continue

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Cheaper and faster than /compact. Restores previous session context by reading transcripts directly — no LLM calls, no token cost

ww-w-ai By ww-w-ai schedule Updated 5/20/2026
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name: continue description: 'Cheaper and faster than /compact. Restores previous session context by reading transcripts directly — no LLM calls, no token cost' when_to_use: Use when starting a new session and want to pick up previous work. Triggers on "continue", "continue last", "restore context", "what was I doing", "pick up where I left off", "resume work", "previous session".

Restore context from previous sessions so the user can pick up where they left off — without the cost of /compact.

Help

ONLY show help if the user's argument literally contains the word "help" (e.g. /continue help). If no argument or any other argument is given, SKIP this section entirely and proceed to Step 1.

If the user provides "help" as argument, show usage summary and stop:

/continue — Restore context from previous sessions (zero LLM calls)

Options:
  (nothing)     Show session list, pick which to restore
                  - Current session with context-loss events appears as #0 [default]
                  - Press Enter to restore just #0, or add more numbers
  last          Quick restore:
                  - Current session if it had /compact or auto-compact
                  - Otherwise, most recent other session
  help          Show this help

Examples:
  /continue
  /continue last

Do not run any analysis or restoration. Just display the help text and stop.

Language

Detect the user's language from their message accompanying the /continue invocation. If no message was provided (bare /continue), detect the dominant language from the session list's firstMsg/lastMsg content after Step 1 runs. All UI messages (session list header, selection prompt, progress updates, final reference note) MUST be in the detected language. The examples below are in English — translate naturally, don't transliterate.

Quick Restore: /continue last

If the user invoked /continue last, skip the session list entirely. Run list-sessions with --limit 3. Then pick automatically based on the isCurrent and hasContextLoss fields:

  • If the current session has context-loss (isCurrent: true AND hasContextLoss: true) → auto-pick the CURRENT session. Its pre-context-loss content is what needs restoration.
  • Otherwise → auto-pick the most recent session where isCurrent: false (the previous session).
  • If no valid target (current session has no context-loss AND no previous sessions exist) → print "No previous sessions found in this project." and stop.

Jump directly to Step 3 with the selected session. No user prompt needed.

Step 1: List & Select

If /continue last was used, skip this step (see above).

Run the list-sessions script to get main sessions only (subtask/system-only sessions are filtered out). Requires Node.js.

PROJECT_HASH=$(echo "${PWD}" | sed 's/[^a-zA-Z0-9]/-/g')
TRANSCRIPTS_DIR="${HOME}/.claude/projects/${PROJECT_HASH}"
node ${CLAUDE_PLUGIN_ROOT}/scripts/list-sessions.js "${TRANSCRIPTS_DIR}" --limit 11 --offset 0

The script outputs JSON. If the script returns an empty array, display "No previous sessions found in this project." and stop.

Current session identification: The script sets isCurrent: true on the session whose JSONL is most recently modified (the one being actively written). This is reliable even after auto-compact (unlike firstMsg comparison, which fails because the LLM's first visible message becomes the summary).

Case A/B/C/D list display:

  • Look at the session with isCurrent: true:
    • If hasContextLoss: true → display it as #0 [default] (with 📍 marker plus any @@/+/++ event badges). #1..N are other sessions.
    • If hasContextLoss: false → exclude it entirely from the list (its full content is in live memory, nothing to restore). #1..N are other sessions.
  • If no other sessions exist and current has context-loss → auto-restore current session, skip list display (Case C).
  • If no other sessions exist and current has no context-loss → print "No previous sessions found in this project." and stop (Case D).

Format each session for display (preserve existing Case A/B/C/D logic — current session #0 with context-loss marker, etc.):

📂 Found {N} previous sessions in this project.

Pick the ones you want to restore — Claude will read them and bring the
context into this session so you can continue where you left off.

💡 Tip: Selecting 1-2 sessions is fast (almost always faster than /compact).
   Selecting many sessions takes longer, but still no LLM summarization needed.

| # | Started | Last active | First message | Last message | Size |
|---|---------|-------------|---------------|--------------|------|
| 1 | Mar 31 09:00 | today 14:05 | "improve the skill..." | "ok go ahead..." | 122KB · 3 msgs |
| 2 | Mar 31 08:30 | today 13:59 | "local agent actually..." | "let me test the skill..." | 2.1MB · 82 msgs |
| ... | | | | | |

Enter:
  - numbers only (e.g., "1,3" or "1-4") — fast restore
  - numbers + ":" + topic (e.g., "1,3 : PDCA 구현") — topic-based restore (slower, more accurate)
  - "more" for pagination
  - (empty) for default

💡 Topic search adds an LLM step so it takes longer, but restores specific memories more accurately.

Use --limit N and --offset N for pagination. When the user types "more", re-run list-sessions with --offset increased by 10 (the limit). Numbers continue sequentially across pages.

Wait for user selection before proceeding. This avoids preprocessing sessions the user doesn't need.

Step 2: Parse Input

Split user input on the first ::

  • Left side → numbers part. Parse using existing Case A/B/C/D logic (additive with #0, ranges, comma lists).
  • Right side (optional) → topic string (trim whitespace). May be absent.

