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Inspect Claude Code session JSONL files at ~/.claude/projects/ to extract real conversation telemetry: token counts (input/output/cache reads/cache writes), assistant turn counts, human prompt counts, tool-use counts, compaction boundaries, and the contents of compaction summarie
Inspect Claude Code session JSONL files at ~/.claude/projects/ to extract real conversation telemetry: token counts (input/output/cache reads/cache writes), assistant turn counts, human prompt counts, tool-use counts, compaction boundaries, and the contents of compaction summaries. Use this skill when the user asks "how many tokens did this session use", "how many prompts have I sent", "show me the stats for this conversation", "what got compacted", "where are the compaction boundaries", "introspect the session", "do brain surgery on the JSONL", or wants any data point that lives inside the on-disk session log rather than the live context window. Inspired by Tal Raviv's "I wanted to know how compaction works" article.
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Claude Code persists every conversation as a JSONL file on disk. This skill is the recipe for opening one and pulling out the numbers you actually want — token usage, prompt counts, compaction events, tool calls — without guessing.
~/.claude/projects/<encoded-cwd>/<session-uuid>.jsonl
<encoded-cwd> is the absolute path of the project's working directory with / replaced by - and a leading -. Example: /Users/swyx/Work/foo → -Users-swyx-Work-foo.
Each line is one event. The interesting type values:
| type | what it is |
|---|---|
user | a real user message OR a tool result (distinguished by toolUseResult being non-null) |
assistant | an assistant turn (one model response). message.usage has the token counts. |
system | system messages (mostly compaction-related) |
file-history-snapshot | edited-file snapshots used for undo |
attachment | image/file attachments |
permission-mode | permission mode toggles |
# 1. encode current working directory
ENC="-$(pwd | sed 's,/,-,g' | sed 's/^-//')"
# 2. list the project's session files, newest first
ls -t "$HOME/.claude/projects/$ENC/"
# 3. the most recent .jsonl is usually the live one
SESSION="$HOME/.claude/projects/$ENC/$(ls -t "$HOME/.claude/projects/$ENC/" | head -1)"
echo "$SESSION"
If you know the session UUID (Claude Code shows it, and image-cache paths embed it), you can grep all projects:
find ~/.claude/projects -name '<uuid>.jsonl'
The stats.sh script in this skill folder takes a session path and prints token totals, turn counts, prompt counts, tool-use counts, and any compaction events.
bash stats.sh "$SESSION"
If you don't have the script handy, here are the inline jq one-liners.
jq -s '
[.[] | select(.message.usage)] |
{
assistant_turns: length,
input_tokens: (map(.message.usage.input_tokens // 0) | add),
output_tokens: (map(.message.usage.output_tokens // 0) | add),
cache_read_tokens: (map(.message.usage.cache_read_input_tokens // 0) | add),
cache_create_tokens: (map(.message.usage.cache_creation_input_tokens // 0)| add)
}
' "$SESSION"
input_tokens is the FRESH (non-cached) input. cache_read_tokens is the dominant number on long sessions — it's how much was re-read from prompt cache. cache_create_tokens is what got newly written into the cache. Effective total tokens processed = input + cache_read + cache_create.
jq -r '.type' "$SESSION" | sort | uniq -c
A type:"user" line is a human message only if toolUseResult is null. Even then, the content may be a system-injected reminder, not the human's words.
jq -r '
select(.type == "user" and .toolUseResult == null) |
(.message.content
| if type == "string" then .
else (map(select(.type == "text") | .text) | join("\n"))
end)
' "$SESSION" > /tmp/prompts.txt
# total non-empty user message blocks
grep -cv '^$' /tmp/prompts.txt
# distinct human messages = blocks not starting with <system-reminder> or <command-
awk '
BEGIN { n = 0; cur = "" }
/^$/ { if (cur != "" && cur !~ /^<system-reminder>/ && cur !~ /^<command-/) n++; cur=""; next }
{ if (cur=="") cur=$0 }
END { if (cur != "" && cur !~ /^<system-reminder>/ && cur !~ /^<command-/) n++; print n }
' /tmp/prompts.txt
(Blunt but works. If you want surgical accuracy, parse the content array and skip blocks whose first text element is a <system-reminder> tag.)
jq -r '
select(.type == "assistant") |
.message.content[]? |
select(.type == "tool_use") |
.name
' "$SESSION" | sort | uniq -c | sort -rn
Compaction inserts a system event with subtype:"compact_boundary" (older builds may use isCompactSummary on the next user message). The summary itself is the next user message, prefixed with "This session is being continued from a previous conversation that ran out of context."
