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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Asymptote-Labs/agent-beaconnpx agentmods add skills/asymptote-labs/agent-beacon/beacon-memory-promoteWrote 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-promote)<a href="https://agentmods.dev/skills/asymptote-labs/agent-beacon/beacon-memory-promote"><img src="https://agentmods.dev/badge/skills/asymptote-labs/agent-beacon/beacon-memory-promote/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-promote"><img src="https://agentmods.dev/badge/skills/asymptote-labs/agent-beacon/beacon-memory-promote.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 | $0.00086 | $0.00887 |
| Opus 5.5 | $0.00034 | $0.00355 |
| Sonnet 5.5 | $0.00017 | $0.00177 |
| Haiku 4.5 | $0.00009 | $0.00089 |
Grade A, and why
beacon-memory-promote 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Beacon memory promote: memory to skill
Approved memory is recalled on demand. A skill is loaded by the harness itself
whenever its description matches the task, and when it is committed to the repository it
reaches every teammate and every harness that reads .agents/skills (Codex, Cursor,
Gemini CLI, GitHub Copilot, OpenCode, Amp, goose, and others; Claude Code reads
.claude/skills).
Promote only what earns that. Each installed skill adds to what every agent considers on every task.
What to promote
Good candidates:
workflowandconventionmemories that apply to many tasks in this repository.- A
debugging_patternfor a failure that keeps recurring.
Leave in memory, where recall finds them when needed:
- One-off gotchas tied to a single file or incident.
- Anything that duplicates the repository's CLAUDE.md, AGENTS.md, or contributing docs. Suggest editing those docs instead.
Step 1: choose the memory
beacon memory list --limit 25
beacon memory show <memory-id> --json
The skill commands take the candidate ID, which is the candidate_id field of the
memory.
Step 2: preview
beacon memory skills preview <candidate-id>
Check the preview with the user:
- The
descriptionis the title plus the memory's applicability. It decides when a harness loads the skill, so it must name the trigger (a command, path, or error). If it is vague, the fix is a better memory: draft a sharper title and applicability, approve a replacement through thebeacon-memory-distillworkflow, and supersede the old one. - The body must hold no secrets, hostnames, or personal details. It is about to become a file that is likely to be committed.
Step 3: install, after the user confirms
beacon memory skills install <candidate-id>
This writes .agents/skills/<slug>/SKILL.md in the project (or the --project path). It
refuses to overwrite an existing file; pass --force only when the user wants to replace
the generated skill, and never over a file a person has edited by hand without asking.
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 · 95 lines · 86 tokens per session scan A a63510a15123
beacon-memory-promote is a skill published in the GitHub repository Asymptote-Labs/agent-beacon (1,834 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 887 once invoked, about $0.0003 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.