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 agentmods add skills/frankaging/lite-research-agents/memory-checkpointnpx skills add frankaging/lite-research-agents --skill memory-checkpointgit clone --depth 1 https://github.com/frankaging/lite-research-agentsWrote 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/frankaging/lite-research-agents/memory-checkpoint)<a href="https://agentmods.dev/skills/frankaging/lite-research-agents/memory-checkpoint"><img src="https://agentmods.dev/badge/skills/frankaging/lite-research-agents/memory-checkpoint.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.00565 |
| Opus 5 | $0.00008 | $0.00282 |
| Sonnet 5 | $0.00003 | $0.00113 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
memory-checkpoint 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- memory-checkpoint — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Checkpoint
Defines how to save a checkpoint and how to load one into SI. When to checkpoint is determined by the calling context — the project, the war room, or the agent's own judgment.
How to Save
Create a timestamped folder under memory/checkpoints/. Inside it, write one file per agent indexed by agent number.
memory/checkpoints/YYYYMMDD_HHMMSS/
├── agent-0.md
├── agent-1.md
└── ...
Each agent-N.md captures that agent's state. No length constraints — summarise what is needed to resume. A good checkpoint captures what would be hard to reconstruct, not a verbatim log.
# Agent <N> — <timestamp>
**Role**: <role>
**Trigger**: <what caused this checkpoint>
**Completed**: <what was just finished>
**Next**: <exact next action>
## State
<key facts, file paths, env state, decisions made, blockers — whatever is needed to resume>
After writing all agent files, update memory/SUMMARY.md — rewrite it to point to the latest checkpoint.
# Summary
- **Last checkpoint**: `memory/checkpoints/<timestamp>/`
- **Status**: <not_started | in_progress | blocked | complete>
## State
<key facts — file paths, important values, decisions that affect what comes next>
## Resume From
<one clear instruction: what to do next, any preconditions>
How to Load
On resume, inject into SI in this order:
- Read
memory/SUMMARY.md— always first. Get current status and the latest checkpoint path. - Read the relevant
agent-N.mdfrom the latest checkpoint folder — inject the file for your agent index. - Load other agents' files only if needed — e.g. to understand what another agent did.
- Load older checkpoints only if needed — e.g. to understand a past decision.
Layout
memory/
├── SUMMARY.md
└── checkpoints/
├── 20260328_090000/
│ └── agent-0.md
├── 20260328_142301/
│ ├── agent-0.md
│ └── agent-1.md
└── 20260328_154732/
├── agent-0.md
└── agent-1.md
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.
- 5d ago First seen · 81 lines · 16 tokens per session scan A 491f7100fe3f
memory-checkpoint is a skill published in the GitHub repository frankaging/lite-research-agents (3 stars, last pushed 5mo ago), licensed MIT. It adds 16 tokens to every session and 565 once invoked, about $0.0001 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 skills, from other repositories
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agent-memory-mcp
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
log-session
Append a structured entry to the project's session log (docs/LOGS.md): what was done this session, files touched, decisions taken, and the next step — so the next session (or another person) can pick up the thread without re-reading git history. Run it before /clear, before closing Claude Code, or at any natural…
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Use when durable DPF knowledge lives only in a human's head — a decision rationale, the why behind a choice, a profession technique, domain context a build depends on.
do-it-context
Use when project terms, relationships, or invariants drift between user language, docs, and implementation.
dpf-route-learning-to-commons
Use in the DPF codebase at a task or session boundary when a finding has been confirmed and is durable.