deglacer

name	deglacer
description	MANDATORY gate BEFORE running jq on any .jsonl under ~/.claude/ or reading past CC sessions. Invoke FIRST when introspecting conversations, searching session history, parsing transcripts, or building tools that read ~/.claude/projects/ data. Provides the CC JSONL schema reference and `deglacer` CLI tool — prevents the 54-attempt fumble pattern where Claudes guess at field names. Triggers on 'what happened last session', 'find when we discussed', 'parse session', 'read conversation', 'session history', 'token usage', 'deglacer'. Do NOT use for git history (use git log) or your own current-session context. (user)
allowed-tools	["Bash","Read","Grep","Glob"]

Déglacer — CC Session JSONL Reference

Deglazing the pan to lift the fond — extracting the good bits from past sessions.

When to Use This Skill

You are working with Claude Code session data. This includes:

• Introspecting past conversations — "what did we discuss last session?", "when did we first talk about X?"
• Searching session history — finding sessions that mention a topic, tool, or file
• Parsing JSONL transcripts — extracting human messages, tool calls, thinking blocks, token usage
• Building tooling — anything that reads ~/.claude/projects/ session data
• Debugging session format — understanding why a jq query returns nothing

Do NOT guess at the schema. The CC JSONL format has multiple entry types, triple-duty user entries, streaming-duplicated message.ids, and inconsistent field presence across versions. This reference is the source of truth.

When NOT to Use

• Git history — use git log / git blame for code change history
• Current conversation state — you already have context, no need to parse your own session
• Non-CC JSONL files — this schema is specific to Claude Code sessions

---

deglacer — The CLI Tool

deglacer is the CC JSONL extraction CLI, installed as a uv tool. Use it instead of raw jq for structured extraction.

# Install (once) — from the source repo (local clone, else git+https):
uv tool install 'deglacer @ git+https://github.com/spm1001/deglacer'

# Use:
deglacer SESSION.jsonl

Commands

deglacer SESSION.jsonl                  # conversation text (human + assistant)
deglacer --summary SESSION.jsonl        # human messages only (what was discussed)
deglacer --with-tools SESSION.jsonl     # include tool call summaries
deglacer --with-thinking SESSION.jsonl  # include thinking blocks
deglacer --last 5 SESSION.jsonl         # last 5 turns only
deglacer --json SESSION.jsonl           # structured JSON output
deglacer --stats SESSION.jsonl          # session statistics (tokens, models, tools)
deglacer --timeline SESSION.jsonl       # timestamped turn log
deglacer --find "search term"           # search across recent sessions
deglacer --recent                       # list recent sessions (default 20)
deglacer --recent 10                    # list N most recent
deglacer --today                        # list today's sessions
deglacer --since 2026-03-25             # sessions since a date

Combining Flags

deglacer --with-tools --last 10 SESSION.jsonl    # recent turns with tools
deglacer --with-tools --with-thinking --json ...  # everything, structured
deglacer --summary --last 5 SESSION.jsonl         # quick recap of recent turns

---

File Discovery

Sessions live at:

~/.claude/projects/{encoded-cwd}/{session-uuid}.jsonl

Where {encoded-cwd} replaces / with - in the project path. Subagent transcripts: {session-uuid}/subagents/agent-{id}.jsonl.

Find recent sessions:

ls -lt ~/.claude/projects/*/*.jsonl | head -20

Find sessions for a project:

ls -lt ~/.claude/projects/-home-modha-Repos-myproject/*.jsonl

Match session to slug/name:

head -1 SESSION.jsonl | jq '{sessionId, slug, version}'

---

The Schema

Each line in a .jsonl file is one JSON object. The .type field discriminates.

Entry Types

Type	Purpose	Has timestamp?
assistant	Claude's response	Yes
user	Human msg / tool result / skill injection	Yes
progress	Streaming bash/hook/agent output	Yes
system	Turn timing, API errors, slash commands	Yes
summary	Context compaction	No
queue-operation	Input typed while Claude busy	Yes
last-prompt	Records last user text	No
custom-title	User-set session name	No
agent-name	Session agent name	No
file-history-snapshot	File backup state	No
pr-link	Created PR reference	Yes
saved_hook_context	Persisted hook output	Yes

Common Fields (on user/assistant entries)

uuid            string    Unique entry ID
parentUuid      string?   Previous entry (linked list)
sessionId       string    Session UUID (matches filename)
timestamp       string    ISO 8601
cwd             string    Working directory
gitBranch       string    Current git branch
version         string    CC version (e.g. "2.1.85")
slug            string    Human-readable session name
userType        string    Always "external"
entrypoint      string    "cli" (absent in v2.0.x)
isSidechain     boolean   Side conversation flag

assistant entries

{
  "type": "assistant",
  "message": {
    "id": "msg_...",
    "type": "message",
    "role": "assistant",
    "model": "claude-opus-4-6",
    "content": [/* content blocks */],
    "stop_reason": "end_turn" | "tool_use" | null,
    "usage": {
      "input_tokens": 119,
      "cache_creation_input_tokens": 18531,
      "cache_read_input_tokens": 36004,
      "output_tokens": 500
    }
  },
   

Content block types:

• {type: "text", text: "..."} — Claude's text
• {type: "tool_use", id: "toolu_...", name: "Bash", input: {...}} — tool call
• {type: "thinking", thinking: "...", signature: "..."} — extended thinking

DRAGON: Multiple entries share the same message.id. CC streams incremental updates. Merge content blocks by message.id, dedup tool_use blocks by their id field. deglacer handles this automatically.