Examples:

  • 1,3 → sessions [1, 3], no topic
  • 1-4 : PDCA 구현 → sessions [1, 2, 3, 4], topic = "PDCA 구현"
  • : error handling → only #0 (default), topic = "error handling"
  • `` (empty) → default selection, no topic

Step 3: Ensure Cache & Preprocess

preprocess.js is self-managing: it derives the cache path from the JSONL path, checks format version + mtime, and skips if fresh. Just call it for each selected session.

# For each selected session: ensure compact.txt cache is fresh
node ${CLAUDE_PLUGIN_ROOT}/scripts/preprocess.js "${TRANSCRIPT_PATH}"

The cache file is at:

PROJECT_HASH=$(echo "${PWD}" | sed 's/[^a-zA-Z0-9]/-/g')
CACHE_FILE="${HOME}/.claude/claude-code-token-saver-data/${PROJECT_HASH}/${SESSION_ID}/compact.txt"

Current session with context-loss: The compact.txt contains the FULL session. When reading it, use lastContextLossLine from list-sessions.js to filter: only read entries where L{n} < lastContextLossLine. Content after the last context-loss event is already in live LLM memory.

To extract just the pre-boundary portion without LLM parsing:

awk "/\[Session:.*L${LAST_LOSS_LINE}\]/{exit} 1" "${CACHE_FILE}"

Current session WITHOUT context-loss: Skip — entire session is in live memory.

Past sessions: Read the full compact.txt (none of their content is in live memory).

The preprocessor (v6) outputs a compact text transcript with [Session:{sid} {ISO} L{n}] headers. The L{n} is the JSONL line number of the user message — this enables direct seek into the original transcript for topic-based restoration.

Preprocessing is instant (< 1 second even for 60MB+ transcripts).

Step 4: Load Compact

No size threshold. Always load all selected compact.txt files.

  • No topic → Read all compact.txt files directly using the Read tool. Content is loaded into conversation context as-is. For files exceeding ~10K tokens, read in chunks using offset/limit parameters. Always read the ENTIRE file — never skip sections. Proceed to Step 6.

  • Topic provided → Do NOT Read compact.txt yet. Proceed to Step 5 (topic-based restoration).

Step 5: Topic-Based Original Restoration

Goal: Load compact.txt with the top 20 most topic-relevant truncated turns replaced by their full JSONL originals. The original compact.txt files are never modified — the assembled result is written to a temp file.

Step 5a: Extract user turn list

Extract all user message headers from compact.txt files programmatically (no LLM Read needed):

python3 << 'PYEOF'
import json, os, re

sessions = [
    # (session_id, compact_path) — dynamically populated
]

results = []
for sid, path in sessions:
    with open(os.path.expanduser(path)) as f:
        content = f.read()
    for m in re.finditer(
        r'\[Session:([a-f0-9]+) (\S+) L(\d+)\].*?User: "(.*?)"',
        content
    ):
        results.append({
            "sid": m.group(1),
            "ts": m.group(2),
            "line": int(m.group(3)),
            "msg": m.group(4)[:300]
        })

print(json.dumps(results, ensure_ascii=False))
PYEOF

Step 5b: LLM selects top 20

Read the JSON output from Step 5a. For each user turn, judge topic relevance. Select the top 20 most relevant turns (by topic match strength). Output a list of (sid, line) pairs.

If fewer than 20 turns match, include only those that match. If zero match, skip to Step 4 no-topic path (load compact as-is).

Step 5c: Batch extract originals

Extract all 20 matched turns' originals from JSONL files in a single python script (one pass per JSONL file):

python3 << 'PYEOF'
import json, sys

# Dynamically populated: { "sid": { "jsonl_path": "...", "lines": [40, 83, ...] } }
extractions = {}

results = {}
for sid, info in extractions.items():
    target_lines = set(info["lines"])
    all_lines = {}
    with open(info["jsonl_path"]) as f:
        for i, raw in enumerate(f, 1):
            if i in target_lines or any(i > t for t in target_lines):
                all_lines[i] = raw

    for target_line in info["lines"]:
        d = json.loads(all_lines.get(target_line, '{}'))
        # Extract user content
        content = d.get("message", {}).get("content", "")
        if isinstance(content, list):
            user_text = " ".join(
                b["text"] for b in content
                if isinstance(b, dict) and b.get("type") == "text"
            )[:3000]
        else:
            user_text = str(content)[:3000]

        # Find assistant responses until next user turn
        assistants = []
        for j in range(target_line + 1, target_line + 100):
            if j not in all_lines:
                continue
            row = json.loads(all_lines[j])
            if row.get("type") == "user":
                break
            msg = row.get("message", {})
            if msg.get("role") == "assistant":
                texts = []
                for b in (msg.get("content", []) if isinstance(msg.get("content"), list) else []):
                    if isinstance(b, dict) and b.get("type") == "text" and b.get("text", "").strip():
                        texts.append(b["text"][:3000])
                if texts:
                    assistants.append("\n".join(texts))

        key = f"{sid}_L{target_line}"
        results[key] = {"user": user_text, "assistants": assistants}

