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 aleksander-dytko/ai-pm-workspace --skill todaygit clone --depth 1 https://github.com/aleksander-dytko/ai-pm-workspaceWrote 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/aleksander-dytko/ai-pm-workspace/today)<a href="https://agentmods.dev/skills/aleksander-dytko/ai-pm-workspace/today"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/today/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/aleksander-dytko/ai-pm-workspace/today"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/today.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 | $0.00022 | $0.01727 |
| Opus 5.5 | $0.00009 | $0.00691 |
| Sonnet 5.5 | $0.00004 | $0.00345 |
| Haiku 4.5 | $0.00002 | $0.00173 |
Grade A, and why
today 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Today
You help the user run a morning ritual (plan focus items) or an evening ritual (close-out and prep tomorrow). Both modes write to the daily journal and to Dashboard/tasks.md.
Mode selection
Check $ARGUMENTS:
morning(or no argument before 4 PM local time): morning modeevening(or no argument after 4 PM local time): evening mode
If unclear, ask the user once: "Morning planning or evening close-out?"
Morning mode
Phase 1: Parallel data gather (no user interaction)
Do ALL of these before asking anything:
- Check if
journals/YYYY/MM-Month/DD-MM-YYYY.mdexists (month folder format:02-February). - Read yesterday's journal: mood, energy, activities, "Tomorrow I start with" (if filled).
- If today's journal doesn't exist, create it from
templates/daily-note.md. - Read
Dashboard/tasks.md- extract the "This week" section. - Read
Dashboard/Weekly P-Tasks.md- extract the current week's P-tasks.
Phase 2: Personal questions (one AskUserQuestion, 3-4 questions)
Use yesterday's values as hints.
- Mood (1-10) - hint with yesterday's mood. Options: "3-4 (rough)", "5-6 (okay)", "7-8 (good)", "9-10 (great)"
- Energy (1-10) - hint with yesterday's energy. Options: "3-4 (low)", "5-6 (medium)", "7-8 (high)", "9-10 (full power)"
- Self-care: "What did you do for yourself before work?" Options: "Exercise / run", "Walk", "Nothing special"
- Yesterday evening: "How was yesterday after work?" Options: "Relaxing, no stress", "Active (workout / outing)", "Stressful / tough"
Update the journal's frontmatter mood and energy, and fill yesterday + today personal sections.
If mood < 6 or energy < 6: set low_energy = true.
Phase 3: Calendar + focus planning (one round)
Ask the user to paste their calendar for the day (screenshot or text list).
After receiving the calendar:
- Extract meetings, calculate meeting time, calculate focus time, flag if >50% of the day is in meetings.
- Synthesize a daily plan from
Dashboard/tasks.md"This week", current P-tasks, and calendar deadlines.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 First seen · 161 lines · 22 tokens per session scan A 95927e41ed67
today is a skill published in the GitHub repository aleksander-dytko/ai-pm-workspace (34 stars, last pushed 6mo ago), licensed MIT. It adds 22 tokens to every session and 1,727 once invoked, about $0.0001 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.
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