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 YuiZhou/dayarc-agent --skill dayarc-learn-user-profilegit clone --depth 1 https://github.com/YuiZhou/dayarc-agentWrote 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/yuizhou/dayarc-agent/dayarc-learn-user-profile)<a href="https://agentmods.dev/skills/yuizhou/dayarc-agent/dayarc-learn-user-profile"><img src="https://agentmods.dev/badge/skills/yuizhou/dayarc-agent/dayarc-learn-user-profile/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/yuizhou/dayarc-agent/dayarc-learn-user-profile"><img src="https://agentmods.dev/badge/skills/yuizhou/dayarc-agent/dayarc-learn-user-profile.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 | $0.00015 | $0.00265 |
| Opus 5 | $0.00008 | $0.00133 |
| Sonnet 5 | $0.00003 | $0.00053 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
Learn User Profile 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.
What it actually says
Input
Outgoing signals (sent emails, Teams messages sent, commits, PRs authored), previous daily profile (or empty on bootstrap).
Output
Updated DailyProfile JSON — see memory-schemas.md for exact schema.
Instructions
- Read previous profile (if exists).
- For each outgoing signal, extract topics, contacts, threads.
- Update focus_areas: bump confidence + last_seen for matches, add new at 0.5, decay unseen by -0.1 (remove if <0.1).
- Update learning_interests: detect topics from content (blog links, docs, exploratory code), set trajectory.
- Update key_contacts: increment interaction_count for people in today's signals.
- Update active_threads: add new, update status, increment days_open.
- Set
impact_summariesfrom the dayarc-write-impact-summary output. Preserve its evidence breadcrumbs. - Set priorities_today from infer_priorities output.
- Set unfinished from items lacking completion signal. Each must have source_breadcrumb.
- Bootstrap: if no previous profile, build fresh from today's signals.
- VALIDATE output against memory-schemas.md before returning.
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 · 24 lines · 15 tokens per session scan A b2bacedafc57
Learn User Profile is a skill published in the GitHub repository YuiZhou/dayarc-agent (2 stars, last pushed 14d ago), licensed MIT. It adds 15 tokens to every session and 265 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 2026-08-31.
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