Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/attune-ai/main/.agents/skills/recall/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/recall)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/recall"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/recall/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/smart-ai-memory/attune-ai/recall"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/recall.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 | $0.00046 | $0.01915 |
| Opus 5.5 | $0.00018 | $0.00766 |
| Sonnet 5.5 | $0.00009 | $0.00383 |
| Haiku 4.5 | $0.00005 | $0.00192 |
Grade A, and why
recall 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recall
IMPORTANT: Start your response by telling the user:
Recall — Searching cross-session memory for findings related to your query.
What It Does
/recall is the on-demand companion to the automatic SessionStart
recall. It searches TWO stores and labels results by source:
- Session findings — stashed by the Stop hook across past sessions (file backend by default; Redis AMS when connected).
- The lessons corpus — the repo's accumulated engineering
lessons, retrieved via
attune.lessons.LessonsIndex(query mode only; lessons have no recency, so the no-query mode skips them).
- With a query (
/recall AMS event loop): both stores; session findings keyword + recency ranked, lessons by retrieval score. - No query (
/recall): the most recent session findings for this project.
Pick Your Transport First
Route by what YOUR client can actually do — never assume the universal Python recipe works everywhere (in a sandboxed provider it selects an unwritable file tier or a blocked socket):
- MCP available — the
session_memory_*tools appear in your tool list (Codex, Claude Code with the attune MCP server, any MCP client): use them. They execute host-side, outside your sandbox, and carry the full contract (sanitization, cwd scoping, TTL). Never run in-process Python for memory from a sandboxed client, and never substitute rawredis_memory_storefor finding capture — capture goes throughsession_memory_captureonly. - Trusted host context, no MCP — you can run repo Python directly (Claude Code Bash, the CLI, lifecycle hooks): the Python snippets below remain valid.
- Neither — report honestly that cross-session memory is unavailable in this client (no MCP tools, no trusted Python). Do not fake results and do not claim the backing service is down — you cannot know that from here.
MCP Tools
| Tool | Use |
|---|---|
session_memory_recall |
Semantic search (args: query, optional top_k, cwd) |
session_memory_recent |
Newest findings, no query (args: top_k, cwd) |
session_memory_capture |
Stash a finding (args: content, type, tags, cwd) |
session_memory_forget |
Delete by full record id (args: ids, cwd) |
session_memory_status |
Caller-scoped backend status |
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 · 179 lines · 46 tokens per session scan A c617648ed17b
recall is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (11 stars, last pushed yesterday), licensed Apache-2.0. It adds 46 tokens to every session and 1,915 once invoked, about $0.0002 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 2026-08-31.
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