# Write to temp file
output_path = "/tmp/continue-originals.json"
with open(output_path, "w") as f:
    json.dump(results, f, ensure_ascii=False)
print(f"Extracted {len(results)} turns to {output_path}")
PYEOF

Step 5d: Assemble temp file

Build the restored document by iterating compact.txt in order, replacing matched turns inline:

python3 << 'PYEOF'
import json, re, os

# Inputs (dynamically populated)
compact_paths = []  # ordered list of compact.txt paths
originals_path = "/tmp/continue-originals.json"
output_path = "/tmp/continue-restored.txt"

with open(originals_path) as f:
    originals = json.load(f)

matched_keys = set(originals.keys())

with open(output_path, "w") as out:
    for cpath in compact_paths:
        with open(os.path.expanduser(cpath)) as f:
            lines = f.readlines()

        i = 0
        while i < len(lines):
            line = lines[i]
            # Check if this is a user turn header
            m = re.match(
                r'\[Session:([a-f0-9]+) \S+ L(\d+)\]',
                line
            )
            if m:
                key = f"{m.group(1)}_L{m.group(2)}"
                if key in matched_keys:
                    orig = originals[key]
                    # Write header line as-is
                    out.write(line)
                    i += 1
                    # Write "-> N AI responses" line as-is
                    if i < len(lines) and lines[i].startswith("->"):
                        out.write(lines[i])
                        i += 1
                    # Replace numbered AI response lines with originals
                    ai_idx = 0
                    while i < len(lines) and re.match(r'\d+\.', lines[i]):
                        if ai_idx < len(orig["assistants"]):
                            out.write(f'{ai_idx + 1}. "{orig["assistants"][ai_idx]}"\n')
                        else:
                            out.write(lines[i])
                        ai_idx += 1
                        i += 1
                    continue
            out.write(line)
            i += 1

print(f"Assembled to {output_path} ({os.path.getsize(output_path)} bytes)")
PYEOF

Step 5e: Read restored file

Read the temp file (/tmp/continue-restored.txt) into conversation context using the Read tool. Use offset/limit chunks for large files. Then proceed to Step 6.

Important: The temp file is ephemeral — it may differ each time /continue is invoked with a different topic. The original compact.txt files remain unchanged.

Step 6: Final Completion Message

After restoration (whether 5A or 5B), produce the completion message.

Git history (optional)

If git is available, append commit history for the time range. Use the earliest firstActive among selected sessions as FROM, and the latest lastActive as TO:

git log --since="${FROM}" --until="${TO}" --format="%h %aI %s" --stat --no-merges 2>/dev/null

Last active context

You MUST review the last 5 messages from the restored context and provide a "Last 5 messages" section. Without it, the user has to ask "what was I doing?" separately, which defeats the purpose of /continue.

  1. Last 5 messages (where you left off): Show the last 5 USER messages ONLY (lines starting with [Session:) with [Session:{sid} L{n}] markers, sorted chronologically (oldest first → newest last). Do NOT include assistant messages. Copy the VERBATIM text from the preprocessed transcript — do NOT paraphrase or rewrite. If a message exceeds ~100 chars, hard-cut at 100 chars and append ....

  2. Session summary (2-4 bullets): What was accomplished, any pending decisions, background agents/tasks in progress.

Completion message format

---
[Context restored by /continue]
- {N} session(s) loaded ({date range})
- [Session:{sid} {ISO} L{n}] headers link to original transcripts at ~/.claude/projects/{PROJECT_HASH}/{SESSION_ID}.jsonl — use L{n} to read the exact line.
- Preprocessed caches: ~/.claude/claude-code-token-saver-data/{PROJECT_HASH}/{SESSION_ID}/compact.txt
- 💡 Next session: run `/clear` first, then `/continue` to restore context cheaply

**Last 5 messages:**
- [Session:{sid} L{n}] "{user message, truncated to ~100 chars}..."
- [Session:{sid} L{n}] "{user message}..."
- [Session:{sid} L{n}] "{user message}..."
- [Session:{sid} L{n}] "{user message}..."
- [Session:{sid} L{n}] "{user message}..."

**Session summary:**
{2-4 bullet points — what was accomplished, open items, pending decisions or in-progress tasks.}

---
💡 **Memory search prompt**: If your memory of a specific topic is vague, try this:
> There should be a previous conversation about ___. Find related messages in the text, and if any parts are truncated, use the session ID and line number to retrieve the full text from the original transcript.

The Memory search prompt block goes at the VERY END (after Last messages and Session summary), so it's the last thing the LLM/user sees.

Output Rules

  • Do NOT add any summary beyond the format specified in Step 6 above.
  • Do NOT output emoji status lines, cost calculations, token counts, or savings estimates.
  • Do NOT improvise additional statistics like "Restored context: X tokens" or "Estimated /compact cost".
  • The Step 6 format is the ONLY permitted final output. Follow it exactly.
  • The Memory search prompt block must appear exactly as specified above.
Install via CLI
npx skills add https://github.com/ww-w-ai/claude-code-token-saver --skill continue
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