# count compaction events
jq -r 'select(.type=="system" and (.subtype // "") == "compact_boundary") | .timestamp' "$SESSION" | wc -l
# was each one auto or manual?
jq -r '
select(.type == "system" and (.subtype // "") == "compact_boundary") |
{ts: .timestamp, trigger: (.compactMetadata.trigger // "unknown"), preTokens: (.compactMetadata.preCompactTokens // null)}
' "$SESSION"
# read the compaction summaries (the actual contents that survived)
jq -r '
select(.type == "user" and (.isCompactSummary == true or
((.message.content // "") | tostring | test("session is being continued from a previous conversation"))))
| (.message.content | if type == "string" then . else (map(select(.type=="text").text)|join("\n")) end)
' "$SESSION" | less
jq -r '
select(.message.usage) |
[.timestamp,
(.message.usage.input_tokens // 0),
(.message.usage.output_tokens // 0),
(.message.usage.cache_read_input_tokens // 0)]
| @tsv
' "$SESSION" | column -t
This is how you find the one tool result that bloated your context — sort by cache_read ascending across the session and watch for the jump.
type:"user" is overloaded. Tool results are also type:"user". Always filter on toolUseResult == null to get human turns.input_tokens looks tiny on long sessions. That's correct — it's the delta sent uncached. Almost everything flows through cache_read_input_tokens.ls -t | head is your friend./resume. If you want to do "brain surgery" (Tal Raviv's term), copy the file out, edit the copy, and use claude --resume <copied-uuid> from a clean directory.~/.claude/projects/name: claude-session-introspect description: | Inspect Claude Code session JSONL files at ~/.claude/projects/ to extract real conversation telemetry: token counts (input/output/cache reads/cache writes), assistant turn counts, human prompt counts, tool-use counts, compaction boundaries, and the contents of compaction summaries. Use this skill when the user asks "how many tokens did this session use", "how many prompts have I sent", "show me the stats for this conversation", "what got compacted", "where are the compaction boundaries", "introspect the session", "do brain surgery on the JSONL", or wants any data point that lives inside the on-disk session log rather than the live context window. Inspired by Tal Raviv's "I wanted to know how compaction works" article. license: MIT compatibility: | Requires `jq`. Sessions live at `~/.claude/projects/<encoded-cwd>/<session-uuid>.jsonl`. The encoded-cwd is the absolute working directory with `/` replaced by `-` and a leading `-`. Each line is a JSON object with `type`, `message`, `toolUseResult`, etc. metadata: author: swyxio version: "1.0" last-updated: "2026-04-08" primary-tools: jq, bash
---
name: claude-session-introspect
description: |
Inspect Claude Code session JSONL files at ~/.claude/projects/ to extract real conversation telemetry: token counts (input/output/cache reads/cache writes), assistant turn counts, human prompt counts, tool-use counts, compaction boundaries, and the contents of compaction summaries. Use this skill when the user asks "how many tokens did this session use", "how many prompts have I sent", "show me the stats for this conversation", "what got compacted", "where are the compaction boundaries", "introspect the session", "do brain surgery on the JSONL", or wants any data point that lives inside the on-disk session log rather than the live context window. Inspired by Tal Raviv's "I wanted to know how compaction works" article.
license: MIT
compatibility: |
Requires `jq`. Sessions live at `~/.claude/projects/<encoded-cwd>/<session-uuid>.jsonl`. The encoded-cwd is the absolute working directory with `/` replaced by `-` and a leading `-`. Each line is a JSON object with `type`, `message`, `toolUseResult`, etc.
metadata:
author: swyxio
version: "1.0"
last-updated: "2026-04-08"
primary-tools: jq, bash
---
# Claude Session Introspect
Claude Code persists every conversation as a JSONL file on disk. This skill is the recipe for opening one and pulling out the numbers you actually want — token usage, prompt counts, compaction events, tool calls — without guessing.
## Where sessions live
```
~/.claude/projects/<encoded-cwd>/<session-uuid>.jsonl
```
`<encoded-cwd>` is the absolute path of the project's working directory with `/` replaced by `-` and a leading `-`. Example: `/Users/swyx/Work/foo` → `-Users-swyx-Work-foo`.
