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 aurorascharff/agent-friction-skill --skill passivegit clone --depth 1 https://github.com/aurorascharff/agent-friction-skillWrote 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/aurorascharff/agent-friction-skill/passive)<a href="https://agentmods.dev/skills/aurorascharff/agent-friction-skill/passive"><img src="https://agentmods.dev/badge/skills/aurorascharff/agent-friction-skill/passive/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/aurorascharff/agent-friction-skill/passive"><img src="https://agentmods.dev/badge/skills/aurorascharff/agent-friction-skill/passive.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00090 | $0.02643 |
| Opus 5.5 | $0.00036 | $0.01057 |
| Sonnet 5.5 | $0.00018 | $0.00529 |
| Haiku 4.5 | $0.00009 | $0.00264 |
Grade A, and why
friction-report 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 1mo 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
friction-report
Final-response friction reporter. Scans the completed task for actionable framework and tooling friction. If anything worth reporting was found, it offers to create a structured draft for human review. It sends the draft only after the user explicitly approves the external POST. If the task was clean, it exits silently.
No buffer, no per-turn tracking, no initialization step. Your conversation history is the source of truth.
When to run
- Immediately before sending a final response for a software-development task, after all implementation work and tool calls are complete
- When the user says "done", "thanks", "that's it", or otherwise signals that the development task is ending
- When the user explicitly asks: "report your friction", "what friction did you hit?", "give me the friction report"
- When the harness invokes this skill by name
Do not run during an intermediate update or while more task work remains. Run once for the current user request. If the user resumes the task after a final response, review only the new work at the next final response.
If the user explicitly invoked the friction-log skill during this session, do not run — that skill already produced a detailed log.
What qualifies
Report observed friction that caused a meaningful detour or is likely to recur for another developer or agent, including:
- A build, development server, runtime, or tool crash, hang, out-of-memory failure, or unexpected slowdown
- Incorrect or inconsistent framework, SDK, CLI, codemod, or skill behavior
- Missing, unclear, or conflicting documentation
- A misleading error, missing diagnostic, or failure that points at the wrong source
- Repeated failed approaches or a non-obvious workaround required to complete an ordinary task
- A user correction that reveals one of the product, documentation, or tooling problems above
Do not report routine first-pass debugging, mistakes caused only by the agent, hypothetical concerns, or failures caused by unverified local changes. A task does not need to be blocked for its friction to qualify.
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.
- 1mo ago Changed · +5 lines · +15 tokens per session cb7eb94725e2
- 1mo ago Changed · +20 lines · +28 tokens per session e71f42023d4f
- 1mo ago First seen · 160 lines · 47 tokens per session scan A 94c6a6c861b2
friction-report is a skill published in the GitHub repository aurorascharff/agent-friction-skill (24 stars, last pushed 1mo ago), licensed MIT. It adds 90 tokens to every session and 2,643 once invoked, about $0.0004 per session on Opus 5.5. 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.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
deck-retro
Run a fully local agent-deck retrospective over the user's own transcripts, Recall index and logs. Use when a user wants to find recurring failures or take their own finding from investigation through synthetic reproduction, a user-filed issue, a test-first fix and contributor PR handoff. Also use for weekly usage…
cwv-optimizer
Diagnose and fix Core Web Vitals issues on AEM Edge Delivery Services pages. Goes deeper than generic CWV advice by understanding EDS-specific performance patterns including the 100KB LCP budget, E-L-D loading phases, block rendering behavior, and third-party script impact. Produces specific fixes for LCP, CLS, and…
incident-response-incident-response
Coordinate production incident triage, mitigation, investigation, recovery, and follow-up. Use when responding to outages or other incidents that require structured operational coordination.