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 skills add jhlee0409/all-for-claudecode --skill resumegit clone --depth 1 https://github.com/jhlee0409/all-for-claudecodeWrote 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/jhlee0409/all-for-claudecode/resume)<a href="https://agentmods.dev/skills/jhlee0409/all-for-claudecode/resume"><img src="https://agentmods.dev/badge/skills/jhlee0409/all-for-claudecode/resume/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/jhlee0409/all-for-claudecode/resume"><img src="https://agentmods.dev/badge/skills/jhlee0409/all-for-claudecode/resume.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 | $0.00009 | $0.00691 |
| Opus 5 | $0.00005 | $0.00345 |
| Sonnet 5 | $0.00002 | $0.00138 |
| Haiku 4.5 | $0.00001 | $0.00069 |
Grade B, and why
afc:resume scanned grade B with 1 finding 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 12d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
!`cat .claude/afc/memory/checkpoint.md 2>/dev/null || echo "[NO_CHECKPOINT]"` How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/afc:resume — Restore Session
Restores the previous session state from .claude/afc/memory/checkpoint.md and resumes work.
Arguments
$ARGUMENTS— (optional) none
Checkpoint State (auto-loaded)
!cat .claude/afc/memory/checkpoint.md 2>/dev/null || echo "[NO_CHECKPOINT]"
Execution Steps
1. Load Checkpoint
Use the pre-fetched checkpoint above. If it shows [NO_CHECKPOINT]:
- Check auto-memory fallback: read
~/.claude/projects/{ENCODED_PATH}/memory/checkpoint.md - If fallback also not found: output "No saved checkpoint found." then stop
If checkpoint data was pre-fetched successfully: parse the full contents (extract branch, commit hash, pipeline feature, task progress, modified files).
2. Validate Environment
Compare the checkpoint state against the current environment:
- Branch check: Does the checkpoint branch match the current branch?
- If different: warn + suggest switching
- File state: Have any files changed since the checkpoint?
- First verify HEAD exists:
git rev-parse --verify HEAD 2>/dev/null- If HEAD does not exist (empty repo / no commits): report "No commits yet — cannot check changes since checkpoint." and skip this check
- If checkpoint hash is present and non-empty:
git log {checkpoint hash}..HEAD --oneline - If checkpoint hash is empty or missing: report "Checkpoint has no git reference — cannot diff." and skip this check
- First verify HEAD exists:
- Feature directory: Does .claude/afc/specs/{feature}/ still exist?
3. Report State
## Session Restore
### Previous Checkpoint
- **Saved at**: {time}
- **Message**: {checkpoint message}
- **Branch**: {branch} {(matches current ✓ / differs ⚠)}
### Active Features
| Feature | Status | Progress |
|---------|--------|----------|
| {name} | {status} | {progress} |
### Changes Since Checkpoint
{list of new commits if any, or "No changes"}
### Incomplete Work
{incomplete work list from checkpoint.md}
### Recommended Next Steps
{recommended commands based on state}
- Tasks in progress → resume `/afc:implement`
- Plan complete → `/afc:implement` (tasks generated automatically at start)
- Spec only → `/afc:plan`
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.
- 12d ago First seen · 91 lines · 9 tokens per session scan B 4ff690ef8795
afc:resume is a skill published in the GitHub repository jhlee0409/all-for-claudecode (7 stars, last pushed 5mo ago), licensed MIT. It adds 9 tokens to every session and 691 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
orchestrator-lanes
A file-based project-management playbook for a specific Claude Code development orchestrator. It organizes work into lanes, plans, dependency steps, validation phases, and shipping stages.
eval-agents
Audit Claude Code agents defined in .claude/agents/ for description specificity, model tier appropriateness, tools scoping, and system prompt quality. Detects dispatch ambiguity between agents, flags over-permissive tool grants, and checks for human-in-the-loop patterns that break programmatic orchestration. Use when…
land-and-deploy
Merge PR, wait for CI, verify deploy, run canary. The complete landing pipeline.
self-assessment
Interactive skill assessment with personalized learning path generation.
git-ai-archaeology
Analyze AI config evolution in a git repo. Use when mapping AI adoption history, finding when configs were first introduced, charting commit velocity by month, or identifying maturity phases in a project's AI tooling.
source-command-methodology-advisor
Analyzes your codebase and asks 3 targeted questions to recommend the right AI-assisted development methodology stack.