A file-based memory system for coding agents that keeps useful project context between sessions. It stores readable notes about the project's current state instead of relying on the agent to remember everything.

Use it to continue projects, restore context, update current-state summaries, and record meaningful progress across sessions.

It reduces the need to repeatedly explain a long-running project after a new session starts. It also makes progress and decisions easier to recover when work is interrupted.

recallloom — Skill for Claude Code and Codex by Frappucc1no

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.

Any agent

npx skills add Frappucc1no/recall-loom --skill recallloom

Clone the repo

git clone --depth 1 https://github.com/Frappucc1no/recall-loom

Made for: Claude Code, Codex.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/frappucc1no/recall-loom/recallloom/github.svg)](https://agentmods.dev/skills/frappucc1no/recall-loom/recallloom)

Your own site

<a href="https://agentmods.dev/skills/frappucc1no/recall-loom/recallloom"><img src="https://agentmods.dev/badge/skills/frappucc1no/recall-loom/recallloom/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/frappucc1no/recall-loom/recallloom"><img src="https://agentmods.dev/badge/skills/frappucc1no/recall-loom/recallloom.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 5,190 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 2 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Prompt Injection · line 43
Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.

Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
medium Excessive Agency · line 311
Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.

Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00065	$0.05190
Opus 5	$0.00032	$0.02595
Sonnet 5	$0.00013	$0.01038
Haiku 4.5	$0.00006	$0.00519

Measured 11d ago against content hash 99a2f7d81c0b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

recallloom 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 11d ago.

The scan reads SKILL.md. This mod also ships 30 executable files (scripts/_common.py, scripts/append_daily_log_entry.py, scripts/archive_logs.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/recallloom/SKILL.md · 361 lines

How it starts

The opening of the file, as written. The whole thing — 361 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RecallLoom

RecallLoom is a portable context harness for session-based agents.

It provides a lightweight file model for project continuity across sessions without requiring heavy infrastructure.

The goal is not to remember everything. The goal is to keep the right project state durable, readable, and recoverable across sessions.

Package Scope

This file is the agent-facing entrypoint for the installable recallloom/ skill package.

Install and trigger this package through your host agent's normal skill discovery flow. RecallLoom itself does not require a custom host-specific launcher inside the package. The package may still ship optional native wrapper templates for supported hosts.

This installable package is intentionally kept lean. Human-facing repository landing pages and marketing docs may exist upstream, but they are not bundled into the installed skill directory.

In the source repository, README.md and README.en.md are concise public front doors, README.zh-CN.md is the compatibility entry, INDEX.md is the full map, and USAGE.md is the operator guide. Those files describe the same helper contract as this installed package entrypoint rather than defining a second logic set.

For package inventory, protocol details, and helper-script behavior, rely on the files that ship inside the package itself:

managed-assets.json
package-metadata.json
references/file-contracts.md
references/operation-playbooks.md
references/package-support-policy.md
references/recording-workflow.md
references/protocol.md

Package Facts

package version: 0.5.0
protocol version: 1.0
supported protocol versions:
- 1.0

Runtime Assumptions

Python 3.10 or newer
supported workspace languages:
- en
- zh-CN
supported bridge targets:
- AGENTS.md
- CLAUDE.md
- GEMINI.md
- .github/copilot-instructions.md

Read the full file on GitHub · 361 lines

Files

What ships with it

60 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.

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.

11d ago First seen · 361 lines · 65 tokens per session scan A 99a2f7d81c0b

Subscribe to this mod's changes

recallloom is a skill published in the GitHub repository Frappucc1no/recall-loom (156 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 65 tokens to every session and 5,190 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.

Memory and context agent-memory agent-skills ai-agents ai-coding project-continuity session-memory

recallloom

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

RecallLoom

Package Scope

Package Facts

Runtime Assumptions

What ships with it

What this file has done since we first saw it

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