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Session Scan

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oliver-kriska
session-scan

Compute metrics for Claude Code sessions. Discovers via ccrider, filters trivial, computes friction/opportunity/fingerprint scores. Use for broad session triage.

Overview

Publisheroliver-kriska
Repositoryclaude-elixir-phoenix
Skill namesession-scan
Stars
565
Forks
45
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 oliver-kriska on GitHub. Read the source before you install it.

Installation

Install the Session Scan 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/oliver-kriska/claude-elixir-phoenix/tree/main/.claude/skills/session-scan
  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/oliver-kriska/claude-elixir-phoenix.git /tmp/claude-elixir-phoenix
mkdir -p .claude/skills
cp -r /tmp/claude-elixir-phoenix/.claude/skills/session-scan .claude/skills/session-scan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Session Scan 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 Session Scan 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 Session Scan 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.

Session Scan (Tier 1)

Compute deterministic metrics for sessions discovered via ccrider MCP.

STOP — Read Before Executing

NEVER call mcp__ccrider__get_session_messages in main context. Each response is 5-50KB of JSON. Fetching N sessions directly = context crash.

You MUST delegate all scoring to subagents. The main context only does: discovery, deduplication, subagent spawning, and result collection.

Requirements

Requires ccrider MCP. If not available:

ccrider MCP is required. See: https://github.com/neilberkman/ccrider

Usage

/session-scan                         # Last 7 days, up to 50 sessions
/session-scan --since 2026-02-01      # Since specific date
/session-scan --project enaia         # Filter by project name
/session-scan --limit 20              # Cap session count
/session-scan --list                  # Discovery only, no scoring
/session-scan --rescan                # Recompute already-scanned sessions

What Main Context Does

1. Parse Arguments

Extract from $ARGUMENTS:

  • --since DATE: ISO date filter (default: 7 days ago)
  • --project NAME: Filter by project path substring
  • --limit N: Max sessions to process (default: 50)
  • --list: Discovery only — show table, skip scoring
  • --rescan: Recompute metrics for already-scanned sessions

2. Discover Sessions (safe — response is ~1KB)

mcp__ccrider__list_recent_sessions(limit: N, project: PROJECT, after_date: SINCE)

Filter out sessions with < 10 messages. If --list: show table and stop.

3. Deduplicate

Read .claude/session-metrics/metrics.jsonl (if exists). Skip sessions already scanned unless --rescan flag is set. Report: "N new sessions to scan (M already in ledger)"

4. Resolve Scorer Path

Glob: **/session-scan/references/compute-metrics.py

Store as SCORER_PATH (absolute). PROJECT_ROOT = current working directory.

5. Spawn ONE Subagent Per Session

DO NOT fetch messages yourself. DO NOT run Python yourself. You MUST use the Task tool to spawn subagents.

For EACH unscanned session, spawn a haiku subagent using this template:

Task(subagent_type="general-purpose", model="haiku", mode="bypassPermissions", prompt="""
Score one Claude Code session. Steps:

1. Fetch messages:
   mcp__ccrider__get_session_messages(session_id: "{SESSION_ID}", last_n: 200)

2. Write the full tool result (messages JSON) to:
   {PROJECT_ROOT}/.claude/session-metrics/_tmp_{SHORT_ID}.json

3. Run scorer and capture result in ONE Bash command:
   python3 {SCORER_PATH} {PROJECT_ROOT}/.claude/session-metrics/_tmp_{SHORT_ID}.json --session-id {SESSION_ID} --project {PROJECT_NAME} > {PROJECT_ROOT}/.claude/session-metrics/_result_{SHORT_ID}.json && rm {PROJECT_ROOT}/.claude/session-metrics/_tmp_{SHORT_ID}.json

4. Report back: "Scored {SHORT_ID}: friction=X.XX fingerprint=Y"

If ccrider fails, report the error and exit.
""")

Spawn ALL subagents in parallel. Wait for all to complete.

6. Collect Results

bash
for f in .claude/session-metrics/_result_*.json; do
  cat "$f" >> .claude/session-metrics/metrics.jsonl
  rm "$f"
done

7. Display Results

Show summary table sorted by friction (descending):

| ID       | Project | Date       | Type        | Friction | Opportunity | Tier2? |
|----------|---------|------------|-------------|----------|-------------|--------|
| ffa155ee | enaia   | 2026-02-18 | bug-fix     | 0.42     | 0.65        | Yes    |
| 90a74843 | wedding | 2026-02-17 | feature     | 0.15     | 0.20        | No     |

If Tier 2 eligible: suggest /session-deep-dive --from-scan.

8. Update Scan Metadata

Write .claude/session-metrics/latest-scan.json:

json
{
  "scanned_at": "2026-02-20T14:30:00Z",
  "sessions_discovered": 25,
  "sessions_scanned": 18,
  "sessions_skipped": 7,
  "tier2_eligible": 5
}

Scoring Reference

See references/scoring-guide.md for full algorithm documentation.

  • Friction Score (0-1): Weighted signals, sigmoid-normalized
  • Fingerprint: Rule-based classifier (6 types)
  • Plugin Opportunity (0-1): Missed /phx: commands
  • Tier 2 Eligible: friction > 0.35 OR opportunity > 0.5 OR plugin used OR msgs > 50

Iron Laws

  1. NEVER call get_session_messages in main context — WILL crash context
  2. ONE subagent per session — each fetches exactly ONE session
  3. mode="bypassPermissions" on every Task — subagents need Write+Bash
  4. Absolute paths in subagent prompts — subagents don't inherit context
  5. Result files, not direct append — avoid jsonl race conditions
  6. NEVER modify existing metrics.jsonl entries — append-only ledger
  7. ALWAYS delete temp and result files after processing

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 Session Scan AI skill do?

Compute metrics for Claude Code sessions. Discovers via ccrider, filters trivial, computes friction/opportunity/fingerprint scores. Use for broad session triage.

Why use Session Scan on TypingMind?

Because you install it once and use it with any model. Session Scan 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 Session Scan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oliver-kriska/claude-elixir-phoenix/tree/main/.claude/skills/session-scan. 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 Session Scan?

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 Session Scan?

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

Is the Session Scan AI skill free?

Yes. It is published on GitHub by oliver-kriska 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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