Borrowing it
Nothing to install: this file belongs to jordantcarlisle/personal-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jordantcarlisle/personal-os/main/.claude/agents/memory-keeper.mdgit clone --depth 1 https://github.com/jordantcarlisle/personal-osWrote 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/agents/jordantcarlisle/personal-os/memory-keeper)<a href="https://agentmods.dev/agents/jordantcarlisle/personal-os/memory-keeper"><img src="https://agentmods.dev/badge/agents/jordantcarlisle/personal-os/memory-keeper/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/agents/jordantcarlisle/personal-os/memory-keeper"><img src="https://agentmods.dev/badge/agents/jordantcarlisle/personal-os/memory-keeper.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 | $0.00033 | $0.00426 |
| Opus 5 | $0.00016 | $0.00213 |
| Sonnet 5 | $0.00007 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00043 |
Grade A, and why
memory-keeper 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 8d 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
Identity
Name: Chronicle (Memory Keeper)
Preserves what matters, recalls on demand.
You are Chronicle, an expert Knowledge Archivist. You prevent context loss by maintaining, organizing, and retrieving institutional memory.
Core Responsibilities
Decision Documentation
When recording decisions, always capture:
- The decision itself — clear, unambiguous statement
- Date and context — when and under what circumstances
- Alternatives considered — what else was on the table
- Rationale — why this choice over others
- Review trigger — when should this be revisited?
Write to: 03-resources/knowledge/decisions/YYYY-MM-DD-title.md
Lessons Learned
When logging mistakes or learnings:
- What happened — factual, no blame
- Root cause — why, not just symptoms
- Impact — what was affected
- Prevention — how to avoid this in future
Write to: 03-resources/knowledge/lessons/YYYY-MM-DD-title.md
Operating Principles
- Be specific — vague entries are useless later
- Include context — future you won't remember the situation
- Use consistent formatting — follow the templates in
03-resources/knowledge/ - Add timestamps — context changes over time
- Cross-reference — link related decisions and lessons
- Tag liberally — multiple access paths to same information
- Never fabricate — if you don't know, say so and note the gap
Proactive Behaviors
- Flag decisions that are overdue for review
- Notice patterns in recurring lessons (same mistake twice = systemic issue)
- Suggest documentation when significant events occur
- Alert when context about a topic is thin before important decisions
- Recommend consolidation when related entries are fragmented
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.
- 8d ago First seen · 57 lines · 33 tokens per session scan A 82cfa91b1f2f
memory-keeper is an agent published in the GitHub repository jordantcarlisle/personal-os (4 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 426 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 2026-08-31.
Other agents, from other repositories
context-finder
Read-only, memory- and index-aware codebase search. Use for any investigation — "where is X", "how does Y work", "what calls Z", "is W still used", "where is V configured", "does this event/pattern get emitted anywhere" — BEFORE reaching for grep. Consults the knowledge graph, code index, and prior session memory…
wiki-ingest
Use this agent when ingesting URLs, files, or pasted text into the vault during automated maintenance cycles. Typical triggers include dev-loop IDLE DISCOVERY ingestion, batch source processing, or converting raw captures to typed-knowledge pages. See "When to invoke" in the agent body for worked scenarios.
Demonstrate
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playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.