plan-sprint

plan-sprint is a cursor rule for coding agents from Hundia/autospec. It costs 0 tokens per session (910 once invoked), scanned A, original, MIT.

A sprint-planning workflow that gathers input from several specialist roles and combines it into one prioritized plan. A sprint is a short, focused period of development work.

In plain words
What is it for?
Use it to review project goals and the backlog, choose relevant experts for the work, and write a dependency-aware sprint plan.
Why use it?
It helps turn a broad project goal into ordered tasks while identifying dependencies and risks before work begins.

Cursor rule

Install

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.

agentmods
npx agentmods add rules/hundia/autospec/plan-sprint
Clone the repo
git clone --depth 1 https://github.com/Hundia/autospec

Wrote 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.

agentmods badge for plan-sprint

README.md
[![agentmods](https://agentmods.dev/badge/rules/hundia/autospec/plan-sprint.svg)](https://agentmods.dev/rules/hundia/autospec/plan-sprint)
Your own site
<a href="https://agentmods.dev/rules/hundia/autospec/plan-sprint"><img src="https://agentmods.dev/badge/rules/hundia/autospec/plan-sprint.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Nothing until a file matches its globs; then the whole rule loads.
When invoked 910 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00910
Opus 5 $0.00000 $0.00455
Sonnet 5 $0.00000 $0.00182
Haiku 4.5 $0.00000 $0.00091

Measured today against content hash 6f6cfad7f3b1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

plan-sprint 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 today.

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.

examples/ai-support-ops/skills/cursor/rules/plan-sprint.mdc · 119 lines

How it starts

The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Plan Sprint

Plan a sprint using multi-expert analysis and PM synthesis. This produces a prioritized, dependency-aware sprint plan written to specs/backlog.md.

Phase 1: Goal Analysis & Expert Selection

  1. Read specs/backlog.md to understand the current state
  2. Read specs/01-project-overview.md for project goals and constraints
  3. Identify the sprint goal (what should be accomplished)
  4. Select which experts to activate based on the goal

Expert Activation Criteria

Expert Activate When
Architect New subsystem, major refactor, integration work
UX/UI User-facing features, viewer changes, new workflows
Database Schema changes, data migration, query optimization
Human Experience Onboarding flows, error messages, documentation UX
Security Auth changes, input handling, API exposure
Performance Load concerns, bundle size, query efficiency

Activate 2-4 experts per sprint. Always include Architect for structural changes.

Phase 2: Expert Analysis

Each activated expert provides:

  1. Assessment: What does this sprint goal mean for their domain?
  2. Risks: What could go wrong?
  3. Recommendations: Specific tickets or approaches
  4. Dependencies: What must happen first?
  5. Effort estimate: T-shirt size (S/M/L/XL) per recommendation

Expert Output Format

### Expert: [Role]
**Assessment**: [1-2 sentences]
**Risks**: [bulleted list]
**Recommendations**:
- [ticket idea] (Size: M, Depends on: none)
- [ticket idea] (Size: L, Depends on: above)
**Dependencies**: [cross-expert dependencies]

Phase 3: PM-A Draft

The first PM synthesizes expert input into a draft sprint plan:

  1. Collect all expert recommendations
  2. Resolve conflicting advice (prefer Architect for structure, UX for user-facing)
  3. Order tickets by dependency and priority
  4. Assign ticket numbers (X.1, X.2, etc.)
  5. Write the draft sprint section

Draft Format

## Sprint X — [Sprint Goal]

- 🔲 **X.1** — [Description] (Size: S)
- 🔲 **X.2** — [Description] (Size: M, depends on X.1)
- 🔲 **X.3** — [Description] (Size: L)

Read the full file on GitHub · 119 lines

Changes

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.

  1. today First seen · 119 lines · 0 tokens per session scan A 6f6cfad7f3b1

Subscribe to this mod's changes

plan-sprint is a cursor rule published in the GitHub repository Hundia/autospec (4 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 910 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 2026-09-03.