s-continue

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

Two tools, one history. Sessions come from Claude Code (~/.claude/projects/) and from Codex (~/.codex/sessions/). A Codex rollout is rewritten into the shape Claude Code writes, one output line per input line, so both are read by the same code and an L{n} marker still points at the Codex original's line. Work stopped in one tool is therefore resumable in the other.

Help

ONLY show help if the user's argument literally contains the word "help" (e.g. /s-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:

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

Options:
  (nothing)     Show session list (Claude Code + Codex), 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
  claude|codex  Restrict the list to one tool
  help          Show this help

Examples:
  /s-continue
  /s-continue codex
  /s-continue codex : rust migration

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

What a restore contains

compact.txt is a preview, not a transcript. The preprocessor truncated it when it was built:

Every turn of a selected session is restored. Within one turn the first 24 and last 24 replies are kept as stored and the ones in between are shortened to 50 chars — during an autonomous run the assistant answers dozens of times under one user turn, and without that cut a single turn is unbounded (60 replies under one message measured 27 KB on their own). No reply is dropped: the -> N AI responses at lines X-Y pointer above each turn locates the originals in the JSONL, and the reply's own number is its position within that range.

The # compact-format: preamble and the trailing # Session references: footer are kept. On a Codex session that footer names the original rollout — it is the only line saying which file an L{n} marker addresses, so dropping it breaks recovery.

The only path back to real original text is a topic (Step 5), which re-reads the matched turns from the JSONL at up to 3,000 chars each — and only for the top 20 turns that match it.

A --level N argument from an older habit is accepted and ignored.

Language

Detect the user's language from their message accompanying the /s-continue invocation. If no message was provided (bare /s-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: /s-continue last

If the user invoked /s-continue last, skip the session list entirely. Run list-sessions with --limit 3 (same flags as Step 1). 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 /s-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}"
PLUGIN_ROOT="${CLAUDE_PLUGIN_ROOT:-${CODEX_PLUGIN_ROOT}}"
node "${PLUGIN_ROOT}/scripts/list-sessions.js" "${TRANSCRIPTS_DIR}" \
  --source all --cwd "${PWD}" --current-source claude --limit 11 --offset 0

Resolving PLUGIN_ROOT. Claude Code exports CLAUDE_PLUGIN_ROOT; Codex does not always export CODEX_PLUGIN_ROOT. If both are empty, use the directory that contains THIS SKILL.md, two levels up — the host tells you that path when it loads the skill. Do not guess an install location.

When this skill runs under Codex, pass --current-source codex instead of claude in every command below.

--current-source names the tool this skill is running in, so isCurrent marks the session being written right now instead of whichever transcript happens to be newest. --source takes all (default for this skill), claude, or codex. Pass codex or claude when the user named one tool. Codex keeps every session in one global tree, so --cwd is what scopes them to this project; Codex subagent rollouts are excluded, the same way Claude subtask transcripts are. Non-interactive one-shot runs are excluded too: claude -p (entrypoint: "sdk-cli") and codex exec / SDK runs (session_meta source "exec"). The running session is always kept.

Each result carries source (claude | codex) and, for Codex, originalPath (the rollout the line numbers belong to) alongside path (the normalized copy the other scripts read).

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 (Claude Code + Codex).

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.

| # | Tool | Started | Last active | First message | Last message | Size |
|---|------|---------|-------------|---------------|--------------|------|
| 1 | CC | Mar 31 09:00 | today 14:05 | "improve the skill..." | "ok go ahead..." | 122KB · 3 msgs |
| 2 | Codex | 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 implementation") — 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

First, strip any --level N (or -lN) token from anywhere in the argument and discard it — it is accepted for older habits and changes nothing.

Next, if the remaining ARGUMENT is claude or codex (alone or before a : topic), that is a source filter, not a selection — re-run Step 1 with --source claude or --source codex and show the narrowed list.

Then split the rest 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 implementation → sessions [1, 2, 3, 4], topic = "PDCA implementation"
• : error handling → only #0 (default), topic = "error handling"
• `` (empty) → default selection, no topic
• 1,3 : auth bug → sessions [1, 3], topic = "auth bug"

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.
# TRANSCRIPT_PATH is the `path` field from list-sessions.
node "${PLUGIN_ROOT}/scripts/preprocess.js" "${TRANSCRIPT_PATH}"

# Codex sessions only — name the rollout the L{n} markers belong to, so the
# footer points a reader at the real file instead of the normalized copy:
node "${PLUGIN_ROOT}/scripts/preprocess.js" "${TRANSCRIPT_PATH}" --original "${ORIGINAL_PATH}"

The cache file is at (Claude Code sessions; a Codex session's cache is the same path with codex/ inserted after super-token-saver-data/ — restore.js resolves both, so only build this by hand to inspect the cache):

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

restore.js --before-boundary (Step 4) already does this cut, and finds the boundary from the compact file's own System: "[auto-compact boundary]" block rather than from a line number — so Step 4 alone is enough and this step is only worth running on its own to inspect the cache.

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

Render every turn of each selected session. For a compacted current session, restore only the pre-boundary content. There are no restore levels.

Compact text is a sequence of blocks, each starting with a [Session:...] header line. Cut on that boundary, never on raw line counts, or a turn gets split in half.

