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 agentmods add skills/mpaarating/ai-workflow-kit/todonpx skills add mpaarating/ai-workflow-kit --skill todogit clone --depth 1 https://github.com/mpaarating/ai-workflow-kitWhat 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 | $0.00011 | $0.00603 |
| Opus 5 | $0.00005 | $0.00302 |
| Sonnet 5 | $0.00002 | $0.00121 |
| Haiku 4.5 | $0.00001 | $0.00060 |
Grade A, and why
todo 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 2d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Todo
Capture tasks fast. One task or many, with smart priority and due date detection.
Trigger Phrases
- "todo:"
- "remind me to"
- "need to"
- "don't forget to"
- "add task:"
Workflow
Step 1: Parse Input
Extract task(s) from the user's message. Handle both single and batch input.
- Single:
todo: buy groceries→ one task - Batch:
todo: buy groceries, call dentist, review PR→ three tasks - Split on commas or newlines when multiple tasks are present
Step 2: Detect Priority
Scan each task for priority signals:
| Signal | Priority |
|---|---|
| "urgent", "asap", "critical", "today" | High |
| "soon", "this week", "important" | Medium |
| Everything else | Low |
Strip priority keywords from the task text after detection.
Step 3: Detect Due Date
Look for date references:
- Explicit: "by Friday", "due March 20", "before 5pm"
- Relative: "today", "tomorrow", "this week", "next Monday"
- Convert to an actual date based on the current date
- If no date found, leave blank
Step 4: Save
Add task(s) to the user's task system.
Using {{notes}}: Create or append to a tasks page/database with fields: Task, Priority, Due Date, Status (default: Open).
Markdown fallback: Append to ~/.ai-workflow/todos.md:
- [ ] Buy groceries | Priority: Low | Due: —
- [ ] Call dentist | Priority: Medium | Due: 2026-03-21
Step 5: Confirm
Respond with a single confirmation line per task:
Added: "Buy groceries" (low)
Added: "Call dentist" (medium, due Fri Mar 21)
Added: "Review PR" (low)
Keep it to one line per task. No extra commentary.
Examples
Single task:
todo: schedule haircut
Added: "Schedule haircut" (low)
With priority signal:
remind me to file taxes asap
Added: "File taxes" (high)
Batch:
todo: buy groceries, call dentist by Friday, review PR
Added: "Buy groceries" (low)
Added: "Call dentist" (medium, due Fri Mar 21)
Added: "Review PR" (low)
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.
- 2d ago First seen · 105 lines · 11 tokens per session scan A 776765a78e74
todo is a skill published in the GitHub repository mpaarating/ai-workflow-kit (2 stars, last pushed 3mo ago), licensed MIT. It adds 11 tokens to every session and 603 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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