Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add athola/claude-night-market --skill friction-detectorgit clone --depth 1 https://github.com/athola/claude-night-marketWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/athola/claude-night-market/friction-detector)<a href="https://agentmods.dev/skills/athola/claude-night-market/friction-detector"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/friction-detector/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/athola/claude-night-market/friction-detector"><img src="https://agentmods.dev/badge/skills/athola/claude-night-market/friction-detector.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 138 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00020 | $0.01809 |
| Opus 5 | $0.00010 | $0.00905 |
| Sonnet 5 | $0.00004 | $0.00362 |
| Haiku 4.5 | $0.00002 | $0.00181 |
Grade A, and why
friction-detector scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Friction-to-Learning Pipeline
Overview
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
| 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.
Three-Tier Storage Graduation
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 221 lines · 20 tokens per session scan A ed663e25ff21
friction-detector is a skill published in the GitHub repository athola/claude-night-market (337 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 1,809 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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