Each line is one event. The interesting `type` values:
| type | what it is |
|---|---|
| `user` | a real user message **OR** a tool result (distinguished by `toolUseResult` being non-null) |
| `assistant` | an assistant turn (one model response). `message.usage` has the token counts. |
| `system` | system messages (mostly compaction-related) |
| `file-history-snapshot` | edited-file snapshots used for undo |
| `attachment` | image/file attachments |
| `permission-mode` | permission mode toggles |
## Quick locate: find the current session
```bash
# 1. encode current working directory
ENC="-$(pwd | sed 's,/,-,g' | sed 's/^-//')"
# 2. list the project's session files, newest first
ls -t "$HOME/.claude/projects/$ENC/"
# 3. the most recent .jsonl is usually the live one
SESSION="$HOME/.claude/projects/$ENC/$(ls -t "$HOME/.claude/projects/$ENC/" | head -1)"
echo "$SESSION"
```
If you know the session UUID (Claude Code shows it, and image-cache paths embed it), you can grep all projects:
```bash
find ~/.claude/projects -name '<uuid>.jsonl'
```
## The headline stats (one-shot)
The `stats.sh` script in this skill folder takes a session path and prints token totals, turn counts, prompt counts, tool-use counts, and any compaction events.
```bash
bash stats.sh "$SESSION"
```
If you don't have the script handy, here are the inline jq one-liners.
### Token totals across the whole session
```bash
jq -s '
[.[] | select(.message.usage)] |
{
assistant_turns: length,
input_tokens: (map(.message.usage.input_tokens // 0) | add),
output_tokens: (map(.message.usage.output_tokens // 0) | add),
cache_read_tokens: (map(.message.usage.cache_read_input_tokens // 0) | add),
cache_create_tokens: (map(.message.usage.cache_creation_input_tokens // 0)| add)
}
' "$SESSION"
```
`input_tokens` is the FRESH (non-cached) input. `cache_read_tokens` is the dominant number on long sessions — it's how much was re-read from prompt cache. `cache_create_tokens` is what got newly written into the cache. Effective total tokens processed = `input + cache_read + cache_create`.
### Counts by event type
```bash
jq -r '.type' "$SESSION" | sort | uniq -c
```
### Real human prompts (excluding tool results and system reminders)
A `type:"user"` line is a human message **only if** `toolUseResult` is null. Even then, the content may be a system-injected reminder, not the human's words.
```bash
jq -r '
select(.type == "user" and .toolUseResult == null) |
(.message.content
| if type == "string" then .
else (map(select(.type == "text") | .text) | join("\n"))
end)
' "$SESSION" > /tmp/prompts.txt
# total non-empty user message blocks
grep -cv '^$' /tmp/prompts.txt
# distinct human messages = blocks not starting with <system-reminder> or <command-
awk '
BEGIN { n = 0; cur = "" }
/^$/ { if (cur != "" && cur !~ /^<system-reminder>/ && cur !~ /^<command-/) n++; cur=""; next }
{ if (cur=="") cur=$0 }
END { if (cur != "" && cur !~ /^<system-reminder>/ && cur !~ /^<command-/) n++; print n }
' /tmp/prompts.txt
```
(Blunt but works. If you want surgical accuracy, parse the content array and skip blocks whose first text element is a `<system-reminder>` tag.)
### Tool calls — how many and which tools
```bash
jq -r '
select(.type == "assistant") |
.message.content[]? |
select(.type == "tool_use") |
.name
' "$SESSION" | sort | uniq -c | sort -rn
```
### Compaction boundaries — where, why, and what survived
Compaction inserts a `system` event with `subtype:"compact_boundary"` (older builds may use `isCompactSummary` on the next user message). The summary itself is the *next* user message, prefixed with "This session is being continued from a previous conversation that ran out of context."
