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 2,559 once invoked, about $0.0001 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
hive-mind-advanced
Queen-led collective-intelligence coordination with consensus mechanisms and persistent shared memory. Use when orchestrating a large swarm of agents under a lead coordinator, building consensus across agents, or maintaining durable cross-agent memory.
lore
Curating cross-agent knowledge and institutional memory: extracts patterns from agent journals into METAPATTERNS.md, detects knowledge decay, propagates best practices. Use for memory curation.
mega-context-manager
Manages large context windows via chunking, summarization, and retrieval. Use for: manage context, too much context, chunk document, context budget.
memory-sanitizer
Scores and filters retrieved memory against trust and poisoning risk. Use for: sanitize memory, trust score retrieval, clean knowledge graph, semantic-memory-manager.
multi-platform-messenger-bridge
Unify WhatsApp, Telegram, and future channels with shared conversation context and message-quality assessment. Use for: bridge messengers, unified chat memory, cross-platform reply, WhatsApp responder.
persistent-memory-bridge
Bridges session memory to persistent storage across conversations. Use for: remember this, persistent memory, save to memory, recall later.