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 weekly-plangit 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/weekly-plan)<a href="https://agentmods.dev/skills/aleksander-dytko/ai-pm-workspace/weekly-plan"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/weekly-plan/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/weekly-plan"><img src="https://agentmods.dev/badge/skills/aleksander-dytko/ai-pm-workspace/weekly-plan.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 | $0.00019 | $0.02044 |
| Opus 5 | $0.00010 | $0.01022 |
| Sonnet 5 | $0.00004 | $0.00409 |
| Haiku 4.5 | $0.00002 | $0.00204 |
Grade A, and why
weekly-plan 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Weekly P-Tasks
You help plan the upcoming week's P-Tasks (3-5 meaty priorities) with an overplanning challenge and triage of the in-repo task list.
Input
The user invokes this skill (typically Sunday evening or Monday morning) without arguments, or with context like "plan my week".
The user may:
- Let you suggest P-tasks based on context
- Propose their own P-task list for validation
- Provide calendar context (screenshot or text)
Workflow
1. Review previous week
Read Dashboard/Weekly P-Tasks.md. Find the most recent week's P-tasks.
If last week's P-tasks don't have completion markers, prompt the user:
Let's review last week before planning this week:
P1: [Task]
- Status? (Completed / Partial / Not done)
- If completed: results?
- If partial or not done: what blocked it?
P2: [Task]
...
Wait for review, then update last week's entry with markers:
✅completed (add a note about results)🔄partial / carry over (add a note about what's left)❌not started / deprioritized (add a note about why)
Compute last week's completion rate (completed / total) - used for the overplanning check.
2. Analyze recent daily notes
Read the last 7 days from journals/YYYY/MM-Month/.
Identify:
- Carryover items mentioned but not finished.
- Energy/mood patterns from frontmatter and content:
- Declining trend (e.g., 7 → 5).
- Mentions of overwork, burnout, feeling overwhelmed.
- Recurring themes or blockers.
- Weekly review notes (Friday entries).
Capacity adjustment:
- Energy < 6 or declining trend → suggest 3-4 P-tasks max.
- "Feeling overwhelmed" appears → reduce scope aggressively.
3. Calendar context
If the user hasn't shared their calendar, ask: "Share your calendar for this week or highlight key deadlines."
Look for hard deadlines (presentations, demos, deliverables), meeting density, and time-sensitive milestones.
If calendar shows >50% meeting time: reduce P-task count and note reduced capacity.
4. Review recent meetings for follow-ups
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.
- 11d ago First seen · 263 lines · 19 tokens per session scan A c353a818694e
weekly-plan is a skill published in the GitHub repository aleksander-dytko/ai-pm-workspace (34 stars, last pushed 4mo ago), licensed MIT. It adds 19 tokens to every session and 2,044 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 2026-08-30.
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