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 agent-friction-skillgit 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/agent-friction-skill)<a href="https://agentmods.dev/skills/aurorascharff/agent-friction-skill/agent-friction-skill"><img src="https://agentmods.dev/badge/skills/aurorascharff/agent-friction-skill/agent-friction-skill/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/agent-friction-skill"><img src="https://agentmods.dev/badge/skills/aurorascharff/agent-friction-skill/agent-friction-skill.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 | $0.00076 | $0.02193 |
| Opus 5 | $0.00038 | $0.01097 |
| Sonnet 5 | $0.00015 | $0.00439 |
| Haiku 4.5 | $0.00008 | $0.00219 |
Grade A, and why
friction-log 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 11d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Friction Log
Document agentic developer experience friction as it's encountered during a development task — friction that blocks agents also surfaces DX issues for developers. Treat friction logging as equally important as task completion.
Instructions
When invoked:
- Identify the output file — use the argument if provided. Otherwise, name the log after the task with the
fl-prefix:fl-<feature>.md. If this run scaffolds a newfl-<feature>/folder (see #7), the log lives inside that folder asfl-<feature>/fl-<feature>.md. If no scaffold was created, writefl-<feature>.mdin the current working directory. Keeping the log next to the code it describes makes the artifact self-contained: zipping or sharing thefl-folder ships the log with it. Thefl-prefix makes the file recognisable as a friction log even when it has been moved out of its folder. - Identify the task from the user's first message. The user's first message IS the task. Do not ask clarifying questions before starting unless you are genuinely blocked.
- Identify the input — if the task involves an existing codebase, include it as Input in the log header. Use the URL the user provided; otherwise use the local path.
- Record the prompt — write the user's initial request verbatim into the log before doing any work.
- Record the model and harness — at the top of the log, record the model name (e.g. Claude Sonnet 4.6) and the harness you are running under (e.g. Claude Code, Cursor, DX Agent). Only ask the user if both are genuinely unknown to you.
- Read any URLs in the prompt first — if the user's prompt contains URLs (GitHub repos, documentation links, reference files), read their content BEFORE starting the task. Do not skip URLs or silently proceed without reading them.
- Name any scaffolded repo after the task — if the task involves creating a new app or repo, use a
fl-prefix followed by the feature or API being tested (e.g.fl-catcherror,fl-instant-nav). The prefix makes clear the repo was created as part of a friction log run. Do not use generic names likemy-apportest-app. Write the friction log file inside this folder using the same prefix (fl-<feature>/fl-<feature>.md), not in the parent directory. - Log friction as you encounter it — write to the file in real time, not at the end. Each friction entry must say what you expected, what actually happened, what you tried, and how it resolved (or that it didn't). The resolution is as valuable as the friction itself.
- Do not initiate questions to the user mid-run. Push uncertainty into the log as friction with the right source tag (
[web search],[docs],[training data],[sandbox]) showing what you fell back on, and keep working. Only stop and ask if you are truly blocked AND your harness supports interactive prompts — most agentic harnesses do not. When in doubt, write the friction down and keep going. - Watch for out-of-band context the harness may inject mid-run. Some harnesses surface user messages typed into the running session (chat-thread replies, queued Slack messages, etc.) as system messages at the start of a turn. Treat each item as either:
- An instruction ("also check headers", "use Turbopack instead") — incorporate it into the current run and adjust any in-progress log entries to reflect it.
- A direct question ("did you check the headers?", "what version are you on?") — answer it concisely via whatever reply mechanism your harness provides (e.g. a
slack_reply-style tool), BEFORE continuing other work. Log the exchange verbatim at the point in time it arrived, using> **User:** …and> **Agent:** …blockquotes.
- Cite your sources — every log entry must end with a source tag indicating how you obtained the information. See references/agent-behavior.md for the full list.
