recall

recall is a skill for Claude Code, Codex from rmarquesa/agentmemory-offline. It costs 61 tokens per session (484 once invoked), scanned A, original, Apache-2.0.

A memory search skill for finding observations, past sessions, and learnings about a topic. It searches stored information using several matching methods.

In plain words
What is it for?
Use it to answer questions such as what was previously done, whether a topic came up before, or what a past session learned.
Why use it?
It helps recover earlier decisions and context without guessing what happened in past work.

Skill for Claude CodeCodex

Part of the agentmemory plugin — 15 skills, 2 commands, 12 hooks, 1 MCP server shipped together

Install

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.

agentmods
npx agentmods add skills/rmarquesa/agentmemory-offline/recall
Any agent
npx skills add rmarquesa/agentmemory-offline --skill recall
Clone the repo
git clone --depth 1 https://github.com/rmarquesa/agentmemory-offline

Made for: Claude Code, Codex.

Or install agentmemory, the plugin that ships this one along with the rest of its 15 skills, 2 commands, 12 hooks, 1 MCP server.

Wrote 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.

agentmods badge for recall

README.md
[![agentmods](https://agentmods.dev/badge/skills/rmarquesa/agentmemory-offline/recall.svg)](https://agentmods.dev/skills/rmarquesa/agentmemory-offline/recall)
Your own site
<a href="https://agentmods.dev/skills/rmarquesa/agentmemory-offline/recall"><img src="https://agentmods.dev/badge/skills/rmarquesa/agentmemory-offline/recall.svg" alt="Measured on agentmods" height="20"></a>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 484 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00061 $0.00484
Opus 5 $0.00030 $0.00242
Sonnet 5 $0.00012 $0.00097
Haiku 4.5 $0.00006 $0.00048

Measured 5d ago against content hash 718ab72876b5, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 5d 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.

plugin/skills/recall/SKILL.md · 61 lines

What it actually says

The user wants to recall past context about: $ARGUMENTS

Quick start

memory_smart_search { "query": "jwt refresh token rotation", "limit": 10 }

Expected output:

2 results across 2 sessions.
[importance 8] decision · "Rotate refresh tokens on every use" (session 7f3a9c21)
[importance 5] code · "limit.ts counts per-IP" (session b21d004e)

Why

Only surface what the tool returned. Never fabricate an observation, a session id, or an importance score. If nothing comes back, say so.

Workflow

  1. Call memory_smart_search with the user's text as query and limit: 10. Pass project when the user scopes to a specific repo.
  2. Group results by session.
  3. For each observation show its type, title, and narrative.
  4. Lead with the high-signal observations (importance >= 7).
  5. If zero results, suggest 2-3 alternative search terms and stop. Do not guess.

Anti-patterns

WRONG: results are empty, so you write "We probably discussed token expiry last week" from assumption.

RIGHT: "No memories matched that query. Try refresh token, session expiry, or auth rotation."

Checklist

  • Every observation shown came from the tool response.
  • Results grouped by session, high-importance first.
  • Empty results trigger alternative-term suggestions, not invention.
  • No session id or score was paraphrased or rounded.

See also

  • remember: the write side; recall retrieves what it stores.
  • recap, handoff, session-history: session-scoped views of the same data.

Troubleshooting

See ../_shared/TROUBLESHOOTING.md if memory_smart_search is not available.

Files

What ships with it

1 file 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.

Changes

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.

  1. 5d ago First seen · 61 lines · 61 tokens per session scan A 718ab72876b5

Subscribe to this mod's changes

recall is a skill published in the GitHub repository rmarquesa/agentmemory-offline (0 stars, last pushed 5d ago), licensed Apache-2.0. It adds 61 tokens to every session and 484 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 2026-08-31.