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 joshuaswarren/remnic --skill remnic-memory-workflowgit clone --depth 1 https://github.com/joshuaswarren/remnicWrote 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/joshuaswarren/remnic/remnic-memory-workflow)<a href="https://agentmods.dev/skills/joshuaswarren/remnic/remnic-memory-workflow"><img src="https://agentmods.dev/badge/skills/joshuaswarren/remnic/remnic-memory-workflow/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/joshuaswarren/remnic/remnic-memory-workflow"><img src="https://agentmods.dev/badge/skills/joshuaswarren/remnic/remnic-memory-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.00738 |
| Opus 5 | $0.00027 | $0.00369 |
| Sonnet 5 | $0.00011 | $0.00148 |
| Haiku 4.5 | $0.00005 | $0.00074 |
Grade A, and why
remnic-memory-workflow 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use
Use this skill as the default playbook whenever Claude Code picks up a task that could benefit from prior context, or when the user explicitly asks the agent to remember or recall something. The individual remnic-recall, remnic-remember, remnic-search, remnic-entities, and remnic-status skills implement the detailed steps.
Triggers:
- New ticket, branch, or task begins.
- "What do you remember about …"
- "Save this for later."
- Long-running turn produces a durable outcome worth capturing.
Inputs
- Current user request (natural language).
- Optional: active project path, ticket number, branch name.
- Optional: topic keywords surfaced earlier in the session.
Procedure
- Recall first. Call
remnic_recallwith a concise natural-language query built from the user's request. Pull 3–8 results and filter for relevance. - Mention relevant memories briefly to the user when they change the plan; otherwise use them as quiet context.
- Observe during work. For significant tool results (Write/Edit/MultiEdit, Bash exits, test output) call
remnic_observeso Remnic keeps its ambient context fresh. - Deep search on demand. If
remnic_recallmissed something the user insists exists, fall back toremnic_lcm_searchwith a more literal phrase. - Browse entities. When the user names a project, person, or concept, call
remnic_entity_getto pull facts and relations. - Remember at the end. Before ending the turn, store durable decisions, preferences, and findings via
remnic_memory_store.
Efficiency plan
- One broad recall beats several narrow ones.
- Skip recall for trivially local tasks (formatting, arithmetic, mechanical refactors).
- Reuse recall results within the same turn.
- Store each memory once; update rather than duplicate.
Pitfalls and fixes
- Pitfall: Storing transient state. Fix: Only store facts with durable value.
- Pitfall: Leaking secrets. Fix: Redact credentials and tokens before calling
remnic_memory_store. - Pitfall: Flooding the user with recalled context. Fix: Summarize in 1–3 bullet points.
- Pitfall: Forgetting the final write. Fix: Make "remember the decision" the last step of any non-trivial turn.
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 · 62 lines · 54 tokens per session scan A 7157fbe70062
remnic-memory-workflow is a skill published in the GitHub repository joshuaswarren/remnic (195 stars, last pushed 2d ago), licensed MIT. It adds 54 tokens to every session and 738 once invoked, about $0.0003 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.
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