- Action Items are the most important part of the log. Every friction point (🟡 or 🔴) MUST produce at least one action item. Do NOT leave placeholders like
[To be filled]— write concrete, specific action items. Each item MUST have an indentedContext:line below it with the specific friction encountered. Split them into three subsections:- Docs — fixable with better documentation, clearer callouts, or updated examples.
🔧prefix. - Framework — requires a code change: error messages, warnings, scaffold defaults, tooling, agent-layer infrastructure.
🔧prefix. - DX / Research — open questions or investigations worth pursuing.
🔍prefix.
- Docs — fixable with better documentation, clearer callouts, or updated examples.
- Build failures are friction, not stopping points. When a build or test fails, log it as a 🔴, read the error output carefully, try to fix it, and rebuild. A failed build is often the most valuable part of the log. Cap retries — three attempts is usually enough; if it still fails, write what you learned and move on. Do NOT brute-force the same failing command.
- Track cumulative build time. If the harness exposes per-build duration (or you can time it cleanly), keep a running total across all build attempts and write it in the header as
**Build time:** Xs. This captures total time lost to builds — more builds from bad DX means higher build time. If you cannot time builds, omit the field rather than guess. - Tool timeouts are friction signal. If a tool returns
timedOut: true(or its equivalent), log it as 🔴, do not retry the same command, and switch tools or approach. Repeated timeouts on the same command are pure budget waste. - When done, write the log, then
catit to stdout, then stop. After the file is finalized, print its full contents (e.g.cat fl-<feature>.mdorcat fl-<feature>/fl-<feature>.md) so the user sees the log inline in the terminal — do not just point at the file path. You may also mention that the log can be pasted into https://agent-friction-skill.vercel.app/ for a collapsible, severity-coded view. Do not ask the user to review it, do not offer to deploy, do not ask follow-ups. Do NOT add an empty## Skill Feedbacksection — an empty placeholder is uglier than no section. Only append a## Skill Feedbacksection later, if the user reviews the log and explicitly points out a place where the skill itself caused you to behave incorrectly (use🔁prefix). This is not DX feedback; it is feedback for improving the skill. If the failure was caused by the task environment, it belongs in Action Items instead. - The final log MUST always include every required section from the template — Header, Prompt, Tool Timeline, Summary, Action Items (Docs + Framework + DX / Research), and Log. No exceptions. Even if the build never succeeded, write a Summary of what happened and Action Items based on the friction you observed. An incomplete log with a missing Summary or missing Action Items is a failure.
Skill Feedbackis optional and added later (see #16) — never include an empty version. - Write the log progressively — NEVER use placeholders. Do NOT write the full template skeleton with
[To be filled],[pending],[TBD], or empty sections. Instead:- At the START: write only the header (Date, Model, Harness, Task) and the Prompt section.
- DURING the task: append Log entries as you encounter them. Rewrite the build-time field after each build.
- At the END: write the Summary and Action Items based on everything you observed, then rewrite the full file with all sections filled in.
- If you are running low on context or steps, STOP working on the task and immediately write the complete final log with Summary and Action Items based on what you have so far. A complete log with fewer log entries is far better than an incomplete log. Never end the log mid-sentence or without Summary + Action Items — these are mandatory regardless of how the run went.
- Always write a
## Tool Timelinesection. Place it directly after## Prompt, before## Summary. List your tool calls chronologically as- HH:MM:SS — <tool>: <short description>. Write it yourself even if your harness also appends one — the log is read after the run, often pasted into a viewer that doesn't have access to harness-side appendices, so the timeline must be inside the file itself. Keep entries terse; this section is reference material, not narrative.
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.
- 11d ago First seen · 57 lines · 76 tokens per session scan A 9267236f93ec
friction-log is a skill published in the GitHub repository aurorascharff/agent-friction-skill (23 stars, last pushed 11d ago), licensed MIT. It adds 76 tokens to every session and 2,193 once invoked, about $0.0004 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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