name	Running Log
description	Persistent schema-driven running log with three-component architecture - quick-capture ideas, AI auto-detection, and backlog review librarian
version	2.0.0

Name: running-log Version: 2.0 Domain: Process Memory, Decision Tracking, Cross-Session Learning Status: Redesigned based on Phase 2 validation findings

Purpose

Maintain a persistent, schema-driven running log that captures:

Ideas (human quick-capture backlog)
Consultations (AI-detected external sources)
Process Memory (AI-detected reasoning patterns)

Creates searchable, auto-organized entry backlog across sessions through three distinct workflows:

Human quick-capture (/idea)
AI auto-detection (Consultation, Process Memory)
Post-processing librarian (/review-backlog)

Critical Design Insight: Human entry workflows differ fundamentally from AI auto-detection workflows. v2.0 separates these cleanly.

Architecture: Three-Component System

Component 1: `/idea` Command (Human Territory)

Purpose: Ultra-minimal quick capture while working

Workflow:

User: /idea Local copies of Anthropic docs in AI-optimized format
→ Entry created immediately with defaults
→ User continues work

What AI fills automatically:

Entry ID: #ID-YYYYMMDD-NNN (auto-incremented)
Timestamp: ISO 8601
Confidence/Priority: TBD (To Be Determined - evaluated during backlog review)
Status: Backlog (default for all ideas)
Tags: AI-generated from description + existing tag taxonomy
Type: Idea/Note
Profile: Active profile (e.g., DEVELOPER)

Entry Schema (Ideas):

## Idea/Note | #ID-YYYYMMDD-NNN | [ISO 8601 Timestamp]

**Description**: [User-provided 1-line description]
**Confidence/Priority**: TBD
**Status**: Backlog
**Type**: Idea/Note
**Profile**: [Active Profile]
**Tags**: [AI-generated tags]

---

Why this works:

Zero friction: User types one line, gets back to work
No nonsensical prompts for confidence (ideas are captured, not evaluated)
No status guessing (all ideas start as backlog)
Consistent tags (AI prevents million inconsistent human tags)
Evaluation happens later during /review-backlog

Component 2: AI Auto-Detection (AI Territory)

Purpose: Monitor Claude's responses for reasoning patterns worth capturing

Entry Types:

Consultation (External Sources)

AI detects when referencing external knowledge:

Documentation lookups
Perplexity/research queries
User-provided references
Framework/library citations

Auto-generates:

## Consultation | #ID-YYYYMMDD-NNN | [Timestamp]

**Description**: [What was consulted]
**Source**: [Citation/URL]
**Confidence**: [AI's confidence in source quality: High/Med/Low]
**Status**: Reviewed
**Type**: Consultation
**Profile**: [Active Profile]
**Tags**: [domain, source-type, framework]

---

Process Memory (AI Reasoning Patterns)

AI detects loggable reasoning patterns in its own responses:

Pattern 1: Uncertainty

/uncertainty\s+(on|about|regarding|around)\s+([^.!?]+)/i

→ Logs: What's uncertain, confidence level

Pattern 2: Assumption

/assum(e|ing|ption)\s+(that|about|the)\s+([^.!?]+)/i

→ Logs: Assumption made, validation status

Pattern 3: Confidence Threshold

/confidence\s+(less\s+than|below|<)\s*(\d+)%?/i

→ Logs: Low-confidence item needing validation

Pattern 4: Decision/Fork

/(fork|branch|decision\s+point|chose|decided|rejected)\s+(in|on)?\s*([^.!?]+)/i

→ Logs: Decision made, alternatives considered, rationale

Pattern 5: Critical Signal

/critical|blocker|blocking|must\s+(clarify|understand|verify)/i

→ Logs: Critical issue flagged, requires attention

Auto-generates:

## Process Memory | #ID-YYYYMMDD-NNN | [Timestamp]

**Description**: [Reasoning pattern detected]
**Confidence**: [AI's certainty about this pattern: 0-100%]
**Status**: [Assumed/Validated/Rejected]
**Type**: Process Memory
**Profile**: [Active Profile]
**Tags**: [pattern-type, domain, criticality]
**Pattern Detected**: [Which regex matched]
**Raw Output**: [Exact phrase from Claude's response]

**Extended Context**:
[Why this pattern matters, implications, next steps]

---

Cadence: 3 automatic checks per session

Session Start: Continuity from previous session
Mid-Toolchain: After floor(tool_count / 3) tools executed
Session End: Archive session learnings

Confidence Thresholds (Auto-log only if >= threshold):

DEVELOPER: 75%
RESEARCHER: 60%
ENGINEER: 70%
DEFAULT: 70%

Noise Filtering:

Confidence threshold (above)
Entry cap per session (DEVELOPER: 8, RESEARCHER: 12, ENGINEER: 10)
Deduplication (Levenshtein 85% similarity suppresses duplicates)

Component 3: `/review-backlog` Command (Librarian Function)

Purpose: Post-process entries to organize, prioritize, and link

What it does:

Relationship Identification: AI analyzes all entries and identifies connections
Tag Refinement: Harmonizes tags across entries, suggests taxonomy improvements
Prioritization: Reviews TBD priorities, suggests High/Med/Low based on context
Linking: Populates Linked To field by finding related entries
Auto-Section Generation: Regenerates High-Priority Ideas, Open Risks, Linked Insights

Usage:

