Menu
Detect friction signals; graduate patterns into rules. Use for session retrospectives.
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
A direct command skips the review prompt. Inspect the source before running it.
npx skills add https://github.com/athola/claude-night-market --skill friction-detectorThe command stays on one line. Scroll horizontally to inspect it before copying.
Prefer a local copy? Download the files currently available to SkillsMP.
Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules. Use when vetting ideas. Not for QA; use night-market-validation-and-qa.
Generates and self-executes a diff-derived test plan for a PR. Use when validating PR changes before merge. Do not use for code review; use sanctum:pr-review.
Curate the web-capture index. Use when the capture backlog grows, captures sit unprocessed at seedling/pending, or to surface stored research during work.
Based on SOC occupation classification
Showing SKILL.md
| name | friction-detector |
| description | Detect friction signals; graduate patterns into rules. Use for session retrospectives. |
| category | workflow-optimization |
| alwaysApply | false |
| trigger | friction, friction detection, session retrospective, learning pipeline, recurring mistakes, pattern graduation, friction report |
| model_hint | standard |
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.
| Signal | Detection Method | Weight |
|---|---|---|
| Repeated corrections | User overrides same tool call 2+ times in session | High |
| Command failures | Exit code != 0 patterns (same command type fails repeatedly) | Medium |
| Permission denials | User denies tool call, indicating unexpected behavior | High |
| Re-reads | Same file read 3+ times in session (lost context) | Low |
| Retry loops | Same action attempted 3+ times with variations | Medium |
| User frustration | Explicit negative feedback or correction language | High |
Weight scoring: High = 3, Medium = 2, Low = 1 points per occurrence. Weighted score determines graduation velocity.
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_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
Run at session end, at 80% context usage (via
conserve:clear-context), or after failed improvement
cycles (when metacognitive-self-mod detects regression).
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:
{
"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.
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
Aggregate across session logs: sum weighted occurrences, apply recency decay, divide by session count, compare against tier thresholds.
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.
Write session log to
~/.claude/friction/sessions/{date}-{session_id}.json
and update ~/.claude/friction/index.json.
Ignore these signals:
Decay factor: signals older than 30 days contribute only 10% of their original weight (see graduation formula recency_factor).
## 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)
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.
abstract:skill-authoring)/abstract:aggregate-logs)abstract:metacognitive-self-mod: improvement analysisabstract:skills-eval: evaluation criteria/abstract:aggregate-logs: manual LEARNINGS.md generationconserve:clear-context: triggers friction scan at 80%~/.claude/friction/sessions/{date}-{session_id}.json
via the session-capture/1 schemagraduation_score >= 12.0 generate a Tier 3
proposal; skill does not auto-modify CLAUDE.md