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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
65 lines
3 KiB
Markdown
65 lines
3 KiB
Markdown
---
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name: cc-compact
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description: 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.
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---
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You are reloading the context of a previous Claude Code session so you can
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**continue that work in this session** — like `/resume` followed by `/compact`, but pre-compacted. The
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bundled helper script extracts the key signals and prints a compact report.
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**Hard rule: you may NOT read the session `.jsonl` file by any means** — no
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`cat`, `head`, `tail`, `jq`, `grep`, `Read`, nothing. Run the helper script
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**once** and work solely from its output. That is the only thing allowed to
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touch the file.
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The helper script lives at `~/.claude/skills/cc-compact/compact_session.py`.
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## Step 1: Resolve the session
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The argument is one of two forms:
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- **A session id** — when invoked like `/resume claude --resume <id>` or
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`cc-compact <id>`, the user already gave you the UUID. Pass it as `--id`.
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- **A session name / title** — free text. Pass it as `--title`; the script
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matches it against the `ai-title` records inside the logs (case-insensitive
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substring, newest match wins).
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Run the script exactly once with the matching form:
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```sh
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# By id:
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python3 ~/.claude/skills/cc-compact/compact_session.py --id <session-uuid>
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# By title:
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python3 ~/.claude/skills/cc-compact/compact_session.py --title "the session name"
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```
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## Step 2: Read the output
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It prints a bounded, XML-tagged report:
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- header: project, git branch, time span, record/prompt counts
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- the first few exchanges (user prompt + the agent's reply) — the original intent
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- a few exchanges sampled from the middle (non-overlapping) — the journey
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- the last few exchanges (user prompt + the agent's reply) — where things were heading
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- the most-edited files, ranked by edit count
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- the last several tool calls — what the agent was physically doing last
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- the final assistant message — what it was saying / waiting on last
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## Step 3: Pick up the work
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The output is context for *you* — treat it like a resumed session, not something
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to report on. From it, reconstruct what the work was, which files are in play,
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and what the agent was in the middle of.
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Compaction is lossy — the output may not make the next step unambiguous. Before
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diving in, decide whether you actually know how to proceed:
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- **If the next step is very clear, last few messages from user confirms what to do**, open with one line confirming what you're
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resuming, then continue from where it left off.
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- **If it's ambiguous** (unclear what the user wants next, multiple plausible
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directions, or the session ended mid-decision), **ask the user how to proceed**
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before acting. Surface the few plausible next steps you inferred and let them
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pick or correct you. If there is any doubt, opt to ask the user first.
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