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 TalonT-Org/AutoSkillit --skill audit-frictiongit clone --depth 1 https://github.com/TalonT-Org/AutoSkillitWrote 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/talont-org/autoskillit/audit-friction)<a href="https://agentmods.dev/skills/talont-org/autoskillit/audit-friction"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/audit-friction/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/talont-org/autoskillit/audit-friction"><img src="https://agentmods.dev/badge/skills/talont-org/autoskillit/audit-friction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00067 | $0.02568 |
| Opus 5 | $0.00034 | $0.01284 |
| Sonnet 5 | $0.00013 | $0.00514 |
| Haiku 4.5 | $0.00007 | $0.00257 |
Grade A, and why
audit-friction 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 9d 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Friction Audit Skill
Mine Claude Code conversation logs to identify and categorize friction — repeated failures, stuck loops, tool errors, and misunderstanding cycles that cost the most effort. Surfaces patterns causing the most resistance and recommends concrete mitigations.
When to Use
- User says "audit friction", "find friction", "friction audit", or "what keeps going wrong"
- User wants to know where workflows repeatedly stall or fail
- User wants to identify systemic tooling or workflow issues across sessions
Arguments
The user may provide a "since" date (e.g., 2/7, 2026-02-07, last month). If not specified, use AskUserQuestion to ask what the earliest lookback date should be before proceeding. If the resulting window contains no logs, fall back to the last 30 days and note the adjustment.
Critical Constraints
NEVER:
- Modify any source code files
- Create files outside
{{AUTOSKILLIT_TEMP}}/audit-friction/directory - Have subagents write files — they return all findings in response text only
- Analyze subagent log subdirectories (
*/subagents/) — top-level session files only - Run subagents in the background (
run_in_background: trueis prohibited)
ALWAYS:
- Use subagents heavily for parallel log analysis
- All output goes under
{{AUTOSKILLIT_TEMP}}/audit-friction/(create if needed) - Final report:
{{AUTOSKILLIT_TEMP}}/audit-friction/friction_audit_{YYYY-MM-DD_HHMMSS}.md - Report the file and line counts to the terminal before choosing analysis mode
Friction Categories
Friction is any pattern where repeated effort yields no progress:
- Tool Failure Loops — Same tool called 2+ times with repeated errors (file not found, permission denied, command not found, network errors)
- Approach Reversals — Multiple strategy pivots for the same goal: tries approach A, fails, tries B, fails, tries C
- Test Fix Cycles — Repeated test-run → fail → fix → test-run → still fails sequences without resolution
- Misunderstanding Cycles — User corrections indicating the model misread the task; repeated wrong-direction attempts before course correction
- Search Failures — Repeated failed searches for relevant code (wrong file paths, grep returning nothing, symbol not found)
- Build/Compile Errors — Recurring build or compilation failures that block progress across multiple attempts
- Permission/Access Blockers — Tool calls denied, files inaccessible, repeated approval interruptions halting flow
- Context Re-exploration — Model re-investigates already-covered ground within the same session, forgetting earlier findings
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.
- 9d ago First seen · 240 lines · 67 tokens per session scan A a70d51be9cd9
audit-friction is a skill published in the GitHub repository TalonT-Org/AutoSkillit (5 stars, last pushed today), licensed MIT. It adds 67 tokens to every session and 2,568 once invoked, about $0.0003 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 2026-08-31.
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