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Dig

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Soul-Brews-Studio
dig

Mine Claude Code sessions — timeline, gaps, repo attribution, session history. Use when user says "dig", "sessions", "past sessions", "timeline", "what did I work on", or wants to see session history. Do NOT trigger for finding code/projects (use /trace), exploring repos (use /learn), or current session status (use /recap).

Overview

PublisherSoul-Brews-Studio
Repositoryarra-oracle-skills-cli
Skill namedig
Stars
125
Forks
59
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Soul-Brews-Studio on GitHub. Read the source before you install it.

Installation

Install the Dig AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Soul-Brews-Studio/arra-oracle-skills-cli/tree/alpha/skills/dig
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Soul-Brews-Studio/arra-oracle-skills-cli.git /tmp/arra-oracle-skills-cli
mkdir -p .claude/skills
cp -r /tmp/arra-oracle-skills-cli/skills/dig .claude/skills/dig
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dig in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dig on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dig is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/dig - Session Goldminer v2

Mine Claude Code session data for timelines, gaps, and repo attribution. No query needed.

Usage

/dig                    # Current repo, 10 most recent
/dig [N]                # Current repo, N most recent
/dig --all              # All repos, ALL sessions (auto-detect count)
/dig --all [N]          # All repos, N most recent
/dig --deep             # Deep scan — ALL .jsonl files (not just most-recent per project)
/dig --deep [N]         # Deep scan, N most recent
/dig --all --deep       # All repos, deep scan — full coverage
/dig --timeline         # Day-by-day grouped (current repo)
/dig --all --timeline   # Day-by-day grouped (all repos, ALL sessions)

Deep Mode (#216)

Standard /dig deduplicates by session basename — only showing the most recent file per session. This misses historical sessions, subagent sessions, and worktree sessions.

--deep scans ALL .jsonl files across all project directories. Output includes extra fields:

  • toolCalls — number of tool invocations in the session
  • fileSizeKB — session file size in KB
  • Coverage metadata: "X of Y sessions" showing how many were returned vs found

Timezone (#214)

dig.py v2 auto-detects timezone instead of hardcoding GMT+7:

  1. MAW_DISPLAY_TZ env var (e.g., Asia/Bangkok, 7, America/New_York)
  2. TZ env var
  3. System timezone
  4. Fallback: UTC

Output metadata includes detected timezone name and offset.

Step 0: Timestamp

bash
date "+🕐 %H:%M %Z (%A %d %B %Y)"
ENCODED_PWD=$(pwd | sed 's|^/|-|; s|[/.]|-|g')
PROJECT_BASE=$(ls -d "$HOME/.claude/projects/${ENCODED_PWD}" 2>/dev/null | head -1)
export PROJECT_DIRS="$PROJECT_BASE"

# Strip -wt* suffix to find parent project dir
PARENT_ENCODED=$(echo "$ENCODED_PWD" | sed 's/-wt-[^/]*$//')
if [ "$PARENT_ENCODED" != "$ENCODED_PWD" ]; then
  PARENT_BASE=$(ls -d "$HOME/.claude/projects/${PARENT_ENCODED}" 2>/dev/null | head -1)
  [ -n "$PARENT_BASE" ] && export PROJECT_DIRS="$PROJECT_DIRS:$PARENT_BASE"
fi

# nullglob-safe worktree scan (both parent and self)
for base in "$PROJECT_BASE" "$PARENT_BASE"; do
  [ -z "$base" ] && continue
  for wt in "$base"-wt-*(N); do  # (N) = zsh nullglob qualifier
    [ -d "$wt" ] && export PROJECT_DIRS="$PROJECT_DIRS:$wt"
  done
done

Encodes pwd the same way Claude does (replace / and . with -, prepend -) to match the .claude/projects/ directory naming (e.g. github.com → github-com). Detects parent project from worktree dirs (strips -wt-* suffix) and includes both parent and self worktrees. The (N) qualifier prevents zsh crashes when no worktrees exist.