/review-backlog                 # Review all entries, suggest actions
/review-backlog --ideas         # Review only ideas (prioritize, link)
/review-backlog --risks         # Review low-confidence items
/review-backlog --link #ID-001  # Find and link entries related to #ID-001

Example Output:

🔍 Backlog Review Results

Ideas Requiring Prioritization (5):
- #ID-20251222-001: Local AI-optimized docs → Suggested: High (aligns with knowledge-base work)
- #ID-20251221-003: Plugin permission system → Suggested: Med (dependent on architecture)

Suggested Links (3):
- #ID-20251222-001 ← #ID-20251221-008 (both reference documentation workflows)
- #ID-20251221-005 → #ID-20251221-003 (decision impacts idea)

Tag Harmonization:
- Rename "docs" → "documentation" (4 entries)
- Merge "anthropic-api" + "anthropic" (2 entries)

Apply changes? [Y/n]

Why separate from capture:

Humans can't know relationships while capturing ideas mid-work
Requires full-backlog context to identify patterns
Deliberate activity, not real-time capture
AI analyzes relationships humans can't see

File Structure

project/
├── .claude/
│   ├── RUNNING_LOG.md              # Main log (auto-sections + chronological)
│   ├── LAST_ENTRIES.md             # Dedup tracking (20 most recent)
│   └── skills/
│       └── running-log/
│           └── SKILL.md            # This specification
└── [project files]

RUNNING_LOG.md Structure

# Running Log - DEVELOPER Profile

**Created**: [ISO 8601]
**Last Updated**: [ISO 8601]

---

## Auto-Generated Sections

### 🔥 High-Priority Ideas
[Auto-populated from ideas tagged High, status ≠ Done]

### ⚠️ Open Risks / Low-Confidence Items
[Auto-populated from Process Memory with confidence < 60%]

### 🔗 Linked Process Insights
[Auto-populated from entries with Linked To populated]

---

## Entry Backlog

[Entries in reverse chronological order]

---

Commands Summary

`/idea [DESCRIPTION]`

Quick-capture idea while working. AI fills all other fields with defaults.

/idea Local copies of Anthropic docs in AI-optimized format

`/review-backlog [OPTIONS]`

Post-process entries: prioritize, link, harmonize tags.

/review-backlog                 # Full review
/review-backlog --ideas         # Ideas only
/review-backlog --risks         # Low-confidence items
/review-backlog --link #ID-001  # Link related entries

`/running-log --show [N]`

Display last N entries (default: 10).

/running-log --show 5

`/running-log --debug`

Show last 5 entries with full details including regex detection.

/running-log --debug

Configuration

running_log:
  enabled: true
  file_path: ".claude/RUNNING_LOG.md"
  state_file: ".claude/LAST_ENTRIES.md"

  profiles:
    DEVELOPER:
      threshold: 75
      entry_cap: 8
    RESEARCHER:
      threshold: 60
      entry_cap: 12
    ENGINEER:
      threshold: 70
      entry_cap: 10
    DEFAULT:
      threshold: 70
      entry_cap: 8

  deduplication:
    enabled: true
    levenshtein_threshold: 0.85
    cross_session: true

  idea_defaults:
    confidence: "TBD"
    status: "Backlog"
    auto_tag: true  # AI generates tags from description

Migration from v1.0

Changes:

/log command removed → Use /idea [description] instead
Interactive prompting removed → /idea is one-line only
Confidence/Status for ideas → Now defaults (TBD/Backlog)
Tags → AI-generated, not human-entered
Linked To → Post-processing via /review-backlog, not capture-time
/review command → Renamed to /review-backlog with expanded functions

Existing logs compatible: v1.0 entries remain valid, new entries use v2.0 schema

Design Rationale (Phase 2 Learnings)

Problem 1: Nonsensical Fields for Ideas

v1.0: Asked humans for confidence/priority when capturing ideas Issue: Ideas are captured for later evaluation, not evaluated at capture time v2.0 Fix: Defaults to TBD/Backlog, evaluation happens during /review-backlog

Problem 2: Inconsistent Human Tags

v1.0: Asked humans to enter free-form tags Issue: Million inconsistent tags, none relevant v2.0 Fix: AI auto-generates tags from description + existing taxonomy

Problem 3: Impossible "Linked To" Field

v1.0: Asked humans to provide entry IDs while capturing Issue: Humans don't memorize IDs mid-work v2.0 Fix: AI identifies relationships during /review-backlog post-processing

Problem 4: Monolithic Command

v1.0: Single /log command tried to handle all entry types Issue: Human quick-capture ≠ AI auto-detection workflows v2.0 Fix: Split into /idea (human), auto-detection (AI), /review-backlog (librarian)

Version & Maintenance

Current: v2.0 (Redesigned based on Phase 2 validation) Previous: v1.0 (Phase 1 spec-only)

Expected Updates:

v2.1: Post-deployment tuning based on real usage
v3.0: Multi-repository support, cross-project insights

Schema Stability: Core schema stable. Thresholds may adjust based on empirical data.

Next Steps

Implement /idea command (minimal quick-capture)
Implement /review-backlog command (librarian functions)
Update existing /running-log command for display-only modes
Test with real workflows across 5+ sessions
Collect usage data, tune thresholds

End of SKILL.md Specification v2.0

This specification reflects critical design learnings from Phase 2 validation. The three-component architecture (quick-capture, auto-detection, post-processing) separates human and AI workflows appropriately.

Install Skill

SKILL.md