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/VaibhavAher100/wrap-sessionWrote 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/vaibhavaher100/wrap-session/wrap)<a href="https://agentmods.dev/commands/vaibhavaher100/wrap-session/wrap"><img src="https://agentmods.dev/badge/commands/vaibhavaher100/wrap-session/wrap/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/vaibhavaher100/wrap-session/wrap"><img src="https://agentmods.dev/badge/commands/vaibhavaher100/wrap-session/wrap.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 | $0.00000 | $0.02094 |
| Opus 5.5 | $0.00000 | $0.00838 |
| Sonnet 5.5 | $0.00000 | $0.00419 |
| Haiku 4.5 | $0.00000 | $0.00209 |
Grade A, and why
wrap 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 1mo 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are closing a Claude Code session. Execute all steps below in order.
Note: This skill tracks only the current model's context window. Context percentage is read from the Claude Code API via the statusline state file — model-aware, accurate for any context window size. The 5-hour and 7-day usage limits are separate — do not conflate them with session context.
When invoked manually by the user (
/wrap): execute all steps immediately, no confirmation needed. When invoked automatically by the context monitor hook at 40%: the hook will have already asked the user whether they want to wrap. By the time this skill runs, the user has already said yes — proceed directly.
Step 0 — Pre-flight safety check
Use the Bash tool (bash, not PowerShell — these are bash-specific commands):
git -C . status --short 2>/dev/null | grep "^.D\|^ D" | wc -l
If the count is 20 or more uncommitted deletions, stop and output:
Warning: Detected N uncommitted file deletions. Wrapping now may lose context on files that were deleted this session. Type
confirm wrapto proceed anyway, or commit/stash first.
Wait for user response before continuing. If count is under 20, proceed immediately.
Step 1 — Determine session ID
Use the Bash tool:
ls logs/sessions/$(date +%Y-%m-%d)-*.md 2>/dev/null | sort | tail -1
- If output is empty: new ID is
<today's date>-001 - If output is e.g.
...2026-05-21-003.md: new ID is2026-05-21-004 - Do not count manually — use the shell output as the source of truth
Step 2 — Write the session log
Self-assess confidence using this rubric:
confidence: high— full session visible, no compaction events fired, all tool calls in context, clean completion with no unexplained gapsconfidence: medium— at least one of: some compaction occurred, session ran >2 hours, >50 tool calls, some tool outputs were truncated; key decisions are present but some actions may be missingconfidence: low— heavy compaction (early messages gone), unresolved blockers exist that you cannot explain, or more than ~30 minutes of work is unaccounted for in your context
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.
- 1mo ago First seen · 198 lines · 0 tokens per session scan A c3d0419111a3
wrap is a command published in the GitHub repository VaibhavAher100/wrap-session (1 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,094 tokens. 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 commands, from other repositories
mute
Silence Bonsai temporarily for this project (or all projects with --global).
replay
You are reconstructing the historical evolution of this sprint by recompiling every version of claims.json from git history.
create-pr
Streamlines pull request creation by handling the entire workflow: creating a new branch, committing changes, formatting modified files with Biome, and submitting the PR.
status
Warm mid-session check — git detail, last session note, and in-progress backlog. Use when you still have mental context loaded and want a quick pulse. Not for cold starts (use /tools:brief) or planning (use /tools:overview).
handoff
Fast session handover — capture current state and write a resume command for the next session. Use when context is filling up or you need to switch tasks.
release-swarm
Orchestrate complex software releases using AI swarms that handle everything from changelog generation to multi-platform deployment.