Night Market Skill — ported from claude-night-market/abstract. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Friction-to-Learning Pipeline#

Detect friction signals during agent execution, track them across sessions, and graduate recurring patterns into permanent guidance. Bridges the gap between ephemeral session friction and durable CLAUDE.md rules.

Research backing: Claude Coach (hook-based friction detection with SQLite storage), alirezarezvani's self-improving-agent (three-tier MEMORY to CLAUDE.md graduation), and the ACE framework (arXiv: evolving playbooks from execution feedback, +10.6% on agent tasks).

Current gap: LEARNINGS.md exists but requires manual aggregation via /abstract:aggregate-logs. This skill adds automatic friction detection and a structured promotion path.

Friction Signal Types#

SignalDetection MethodWeightRepeated correctionsUser overrides same tool call 2+ times in sessionHighCommand failuresExit code != 0 patterns (same command type fails repeatedly)MediumPermission denialsUser denies tool call, indicating unexpected behaviorHighRe-readsSame file read 3+ times in session (lost context)LowRetry loopsSame action attempted 3+ times with variationsMediumUser frustrationExplicit negative feedback or correction languageHigh

Weight scoring: High = 3, Medium = 2, Low = 1 points per occurrence. Weighted score determines graduation velocity.

Three-Tier Storage Graduation#

text

Tier 1: Friction Log (ephemeral, per-session)
  Location: ~/.claude/friction/sessions/{date}-{id}.json
  Retention: 30 days, then pruned
  Threshold: 1 occurrence, logged, no action

Tier 2: Pattern Candidate (persistent, LEARNINGS.md)
  Location: ~/.claude/skills/LEARNINGS.md (friction section)
  Threshold: 3+ occurrences across 2+ sessions
  Action: flagged for review in next friction report

Tier 3: Graduated Rule (CLAUDE.md or skill update)
  Threshold: reviewed + user-approved
  Action: permanent guidance added to project/user config
  Constraint: NEVER auto-modify CLAUDE.md

Graduation Formula#

text

graduation_score = (weighted_count * recency_factor) / sessions_seen

recency_factor:
  last 7 days  = 1.0
  8-14 days    = 0.7
  15-30 days   = 0.4
  31+ days     = 0.1

Tier 2 threshold: graduation_score >= 6.0
Tier 3 proposal:  graduation_score >= 12.0

Detection Workflow#

Run at session end, at 80% context usage (via conserve:clear-context), or after failed improvement cycles (when metacognitive-self-mod detects regression).

Step 1: Scan Session for Signals#

For each friction indicator found, wrap it in the shared session-capture envelope (ADR-0011) so downstream readers can ingest friction signals and trace-capture entries through one parser:

json

{
  "schema_version": "session-capture/1",
  "session_id": "2026-04-14-abc12345",
  "timestamp": "2026-04-14T10:23:00Z",
  "source": "friction-detector",
  "payload": {
    "signal_type": "retry_loop",
    "description": "rg command failed 3x, fell back to grep",
    "context": "searching for pattern in node_modules",
    "weight": "medium"
  }
}

Legacy files written before envelope adoption are read as session-capture/0 (entire file treated as the payload). See docs/adr/0011-session-capture-envelope.md for the contract and migration path.

Step 2: Compare Against Existing Log#

bash

FRICTION_DIR=~/.claude/friction/sessions
mkdir -p "$FRICTION_DIR"

# Count prior occurrences of similar signals
if command -v rg &>/dev/null; then
  rg -c "$SIGNAL_TYPE" "$FRICTION_DIR"/*.json 2>/dev/null || echo "0"
else
  grep -rc "$SIGNAL_TYPE" "$FRICTION_DIR"/*.json 2>/dev/null || echo "0"
fi

Step 3: Calculate Graduation Score#

Aggregate across session logs: sum weighted occurrences, apply recency decay, divide by session count, compare against tier thresholds.

Step 4: Propose Graduations#

Tier 2 crossing: append to LEARNINGS.md friction section. Tier 3 crossing: present proposal with evidence to user, wait for explicit approval before any modification.

Step 5: Store Results#

Write session log to ~/.claude/friction/sessions/{date}-{session_id}.json and update ~/.claude/friction/index.json.

Anti-Noise Rules#

Ignore these signals:

1. One-off failures: transient network/CI errors (unless they recur 3+ times)
2. User-initiated exploration: deliberate experimentation is not agent error
3. Already-graduated patterns: covered by existing CLAUDE.md rules or skill instructions
4. External tool failures: MCP server crashes and similar tool bugs unrelated to agent behavior

Decay factor: signals older than 30 days contribute only 10% of their original weight (see graduation formula recency_factor).

Friction Report Format#

markdown

## Friction Report -- Session {date}

### New Signals (Tier 1)
- [RETRY] `rg` command failed 3x, fell back to `grep`
- [RE-READ] Read SKILL.md 4 times (lost file structure context)

### Recurring Patterns (Tier 2 candidates)
- [CORRECTION] User corrected file path format 4x across 3 sessions
  Score: 8.4 (threshold: 6.0)
  Candidate: Add path format guidance to CLAUDE.md

### Graduation Proposals (Tier 3)
- [RULE] "Always use absolute paths in Read tool"
  Evidence: 7 corrections across 5 sessions
  Score: 14.2 (threshold: 12.0)
  Action: Approve / Reject / Defer

### Noise Filtered
- 2 transient network timeouts (ignored)
- 1 user-initiated deep exploration (ignored)

Integration#

Feeds into: LEARNINGS.md (Tier 2 patterns, same format as /abstract:aggregate-logs), skill-improver (priority scoring), and metacognitive-self-mod (pipeline effectiveness).

Consumes from: session transcripts, aggregate_learnings_daily hook data, and the performance tracker for trend correlation.

When NOT to Use#

• Single isolated failures (wait for recurrence)
• Skill authoring (use abstract:skill-authoring)
• Routine log aggregation (use /abstract:aggregate-logs)

Related#

• abstract:metacognitive-self-mod: improvement analysis
• abstract:skills-eval: evaluation criteria
• /abstract:aggregate-logs: manual LEARNINGS.md generation
• conserve:clear-context: triggers friction scan at 80%