plan-feature-execution

plan-feature-execution is a cursor rule for Cursor from golid-ai/golid. It costs 17 tokens per session (754 once invoked), scanned A, original, MIT.

A set of rules for turning a feature plan into safe, reviewable implementation slices.

In plain words
What is it for?
Use it with a feature plan to review data, permissions, APIs, user experience, documentation, quality checks, and recovery options.
Why use it?
It adds readiness checks, critique steps, deployment gates, rollback planning, and manual testing paths before work is shipped.

Cursor rule for Cursor

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/golid-ai/golid/plan-feature-execution
Clone the repo
git clone --depth 1 https://github.com/golid-ai/golid

Made for: Cursor.

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-feature-execution

README.md
[![agentmods](https://agentmods.dev/badge/rules/golid-ai/golid/plan-feature-execution.svg)](https://agentmods.dev/rules/golid-ai/golid/plan-feature-execution)
Your own site
<a href="https://agentmods.dev/rules/golid-ai/golid/plan-feature-execution"><img src="https://agentmods.dev/badge/rules/golid-ai/golid/plan-feature-execution.svg" alt="Measured on agentmods" height="20"></a>
Per session 17 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 754 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 $0.00017 $0.00754
Opus 5 $0.00009 $0.00377
Sonnet 5 $0.00003 $0.00151
Haiku 4.5 $0.00002 $0.00075

Measured 4d ago against content hash 0cd2d7848023, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

plan-feature-execution 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 4d 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.

.cursor/rules/plan-feature-execution.mdc · 82 lines

How it starts

The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Feature Plan Execution

Thesis: After the data model and API surface are planned, gate slices for deploy readiness, run critique loops on high-risk plans, and hand off to slice-and-ship with permissions and reference patterns locked in.

Use with: plan-feature (planning checklist and structure).

Execution Readiness

  • Prefer shippable slices that deliver user-visible value. Internal-only schema/service work can be an implementation milestone, but don't call it a complete slice unless it must land separately for review size.
  • Each user-facing slice needs a deploy gate, a rollback or kill-switch note, and a manual QA smoke path.
  • For risky migrations or auth/security changes, call out asymmetric rollback cases and whether hotfix-forward is safer than revert.

Iterative Planning Loop

For T2/T3 plans, treat planning as a critique loop before implementation, not a single authoring pass:

  1. Parent agent drafts or updates the plan and owns final decisions.
  2. Spawn focused subagents to audit independent surfaces: data/permissions, API/contracts, frontend/UX, docs/QA/rollback.
  3. Parent reconciles findings into the plan, explicitly recording decisions and rejected alternatives where future audits might disagree.
  4. Repeat once when subagents find high-severity gaps or plan contradictions.

Use this loop when the feature crosses privacy, security, money, public contracts, or three or more modules. Skip it for T0/T1 work; the extra time only pays back when a missed decision would create release risk.

Role-Based Permissions

When planning a feature, map each action to user roles. Example role levels:

Role Who
User Can view own data
Admin Can create/edit/manage all resources

In handlers, extract auth and check roles:

userID, err := requireUserID(c)
userType, err := requireUserType(c)
if userType != "admin" {
    return apperror.Forbidden("Admin access required")
}

Services accept userID directly from the handler (extracted from JWT context) — never look up user details in the service layer for auth purposes.

Read the full file on GitHub · 82 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. 4d ago First seen · 82 lines · 17 tokens per session scan A 0cd2d7848023

Subscribe to this mod's changes

plan-feature-execution is a cursor rule published in the GitHub repository golid-ai/golid (40 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 754 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.