# TRANSCRIPT = the `path` field from list-sessions (for Codex, that is the rollout —
#              restore.js normalizes it and keeps the original's line numbers).
# --before-boundary cuts everything from the LAST compaction onward, which is what a
#   compacted current session needs; leave it off to render a whole past session.
node "${PLUGIN_ROOT}/scripts/restore.js" "${TRANSCRIPT}" [--before-boundary] --out "${OUT}"

restore.js owns the rendering and refreshes the compact cache first (so Step 3 is folded in). Do not reimplement the rendering here — one rule with two copies is how this skill and the after-compact hook drifted apart once already. (That hook now has its own path, restore-ledger.js, which restores verbatim from a per-session ledger; it does not read compact.txt.)

Then:

• No topic → Read the whole file. For files over ~10K tokens read in chunks using offset/limit. Always read the ENTIRE file — never skip sections. Proceed to Step 6.
• Topic provided → Do NOT Read compact.txt yet. Proceed to Step 5.

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):

jsonl_path is the path field from list-sessions, never originalPath. For a Codex session those differ: the extractor below parses the shape Claude Code writes, so handing it the raw Codex rollout produces empty user text instead of an error — a silent, plausible-looking failure. The normalized copy carries the same line numbers, so L{n} still lands on the right turn.

python3 << 'PYEOF'
import json, sys

# Dynamically populated: { "sid": { "jsonl_path": "...", "lines": [40, 83, ...] } }
# jsonl_path = list-sessions `path` (the normalized copy for Codex), NOT `originalPath`.
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 /s-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 /s-continue.

1. Last 5 messages (where you left off): When sessions from both tools were restored, label each line with its tool. 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 /s-continue]
- {N} session(s) loaded ({date range}) — {n} Claude Code, {m} Codex
- {N} turns restored in full; {m} long turns had their middle replies shortened to 50 chars (say "none" if none)
- [Session:{sid} {ISO} L{n}] headers link to the original transcript — Claude Code at ~/.claude/projects/{PROJECT_HASH}/{SESSION_ID}.jsonl, Codex at the `originalPath` from list-sessions. Use L{n} to read the exact line; the numbering is the original's in both cases.
- Preprocessed caches: ~/.claude/super-token-saver-data/{PROJECT_HASH}/{SESSION_ID}/compact.txt (Codex: …-data/codex/{PROJECT_HASH}/{SESSION_ID}/compact.txt)
- 💡 Next session: run `/clear` first, then `/s-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.

Step 7: Auto-load a /s-compact handoff (if present)

/s-compact (the write-side pair of this skill) may have saved a handoff for this project — the distilled non-dialogue layer (subagent findings, tool-output numbers, process lessons) that the transcript restore above cannot recover. Load it automatically so the user never has to paste it.

PROJECT_HASH=$(echo "${PWD}" | sed 's/[^a-zA-Z0-9]/-/g')
HANDOFF="${HOME}/.claude/super-token-saver-data/${PROJECT_HASH}/handoff.md"
[ -f "${HANDOFF}" ] && echo "FOUND ${HANDOFF}" || echo "none"

• If it exists: Read it fully into context (it complements the restored transcript — it holds what the transcript does not). Then mark it consumed so a stale handoff is never silently re-applied on a later /s-continue:

  mv "${HANDOFF}" "${HANDOFF%.md}.applied.md"

  Add one line to the completion message: - Handoff loaded from /s-compact (non-dialogue context: subagents, measurements, lessons).
• If it does not exist: do nothing extra — the transcript restore stands on its own. (This is the /s-continue-alone path: fast context restore with no wasted /compact tokens.)

Run this step AFTER the transcript restore (Steps 1–6) so the handoff layers on top of it.

Step 7.5: Auto-load the latest super-compact summary (if present)

When super-compact (the Claude Code mod) compacted a restored session, it persisted each compaction's prepared summary to <session cache>/super-compact/<n>.md. That summary holds what the transcript restore cannot recover for the automatic path: teammate reports and subagent completion notices verbatim, tool-output numbers, and the reasons behind directives. The /s-continue dialogue restore deliberately leaves teammate messages, subagent notices and idle notifications out of compact.txt, so this summary is the only place that content survives for those spans.

For each restored session, load ONLY the highest-numbered file that has text — its section A already carries every earlier compaction, so the latest one is enough. A file holding only whitespace carries no summary and is skipped:

SC_DIR="${HOME}/.claude/super-token-saver-data/${PROJECT_HASH}/${SESSION_ID}/super-compact"
LATEST=$(for f in $(ls "${SC_DIR}"/[0-9]*.md 2>/dev/null | sort -V); do grep -q '[^[:space:]]' "$f" && echo "$f"; done | tail -1)
[ -n "${LATEST}" ] && echo "FOUND ${LATEST}" || echo "none"

• If one exists: Read it fully into context. It complements the restored transcript. Add one line to the completion message: - super-compact summary loaded (teammate/subagent results and measurements from compacted spans).
• If none exists: do nothing — the session was never compacted by the mod, or ran on Codex / older Claude Code (its after-compact restore already put that content back verbatim at the time).

Do NOT consume or move these files: they are per-session history the mod keeps writing, not a one-shot handoff like /s-compact's.

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.