Agent Beacon is a local-first memory layer that records AI coding-agent sessions and turns useful workflows, corrections, and debugging patterns into knowledge that other agents can reuse. Developers use it across Claude Code, Cursor, Codex, OpenCode, and other agent harnesses so lessons and repository conventions persist between sessions. The catalogue entries provide instructions and a skill for working with Beacon's shared agent memory.
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 Asymptote-Labs/agent-beacon --skill beacon-memory-recallgit clone --depth 1 https://github.com/Asymptote-Labs/agent-beaconWrote 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/asymptote-labs/agent-beacon/beacon-memory-recall)<a href="https://agentmods.dev/skills/asymptote-labs/agent-beacon/beacon-memory-recall"><img src="https://agentmods.dev/badge/skills/asymptote-labs/agent-beacon/beacon-memory-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/asymptote-labs/agent-beacon/beacon-memory-recall"><img src="https://agentmods.dev/badge/skills/asymptote-labs/agent-beacon/beacon-memory-recall.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 | $0.00106 | $0.01127 |
| Opus 5.5 | $0.00042 | $0.00451 |
| Sonnet 5.5 | $0.00021 | $0.00225 |
| Haiku 4.5 | $0.00011 | $0.00113 |
Grade A, and why
beacon-memory-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 20d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Beacon memory recall
Beacon records what agents do across every supported harness into a local log. Through
the beacon-memory-distill workflow, a person reviews selected sessions and approves the
reusable lessons into project memory. This skill reads that memory back, so you start
the task knowing what earlier agents, in this harness or any other, already learned about
this repository.
Approved memory has been reviewed by a person. Raw traces have not. Keep the two apart in everything you say.
When to recall
- Before planning a non-trivial change in a repository you have not worked in during this session.
- When a command fails in a way that could have a known fix (flaky tests, packaging, environment setup, release steps).
- When the user asks what was learned, tried, or decided before.
Skip it for trivial edits, and recall at most once per task unless the task changes direction.
Step 1: check that Beacon is available
beacon version
If beacon is not found, tell the user that project memory is unavailable because the
Beacon CLI is not installed, point them to https://docs.beacon.sh/get-started/overview, and
continue the task without memory. Do not install it yourself.
Step 2: retrieve memory
Prefer the Beacon MCP tools when this session has them. They are named search_memory,
get_memory, and get_memory_context, usually under a server called beacon.
- Call
get_memory_contextwithtaskset to two or three distinctive keywords from the current task (a tool name, file, error string). Terms are ANDed, so a full sentence will over-constrain the search and return nothing. Drop terms if the first attempt is empty. - Call
get_memorywith anidfrom that result when you need the full body and its evidence.
Without the MCP tools, use the CLI from inside the repository:
# Up to five memories matching the task's key terms (every term must match)
beacon memory list --json --limit 5 -q "<two or three distinctive terms>"
# Everything approved for this project, newest first
beacon memory list --limit 25
# One memory in full, with its source traces
beacon memory show <memory-id>
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.
- 20d ago First seen · 108 lines · 106 tokens per session scan A e9d97e560366
beacon-memory-recall is a skill published in the GitHub repository Asymptote-Labs/agent-beacon (1,834 stars, last pushed today), licensed MIT. It adds 106 tokens to every session and 1,127 once invoked, about $0.0004 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 2026-09-26.
Other skills, from other repositories
memory-poisoning-detection
Detect persistent instruction injection or long-term memory poisoning. Focus on writing/retaining hostile instructions for future tasks, not data leakage.
abmind-runtime-operations
Inspect and maintain abmind memory, persona files, sleep, backups, encryption, and runtime health.
abmind_recall_workaround
Fallback when abmind recall / the abmindrecall tool fails — read memories directly from the SQLite DB.
memory-search
Search persistent memory for facts, decisions, and past conversations.
read-messages
Read back raw conversation messages from the abmind messages table (session list + message dump).
topic-save
Save and manage topic-specific knowledge files from conversations.