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/tianhanz/ears/checkpointnpx skills add tianhanz/ears --skill checkpointgit clone --depth 1 https://github.com/tianhanz/earsWrote 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/tianhanz/ears/checkpoint)<a href="https://agentmods.dev/skills/tianhanz/ears/checkpoint"><img src="https://agentmods.dev/badge/skills/tianhanz/ears/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 | $0.00048 | $0.01334 |
| Opus 5 | $0.00024 | $0.00667 |
| Sonnet 5 | $0.00010 | $0.00267 |
| Haiku 4.5 | $0.00005 | $0.00133 |
Grade A, and why
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 4d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Checkpoint Skill
Save current task progress to a structured YAML checkpoint file that survives context loss.
When to Use
- Session ending: Before closing session or switching tasks
- Context window full: Approaching token limit
- Significant progress: Completed a major phase or experiment
- Before risky operations: Before destructive changes or complex merges
- Manual checkpoint: User explicitly requests
/checkpoint
Step 1 — Gather current state
Review conversation history and files to extract:
Phase tracking:
- What phase are we in? (e.g., parameter_extraction, mesh_generation, convergence_testing, debugging, implementation)
- What phases are completed?
- What phases are next?
Critical state:
- Parameters: Extracted values with sources (paper page/table or "estimated from X") and confidence levels
- Experiments: What's been tried? Status (pending/in_progress/completed/failed/diverged), results paths, failure reasons
- Decisions: Key choices made, rationale, alternatives considered
- Custom state: Task-specific data (meshes, models, datasets, API endpoints, etc.)
Blockers:
- Open questions blocking progress
- Decisions needed
- Assumptions made that need verification
Artifacts: Critical files (configs, results, scripts) with roles
Resume instructions: Specific commands to run next session, conditions to verify
Step 2 — Determine checkpoint filename
BRANCH=$(git branch --show-current)
TIMESTAMP=$(date -u +"%Y%m%d-%H%M%S")
CHECKPOINT_FILE=".claude/checkpoints/${BRANCH}-${TIMESTAMP}.yaml"
mkdir -p .claude/checkpoints
Step 3 — Write checkpoint YAML
Quality bar:
- Every parameter must have
source(paper page/table or "estimated from X") - Every failed experiment must have
reason(not just "didn't work") - Every decision must have
rationale - Resume instructions must be actionable (specific commands or file checks, not vague "continue work")
Example checkpoint:
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.
- 4d ago First seen · 170 lines · 48 tokens per session scan A f1211f41f98a
checkpoint is a skill published in the GitHub repository tianhanz/ears (5 stars, last pushed 4mo ago), licensed MIT. It adds 48 tokens to every session and 1,334 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.
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The essential habits for an AI agent with memory — session bookends, learning triggers, verification, safety, and the operational discipline that turns raw recall into compounding intelligence. Pinned, always-injected.
s-continue
Cheaper and faster than /compact. Restores previous session context from Claude Code AND Codex transcripts by reading them directly — no LLM calls, no token cost. Also auto-loads a handoff written by /s-compact, if one exists. Triggers on "s-continue", "restore context", "what was I doing", "pick up where I left off"…
project-memory
Generate a project-specific context file from a brief so an AI assistant remembers your editorial constraints, voice, audience, and quality bar across sessions.
sync
Fast save-point: sync all session-context files and MEMORY.md with current progress. Zero questions, zero delay. Use when user says /sync, save progress, save state, sync context, or /sync --full for capability inventory.
handoff-more
Pull older handoffs from .claude/handoffhistory/ into the current session's context. Requires the scripts/hooks installed by this repo's ./install.sh — the history it reads only exists once those are in place. Use when the auto-loaded handoffcurrent.md is thin (placeholder Notes, missing context the user is…
mk:session-continuation
Persists and restores mid-session workflow progress across context resets. Use when resuming workflows, saving state, or handling context compaction. Triggers on: handoff, save state, resume workflow.