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.
git clone --depth 1 https://github.com/ils15/pantheon-legacyWrote 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/commands/ils15/pantheon-legacy/pantheon-remember)<a href="https://agentmods.dev/commands/ils15/pantheon-legacy/pantheon-remember"><img src="https://agentmods.dev/badge/commands/ils15/pantheon-legacy/pantheon-remember/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/commands/ils15/pantheon-legacy/pantheon-remember"><img src="https://agentmods.dev/badge/commands/ils15/pantheon-legacy/pantheon-remember.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 | $0.00047 | $0.00430 |
| Opus 5 | $0.00023 | $0.00215 |
| Sonnet 5 | $0.00009 | $0.00086 |
| Haiku 4.5 | $0.00005 | $0.00043 |
Grade A, and why
pantheon-remember 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 6d 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.
What it actually says
/pantheon-remember — Memory Store & Recall
What: Stores a memory entry via memory_store() or recalls via memory_recall(). Interactive mode if no arguments.
Usage
/pantheon-remember "Decisão: usar FastAPI em vez de Flask" → Store (namespace: default)
/pantheon-remember "Decisão: ..." --namespace=decisions → Store em namespace específico
/pantheon-remember recall "FastAPI decision" → Recall por key
/pantheon-remember recall "FastAPI decision" --namespace=decisions → Recall em namespace
/pantheon-remember → Modo interativo (pergunta→store→pergunta recall)
Behavior
Store mode (default)
- Chama
memory_store()com o texto fornecido - Metadata: preenche
agent,type,timestampautomaticamente - Pergunta: "Quer buscar algo relacionado? (y/n)"
y→memory_recall()com extração automática de keywordsn→ fim
Recall mode
- Chama
memory_recall()com a key fornecida - Se não achar, tenta
memory_search()como fallback
Interactive mode (no args)
- "O que você quer armazenar?"
- Recebe texto →
memory_store() - "Quer buscar algo relacionado?"
- Opcional: "Qual namespace?" (default: "default")
Output
✅ Stored — id: 42 | namespace: decisions | key: auto-generated
📎 Related entries (memory_recall):
- #1: "Decisão: usar FastAPI..." (score: 0.92)
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.
- 6d ago First seen · 45 lines · 47 tokens per session scan A 5396446094eb
pantheon-remember is a command published in the GitHub repository ils15/pantheon-legacy (10 stars, last pushed 7d ago), licensed MIT. It adds 47 tokens to every session and 430 once invoked, about $0.0002 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 2026-09-03.
Other commands, from other repositories
minutes-ideas
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learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.