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 MarshallEriksen-Neura/ragcode --skill ragcode-memorygit clone --depth 1 https://github.com/MarshallEriksen-Neura/ragcodeWrote 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/marshalleriksen-neura/ragcode/ragcode-memory)<a href="https://agentmods.dev/skills/marshalleriksen-neura/ragcode/ragcode-memory"><img src="https://agentmods.dev/badge/skills/marshalleriksen-neura/ragcode/ragcode-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/marshalleriksen-neura/ragcode/ragcode-memory"><img src="https://agentmods.dev/badge/skills/marshalleriksen-neura/ragcode/ragcode-memory.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 | $0.00101 | $0.01283 |
| Opus 5.5 | $0.00040 | $0.00513 |
| Sonnet 5.5 | $0.00020 | $0.00257 |
| Haiku 4.5 | $0.00010 | $0.00128 |
Grade A, and why
ragcode-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 1mo 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RagCode Memory
RagCode provides a shared, project-scoped memory that persists across sessions and is visible to every AI agent connected to the same repo over MCP. What one agent records, another agent reads. Memory is bound to the repo root, not to any agent — it does not leak across projects.
When to use
- Session start / agent switch →
memory_list(orget_context, which now attachesmemoryHints) to load key decisions and preferences before assuming a blank slate. - User states a preference, rule, or constraint →
memory_write(typeuserorfeedback). - A decision or architectural choice is made →
memory_write(typedecision). - User corrects your approach →
memory_write(typefeedback). - Before a non-trivial change →
memory_queryfor related prior decisions. - A tool response includes
memoryHintsormemorySnippets→ follow up withmemory_queryto explore.
MCP memory tools (server: ragcode)
| Action | Tool | When |
|---|---|---|
| Record a memory | memory_write |
preference stated, decision made, lesson learned |
| Search memories | memory_query |
before risky changes, when hints appear, checking past decisions |
| List / browse | memory_list |
session start, agent switch, catching up (chronological) |
| Remove | memory_delete |
memory is wrong (prefer supersede on write instead) |
CLI fallback (MCP unavailable)
ragcode memory write <topic> --type <type> --title "<title>" --body "<markdown>"
ragcode memory query "<query>" [--mode exact|semantic|hybrid] [--limit <n>]
ragcode memory list [--type <type>] [--topic <topic>] [--limit <n>]
ragcode memory delete <id> --reason "<reason>"
Prefer MCP tools when available because they preserve structured results for the calling agent. Use the CLI fallback for local diagnostics, recovery, or non-MCP agent surfaces.
Memory types
| Type | Write when | Example |
|---|---|---|
user |
user preference / trait | "prefers Go over Rust" |
feedback |
user correction / rule | "don't mock the database in tests" |
project |
project state / fact | "auth middleware rewritten for compliance" |
reference |
external pointer | "bugs tracked in Linear INGEST" |
decision |
architectural / design choice | "chose event sourcing over CRDT" |
context |
current work context | "refactoring the payment module" |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 97 lines · 101 tokens per session scan A b124b8ff06b2
ragcode-memory is a skill published in the GitHub repository MarshallEriksen-Neura/ragcode (11 stars, last pushed 2mo ago), licensed MIT. It adds 101 tokens to every session and 1,283 once invoked, about $0.0004 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
claude-md-revision
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memtrace-decision-memory
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droste-code-memory
Use when Claude needs project-aware code memory, MCP context retrieval, repository indexing, causal code search, or Droste CLI workflows. Triggers include requests to index a repo with Droste, configure droste mcp, retrieve graph-aware context, inspect caller/callee relationships, avoid blind file reads, or keep…
memtrace-index
Index a source-code repo into the Memtrace knowledge graph and poll the job to completion. Use when the user asks to index, parse, ingest, reindex, watch, or prepare a source-code repo for Memtrace analysis, when code exploration needs an index, or when searches return 0/partial results for source paths under an…
memtrace-session-continuity
Catch up on everything that changed in an indexed source-code repo since the last session, using stored session anchors and Memtrace change memory. Use when the user asks to continue, catch up, resume, see what changed while away, recover prior context, or orient at session start without guessing timestamps. Do not…