```bash
# count compaction events
jq -r 'select(.type=="system" and (.subtype // "") == "compact_boundary") | .timestamp' "$SESSION" | wc -l
# was each one auto or manual?
jq -r '
select(.type == "system" and (.subtype // "") == "compact_boundary") |
{ts: .timestamp, trigger: (.compactMetadata.trigger // "unknown"), preTokens: (.compactMetadata.preCompactTokens // null)}
' "$SESSION"
# read the compaction summaries (the actual contents that survived)
jq -r '
select(.type == "user" and (.isCompactSummary == true or
((.message.content // "") | tostring | test("session is being continued from a previous conversation"))))
| (.message.content | if type == "string" then . else (map(select(.type=="text").text)|join("\n")) end)
' "$SESSION" | less
```
### Per-turn token usage (for spotting blowups)
```bash
jq -r '
select(.message.usage) |
[.timestamp,
(.message.usage.input_tokens // 0),
(.message.usage.output_tokens // 0),
(.message.usage.cache_read_input_tokens // 0)]
| @tsv
' "$SESSION" | column -t
```
This is how you find the one tool result that bloated your context — sort by `cache_read` ascending across the session and watch for the jump.
## Gotchas
- **`type:"user"` is overloaded.** Tool results are also `type:"user"`. Always filter on `toolUseResult == null` to get human turns.
- **`input_tokens` looks tiny on long sessions.** That's correct — it's the *delta* sent uncached. Almost everything flows through `cache_read_input_tokens`.
- **The "live" session file isn't always the newest.** If multiple Claude Code windows are open in the same project, both write to the same project folder. Disambiguate by UUID — the chat header and image-cache paths both expose it.
- **JSONL files grow without bound.** A long-running project folder can have hundreds of session files. `ls -t | head` is your friend.
- **Don't edit a live JSONL.** Claude Code reads it back on `/resume`. If you want to do "brain surgery" (Tal Raviv's term), copy the file out, edit the copy, and use `claude --resume <copied-uuid>` from a clean directory.
## When to reach for this skill
- "How many tokens has this session burned?"
- "How many prompts have I sent today?"
- "Where did compaction kick in and what got summarized?"
- "Which tool call blew up the context?"
- Building a stats display, leaderboard, or "built with Claude Code" badge that needs real numbers.
- Forensics on a session that went sideways — replaying tool calls in order.
## Reference
- Tal Raviv, "I wanted to know how compaction works" — https://www.talraviv.co/p/i-wanted-to-know-how-compaction-works
- Claude Code docs on session storage — `~/.claude/projects/`
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
69/100
Promising
Trust
66/100
Sandbox only
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"slug": "swyxio-claude-session-introspect",
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"description": "Inspect Claude Code session JSONL files at ~/.claude/projects/ to extract real conversation telemetry: token counts (input/output/cache reads/cache writes), assistant turn counts, human prompt counts, tool-use counts, compaction boundaries, and the contents of compaction summaries. Use this skill when the user asks \"how many tokens did this session use\", \"how many prompts have I sent\", \"show me the stats for this conversation\", \"what got compacted\", \"where are the compaction boundaries\", \"introspect the session\", \"do brain surgery on the JSONL\", or wants any data point that lives inside the on-disk session log rather than the live context window. Inspired by Tal Raviv's \"I wanted to know how compaction works\" article.",
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},
{
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"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 79,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Stars/forks activity: 156 stars, 9 forks; issue activity unavailable in current metadata",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 69,
"label": "Promising"
},
"supply": {
"track": "Research and knowledge work",
"scenario": "Research agents",
"maintenance": "20d since push",
"risk": "Needs review"
},
"alternative_skills": [
{
"slug": "yanliudesign-mono-color-skill",
"name": "mono-color",
"url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
"stars": 1919,
"install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
"trust_score": 85,
"audit_score": 93
}
],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"high-compliance environments without internal security review",
"No OpenAgentSkill engagement data yet",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution"
],
"agent_contract": {
"task_input": "Use claude-session-introspect in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 74/100 Strong shortlist",
"Audit: 79/100 Needs review",
"Safety: 39/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "swyxio-claude-session-introspect (claude-session-introspect)",
"install_command": "npx skills add swyxio/skills --skill claude-session-introspect",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "swyxio-claude-session-introspect",
"task": "Use claude-session-introspect in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/swyxio-claude-session-introspect",
"api": "https://www.openagentskill.com/api/agent/skills/swyxio-claude-session-introspect",
"audit": "https://www.openagentskill.com/skills/swyxio-claude-session-introspect/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=swyxio-claude-session-introspect&task=Use%20claude-session-introspect%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20claude-session-introspect%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20claude-session-introspect%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/swyxio-claude-session-introspect/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/swyxio-claude-session-introspect"
}
}Listing source
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Audit
79/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.