Distill Sessions

Distill Sessions

Turn raw session transcripts into a ranked list of concrete improvements. Read-only analysis of the logs; never modify the log files. Output a numbered proposal list with one verbatim (redacted) evidence line per finding, then let the user pick which to apply.

Where the logs live

• Claude Code: ~/.claude/projects//*.jsonl (top-level sessions). Per-session subagent transcripts are under /subagents/agent-*.jsonl — skip these when counting "sessions"; they're part of a parent.
• Codex: ~/.codex/sessions////rollout-*.jsonl.

Both are JSONL — one event per line.

1. Pick the N most recent sessions

Default N = 50 unless the user gives a number. date/strftime may be missing from the shell — use perl for timestamps.

{ find ~/.claude/projects -name '*.jsonl' -type f | grep -v '/subagents/'; \
  find ~/.codex/sessions -name 'rollout-*.jsonl' -type f; } \
| xargs stat -f '%m %z %N' | sort -rn | head -50 \
| perl -lane 'use POSIX qw(strftime); my($m,$s,@p)=@F; printf "%s %7dKB %s\n", strftime("%Y-%m-%d %H:%M",localtime($m)), $s/1024, join(" ",@p)'

2. Extract signal (use jq — do NOT cat whole files)

Files are large and full of tool-output noise. jq is the right tool. Two schemas:

Claude Code

# human-typed messages (string form)
jq -rc 'select(.type=="user" and (.message.content|type=="string")) | .message.content' FILE
# human messages (array form; skip /system-reminder noise by eye)
jq -rc 'select(.type=="user" and (.message.content|type=="array")) | .message.content[]? | select(.type=="text") | .text' FILE
# bash commands the assistant ran
jq -rc 'select(.type=="assistant") | .message.content[]? | select(.type=="tool_use" and .name=="Bash") | .input.command' FILE
# tool errors
jq -rc 'select(.type=="user") | .message.content[]? | select(.type=="tool_result" and .is_error==true) | (.content|if type=="array" then (map(.text//"")|join(" ")) else tostring end)' FILE

Codex

# human messages (the FIRST is an AGENTS.md preamble — ignore it)
jq -rc 'select(.type=="event_msg" and .payload.type=="user_message") | .payload.message' FILE
# shell commands
jq -rc 'select(.type=="response_item" and .payload.type=="function_call") | .payload.arguments' FILE
# command outputs (grep for errors)
jq -rc 'select(.type=="response_item" and .payload.type=="function_call_output") | (.payload.output|tostring)' FILE | grep -iE 'error|not found|exception|fatal|denied' | head

Surface corrections fast by grepping extracted human messages: grep -iE "no,|actually|that.?s wrong|don.?t |stop |instead|you should have|i told you|revert|why did you|wrong"

3. Fan out — one subagent per batch

50 sessions won't fit one context. Split the file list into ~7 round-robin batches (so big files spread out) and dispatch one subagent per batch in parallel, each with the jq cheat-sheet above and an identical brief. Each subagent returns a structured findings list; the orchestrator dedupes across batches and synthesizes. Round-robin assignment:

awk '{print $NF}' top.txt | awk '{ b=((NR-1)%7)+1; print > ("batch_" b ".txt") }'

4. What to find (four categories)

1. Corrections — the user pushed back ("no", "actually", "that's wrong", "don't do that", reverting/redoing). Highest value — capture every one.
2. Repeated / errored commands — a command or tool that errored, or was retried several times before working. Note what finally fixed it.
3. Repeated setup steps — the same setup/config/env/login/build/cache-clear rediscovered across more than one session.
4. Content moments — something worth an article, tweet, tutorial, diagram, prompt, product idea, or example.

5. Redaction (mandatory)

In every quoted evidence line, replace emails, API keys, tokens, secrets, passwords, and bearer strings with [REDACTED]. Keep quotes short.

6. Output — a numbered proposal list

For each finding, give: the proposal, the one verbatim (redacted) evidence line it came from (with session basename), the destination, and a one-sentence why. Destinations:

• a content idea to draft / add to the idea library
• a line to add to CLAUDE.md / AGENTS.md (name the file)
• a slash command or skill to add or update (name it)
• a hook that should run automatically
• a tool or CLI that should be fixed
• a config or settings change
• or nothing, if it was a genuine one-off

Do not change anything. Present the list and let the user choose which to apply. Lead with the cross-cutting themes (patterns that recurred across 3+ sessions are the highest-value to act on).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: leek
• Source: leek/agent-skills
• License: MIT
• Homepage: https://skills.sh/leek/agent-skills

Install and usage instructions live in the source repository linked above.