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 samrusani/AliceMemory --skill alice-correction-loopgit clone --depth 1 https://github.com/samrusani/AliceMemoryWrote 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/samrusani/alicememory/alice-correction-loop)<a href="https://agentmods.dev/skills/samrusani/alicememory/alice-correction-loop"><img src="https://agentmods.dev/badge/skills/samrusani/alicememory/alice-correction-loop/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/samrusani/alicememory/alice-correction-loop"><img src="https://agentmods.dev/badge/skills/samrusani/alicememory/alice-correction-loop.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 | $0.00026 | $0.00406 |
| Opus 5 | $0.00013 | $0.00203 |
| Sonnet 5 | $0.00005 | $0.00081 |
| Haiku 4.5 | $0.00003 | $0.00041 |
Grade A, and why
alice-correction-loop 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 9d 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
Alice Correction Loop
Goal
Apply corrections through Alice review tools and confirm that recall/resumption behavior updates accordingly.
Trigger Cues
Use this skill when the user asks:
- this is outdated or wrong
- correct this memory
- supersede or mark stale
Required MCP Tools
mcp_<alice_server>_alice_memory_reviewmcp_<alice_server>_alice_memory_correct- Verification:
mcp_<alice_server>_alice_recallormcp_<alice_server>_alice_resume
<alice_server> is usually alice_core.
Workflow
- Fetch review queue or detail with
alice_memory_review. - Select correction action:
confirmeditdeletesupersedemark_stale
- Apply correction with
alice_memory_correct. - Re-run
alice_recalloralice_resumeto verify behavior changed. - Report both the correction action and the observed post-correction result.
Tool Call Templates
mcp_alice_core_alice_memory_review({"status":"correction_ready","limit":10})
mcp_alice_core_alice_memory_correct({"continuity_object_id":"<uuid>","action":"supersede","replacement_title":"<title>","replacement_body":{}})
Output Contract
Always include:
- corrected object ID
- action applied
- reason
- post-correction verification result
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.
- 9d ago First seen · 64 lines · 26 tokens per session scan A 37a08f40d0e0
alice-correction-loop is a skill published in the GitHub repository samrusani/AliceMemory (3 stars, last pushed 12d ago), licensed MIT. It adds 26 tokens to every session and 406 once invoked, about $0.0001 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.
Other skills, from other repositories
plur-create-engrams
Create or improve PLUR engrams from conversations, documents, decisions, observations, and explicit preferences. Use for memory extraction, engram authoring, or reviewing proposed memories, including global, scoped, pinned, retrieved, and provisional knowledge. Ordinary use of existing memories does not require this…
plur-memory
Persistent learning for AI agents. Open engram format. Your agent learns from corrections, remembers across sessions, and transfers knowledge across domains.
plur-session-end
Extract durable learnings at the end of a session. Saves corrections, preferences, and codebase patterns as engrams — nothing ephemeral, nothing sensitive.
plur-memory
Your memory stays on your machine. No cloud, no tracking, no API key. PLUR makes your OpenClaw remember — and shares that memory with every other tool you use.
remnic-memory-workflow
Shared memory workflow for Claude Code agents connected to Remnic — recall before acting, observe during work, remember at the end. Trigger phrases include "what do you remember about", "save this for later", "any context from last time".
remnic-recall
Search Remnic memories by natural-language query. Trigger phrases include "what do you remember about", "recall anything on", "have we discussed".