DRAGON: stop_reason is null in older sessions (pre-v2.1.79).

user entries (TRIPLE DUTY)

The user type serves three purposes. Discriminate with:

Subtype	How to detect	Content shape
Human message	typeof content === "string", has permissionMode	String
Tool result	Has toolUseResult	Array of {type: "tool_result"}
Skill/system injection	isMeta: true	Array of {type: "text"}

Human message:

{
  "type": "user",
  "message": {"role": "user", "content": "the actual human text"},
  "permissionMode": "default",
  "promptId": "..."
}

Tool result:

{
  "type": "user",
  "message": {"role": "user", "content": [
    {"type": "tool_result", "tool_use_id": "toolu_...", "content": "output text"}
  ]},
  "toolUseResult": {/* shape varies by tool */},
  "sourceToolAssistantUUID": "..."
}

toolUseResult shapes:

Tool	Keys
Bash	stdout, stderr, interrupted, isImage, noOutputExpected
Bash (large)	+ persistedOutputPath, persistedOutputSize
Write/Edit	content, filePath, originalFile, structuredPatch, type
Read	file, type
Agent	agentId, agentType, content, prompt, status, totalDurationMs, totalTokens, totalToolUseCount, usage
Error	Bare string: "User rejected tool use"

Token Counting

The input_tokens field is ONLY the non-cached portion. Real input = input_tokens + cache_creation_input_tokens + cache_read_input_tokens.

summary entries (minimal)

{"type": "summary", "leafUuid": "...", "summary": "short text"}

No uuid, parentUuid, timestamp, version, or sessionId. Three fields only.

system entries

.subtype discriminates:

• turn_duration: {durationMs, messageCount}
• api_error: {error: {status, headers, requestID}, retryInMs, retryAttempt}
• local_command: {content: "...", level: "info"} — slash commands

---

jq Recipes (when deglacer isn't enough)

Quick schema discovery (do this FIRST, not head | jq .):

jq -r '.type' FILE.jsonl | sort | uniq -c | sort -rn

Extract human messages:

jq -r 'select(.type == "user" and .permissionMode and (.isMeta | not))
  | .message.content' FILE.jsonl

Extract assistant text (handles multi-block):

jq -r 'select(.type == "assistant")
  | [.message.content[]? | select(.type == "text") | .text]
  | select(length > 0) | join("\n")' FILE.jsonl

Extract tool calls:

jq -c 'select(.type == "assistant")
  | [.message.content[]? | select(.type == "tool_use")
  | {tool: .name, input_keys: (.input | keys)}]
  | select(length > 0)' FILE.jsonl

Session timeline:

jq -c 'select(.type == "user" or .type == "assistant")
  | {ts: .timestamp, type, model: .message.model?}' FILE.jsonl

Token usage per turn:

jq -c 'select(.type == "assistant") | .message.usage
  | {in: (.input_tokens + .cache_creation_input_tokens + .cache_read_input_tokens),
     out: .output_tokens}' FILE.jsonl

Find sessions mentioning a term:

# Prefer: deglacer --find "term"
# Raw jq fallback:
for f in ~/.claude/projects/*/*.jsonl; do
  if jq -e 'select(.type == "user" and (.message.content | type) == "string"
    and (.message.content | test("term"; "i")))' "$f" >/dev/null 2>&1; then
    echo "$f"
  fi
done

---

Anti-Patterns (DON'T)

Don't	Why	Do instead
jq -s '.' on JSONL	Slurps entire file into memory as array	Stream line-by-line (default jq behaviour)
jq '.[]' on JSONL	JSONL isn't an array	Each line is already a separate object
.role at top level	Role is at .message.role, not top-level	Use .type for entry type
.type == "message"	No such type	Types: user, assistant, progress, system, etc.
.type == "human"	No such type	.type == "user" + check it's not a tool result
head -1 | jq . for discovery	Wastes a turn, first line may be queue-operation	jq -r '.type' | sort | uniq -c
Assume content is string	Assistant content is always array; user content varies	Check type before accessing
2>/dev/null on everything	Hides real errors	Understand the schema, don't hedge
Guess at field names	39% of jq-on-.claude commands are schema discovery	Read this reference