With --all (all repos):

bash
export PROJECT_DIRS=$(ls -d "$HOME/.claude/projects/"*/ | tr '\n' ':')

Step 2: Extract Session Data

Run the dig script. Pass 0 for --all (no limit), or N if user specified a count, default 10:

bash
python3 ~/.claude/skills/dig/scripts/dig.py [N] [--deep]
# N=10 (default), N=0 (scan all sessions), N=50 (50 most recent)
# --deep: scan ALL .jsonl files (not just most-recent per basename)

Deep Mode Display

When --deep is used, add extra columns to the table:

markdown
| # | Date | Session | Min | Repo | Msgs | Tools | Size | Focus |

And show coverage in the footer:

markdown
**Coverage**: [returned]/[found] sessions | **Timezone**: [tz_name] (GMT+[offset])

Step 3: Display Timeline

Read the JSON output and display as a table. Sessions are chronological (oldest first). Gap rows (type: "gap") span the session column with · · · prefix:

markdown
## Session Timeline

| # | Date | Session | Min | Repo | Msgs | Focus |
|---|------|---------|-----|------|------|-------|
|   |      | · · · sleeping / offline | | | | |
| 1 | 02-21 | 08:40–09:08 | 28m | oracle-skills-cli | 5 | Wire /rrr to read pulse data |
|   |      | · · · 45m gap | | | | |
| 2 | 02-21 | 09:55–10:23 | 28m | homelab | 3 | oracle-pulse birth + CLI flag |
|   |      | · · · no session yet | | | | |

**Dirs scanned**: [list PROJECT_DIRS]
**Total sessions found**: [count]

Column rendering rules:

  • Gap rows: | | | · · · [label] | | | | | — number + date empty, label in Session col
  • Date: MM-DD short format (strip year)
  • Session: HH:MM–HH:MM using startGMT7 and endGMT7 (strip date, keep time only)
  • Min: [durationMin]m
  • Repo: use repoName field from dig.py output (resolved via ghq)
  • Msgs: realHumanMessages count

"Msgs" = real typed human messages (not tool approvals).


With --timeline: Group by Date

When --timeline flag is present, group sessions by date instead of a flat table. Use --all to see all repos (recommended for timeline).

Step 1: Run dig.py with 0 for --all (scans all sessions), or user-specified count

Step 2: Group sessions by date from startGMT7. Render each day as:

markdown
## Feb 22 (Sun) — [vibe label]

                  · · ·   sleeping / offline
08:48–09:11    23m   homelab        Update Fleet Runbook + Explore black.local
09:11–11:30   139m   homelab        Set Up KVM OpenClaw Node on black.local
09:37–12:51   194m   Nat-s-Agents   /recap → supergateway → CF ZT → arra-oracle-v3 dig
                  · · ·   45m gap
12:51–13:03    12m   Nat-s-Agents   Dig All + Design arra-oracle-v3 ← current
                  · · ·   no session yet

## Feb 21 (Sat) — Long day: Fleet + Brewing + Skills

06:19–08:38   139m   homelab        Moltworker Gateway + MBP Node
08:40           (bg)  openclaw       ClawHub Build Script (idle long)
09:23–16:08   405m   homelab        Debug MBP Node 401 — Gateway Token Auth

Rendering rules:

  • Day header: ## MMM DD (Day) — [vibe label] — infer vibe from session summaries (e.g. "Infrastructure Day", "Brewing + Skills")
  • Session rows: HH:MM–HH:MM [N]m REPO Summary — use repoName for repo, summary for focus
  • Gap rows: · · · [label] between sessions when gap > 30 min
  • Sidechain: prefix (bg) for sessions with isSidechain: true
  • Current: append ← current marker on the last session of the current day (today only)
  • Sort: days newest-first, sessions within each day oldest-first (chronological)
  • Date format: startGMT7 time portion only (HH:MM), endGMT7 time portion (HH:MM)
  • Repo width: pad repo names to align columns

Step 3: Show summary footer:

markdown
**Days**: [count] | **Sessions**: [count] | **Total time**: [sum of durationMin]m

Trace Connection

After displaying the timeline, log the discovery to Oracle so it's searchable later:

arra_trace({
  query: "dig session [N]",
  project: "[repo-name]",
  foundFiles: [],
  foundCommits: [list of commit hashes from timeline],
  foundIssues: []
})

This connects /dig discoveries to /trace — "When did we first work on X?" becomes answerable.


ARGUMENTS: $ARGUMENTS

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dig AI skill do?

Mine Claude Code sessions — timeline, gaps, repo attribution, session history. Use when user says "dig", "sessions", "past sessions", "timeline", "what did I work on", or wants to see session history. Do NOT trigger for finding code/projects (use /trace), exploring repos (use /learn), or current session status (use /recap).

Why use Dig on TypingMind?

Because you install it once and use it with any model. Dig is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dig in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Soul-Brews-Studio/arra-oracle-skills-cli/tree/alpha/skills/dig. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dig?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dig?

As many as you like. As long as a model supports skills, you can use Dig with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dig AI skill free?

Yes. It is published on GitHub by Soul-Brews-Studio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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