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.
git clone --depth 1 https://github.com/hlalljie/agent-workflow-presetsWrote 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/rules/hlalljie/agent-workflow-presets/workflow)<a href="https://agentmods.dev/rules/hlalljie/agent-workflow-presets/workflow"><img src="https://agentmods.dev/badge/rules/hlalljie/agent-workflow-presets/workflow/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/rules/hlalljie/agent-workflow-presets/workflow"><img src="https://agentmods.dev/badge/rules/hlalljie/agent-workflow-presets/workflow.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 | $0.00000 | $0.01587 |
| Opus 5.5 | $0.00000 | $0.00635 |
| Sonnet 5.5 | $0.00000 | $0.00317 |
| Haiku 4.5 | $0.00000 | $0.00159 |
Grade A, and why
workflow 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 7d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Open questions
Each plan folder has its own open-questions.md. Numbers are scoped to that file (each file starts at 1). Numbers are permanent within their file — never renumber, never reuse.
Format: - [ ] N. Short description. Context/links inline.
Adding: pick next unused number in that file. One line with - [ ]. If it needs research, create a research doc and link it.
Resolving: check the box (- [x]). Record in that plan's decisions.md. Update the plan to reflect the decision — remove blocker references, update any sections that were pending this answer. Removing lines from this file after resolution is handled by the /commit flow (commit skill, "After committing") — not here.
Questions can be "parked" or "future" inline if not blocking anything now.
Reference from other docs within the same plan: "blocked by open question 2." Cross-plan references: "blocked by project open question 2" or "blocked by meta open question 6."
Research docs
Research is standalone. It does not contain status markers, links to open questions, or links to plans. Other docs link to research; research links to nothing upstream.
Executing tasks
Normative scope for how many tasks to touch lives in task-scope.mdc (alwaysApply: true). This section is the plan-format companion.
- One task at a time. Complete it and stop for verification before starting the next. Commits only via the commit skill, with owner approval (
git-safety.mdc). - Batch: only when
task-scope.mdcallows it (explicit multiple task numbers in one message, orbatchable: trueon the task file's first line and the user asked to batch in that message). - When delegating to subagents: the prompt must enforce this cadence. Tell the agent to do one task and return, without committing. Do not tell it to "implement all tasks."
- Subagent models follow
agent-models.mdc.
When a task completes
- Check task box.
- If it resolves an open question: check that box too, update decisions.
- If it changes plan scope: update the plan.
- Otherwise just check and move on.
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.
- 7d ago Changed · +13 lines 3f774e3e2f67
- 10d ago Changed 43e2249b5ac4
- 1mo ago First seen · 63 lines · 0 tokens per session scan A e2c2a434e1ac
workflow is a cursor rule published in the GitHub repository hlalljie/agent-workflow-presets (2 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,587 tokens. 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.
Other cursor rules, from other repositories
scrum-master
Scrum Master agent (Bob).
context-discipline
Manage AI context window efficiently - read before write, no re-reads, tool-call budgets, one-pass discipline.
swamr-state-manager
Swamr State Manager — tracks task progress, manages the execution pipeline, handles retries and escalation. Maintains swamr/state.json as the single source of truth for swarm progress.
pn-plugin-quality-gates
Keep Cursor plugin manifests, paths, and component metadata valid during plugin authoring.
github-repo-triage
Triage and maintain GitHub repos — issues, PRs, cherry-picks, releases, publishing.
task_structure_example.mouse
TaskID: MOUSE#TASK0001 # Example. MUST be unique & sequential. Title: "Example Task: Process User Survey Data and Generate Insights Report" Description: | This task involves processing raw user survey data, performing analysis, and generating a comprehensive insights report.