b0070920e3
- cc-compact: skill to reload a past Claude Code session's context (resolves by id or ai-title, extracts a bounded XML summary via compact_session.py) so work can be resumed without ingesting the log - migrate the existing new-work skill into the repo - deploy ~/.claude/skills via ensure_dir_symlink in setup.sh - claude-token-count: count tokens via Anthropic's count_tokens endpoint, pulling the API key from 1Password at runtime
3 KiB
| name | description |
|---|---|
| cc-compact | Summarize ("compact") a past Claude Code session without ingesting its entire history. Resolves the session log, then extracts only the key signals — opening intent, recent prompts, most-edited files, and what the agent was doing last. Use, when asked, to resume long sessions without ingesting entire log. |
You are reloading the context of a previous Claude Code session so you can
continue that work in this session — like /resume followed by /compact, but pre-compacted. The
bundled helper script extracts the key signals and prints a compact report.
Hard rule: you may NOT read the session .jsonl file by any means — no
cat, head, tail, jq, grep, Read, nothing. Run the helper script
once and work solely from its output. That is the only thing allowed to
touch the file.
The helper script lives at ~/.claude/skills/cc-compact/compact_session.py.
Step 1: Resolve the session
The argument is one of two forms:
- A session id — when invoked like
/resume claude --resume <id>orcc-compact <id>, the user already gave you the UUID. Pass it as--id. - A session name / title — free text. Pass it as
--title; the script matches it against theai-titlerecords inside the logs (case-insensitive substring, newest match wins).
Run the script exactly once with the matching form:
# By id:
python3 ~/.claude/skills/cc-compact/compact_session.py --id <session-uuid>
# By title:
python3 ~/.claude/skills/cc-compact/compact_session.py --title "the session name"
Step 2: Read the output
It prints a bounded, XML-tagged report:
- header: project, git branch, time span, record/prompt counts
- the first few exchanges (user prompt + the agent's reply) — the original intent
- a few exchanges sampled from the middle (non-overlapping) — the journey
- the last few exchanges (user prompt + the agent's reply) — where things were heading
- the most-edited files, ranked by edit count
- the last several tool calls — what the agent was physically doing last
- the final assistant message — what it was saying / waiting on last
Step 3: Pick up the work
The output is context for you — treat it like a resumed session, not something to report on. From it, reconstruct what the work was, which files are in play, and what the agent was in the middle of.
Compaction is lossy — the output may not make the next step unambiguous. Before diving in, decide whether you actually know how to proceed:
- If the next step is very clear, last few messages from user confirms what to do, open with one line confirming what you're resuming, then continue from where it left off.
- If it's ambiguous (unclear what the user wants next, multiple plausible directions, or the session ended mid-decision), ask the user how to proceed before acting. Surface the few plausible next steps you inferred and let them pick or correct you. If there is any doubt, opt to ask the user first.