AGENT SKILL

80-20-feature-prioritization

Use this skill when scoping a roadmap, prioritizing features, planning an MVP, or deciding what to ship and what to defer. Trigger when the user asks "what should we build next?", "is this feature worth it?", "should we ship feature X?", or when reviewing a feature backlog. Sub-aspect of `80-20-rule`; read that first.

适用职业产品经理
License 许可
Apache-2.0
最近更新
2026-11-13
Stars
★ 1.6k
Forks
483

01 前言 Front Matter

name
80-20-feature-prioritization
description
Use this skill when scoping a roadmap, prioritizing features, planning an MVP, or deciding what to ship and what to defer. Trigger when the user asks "what should we build next?", "is this feature worth it?", "should we ship feature X?", or when reviewing a feature backlog. Sub-aspect of `80-20-rule`; read that first.

02 资源文件 1

03 SKILL.md 正文

80/20 in feature prioritization

The most direct application of the 80/20 rule: deciding which features to invest in. Most products accumulate features over time; many of those features see little use. The discipline is identifying the critical fraction worth deepening and the long tail worth either tucking or removing.

The prioritization framework

For each feature (proposed or existing), gather:

1. Usage signal

If the feature exists: how many users engage with it? How often? Is engagement growing or declining?

If the feature is proposed: how many users say they want it? Is that signal coming from a vocal minority or a representative sample?

2. Value signal

When the feature is used, how much value does it produce? A rarely-used feature that produces enormous value when used (year-end tax export) may be more important than a frequently-used one that produces marginal value (a counter widget).

3. Effort signal

What does it cost to build, maintain, support, and document?

4. Strategic signal

Does the feature reinforce a competitive position, retain a key customer segment, or enable future capabilities?

The 80/20 cut isn't pure usage — it weights usage by value. A rarely-used essential feature stays in the critical fraction. A frequently-used decorative one might not.

Decision matrix

A simple 2×2 to organize:

                    Low value          High value
                    ───────────         ──────────
Low effort     │  Cleanup later   │   Ship now
High effort    │  Don't build     │   Major investment

The tricky cases are the diagonals: low-effort + low-value tasks accumulate as "death by a thousand cuts" if not pruned; high-effort + high-value tasks need ruthless scoping or they slip schedule.

Roadmap shaping

The 80/20 view of a roadmap:

  • The critical 20% of planned work should account for ~80% of expected user impact. If the planned work is spread evenly across many small features, you're probably underweighting the few that matter most.
  • The trailing 80% of planned work should be either skipped or radically scoped down.

A useful exercise: take a quarterly roadmap. For each item, estimate user impact and engineering effort. Sort by impact-per-effort. Cut the bottom half. The team's quarter is suddenly half the size with most of the impact preserved.

When the 80/20 cut hurts

  • Power-user retention. A feature only 5% of users touch may be the reason that 5% (often the highest-paying segment) chose your product.
  • Compliance and accessibility. Required by law or ethics; can't be dropped.
  • Foundational capability. A feature whose value is in enabling other features. Search isn't valuable in itself; it's valuable because it makes everything findable.
  • Trust signals. Audit logs, exportable data, account deletion. Rarely used directly but their presence reassures prospects.

These deserve a different evaluation than pure-usage 80/20 would suggest.

Roadmap anti-patterns

  • The feature factory. Every quarter ships 10 features. Most are small. None move metrics. Time fragments across all of them.
  • The pet feature. A senior leader's favorite that doesn't show up in user research but ships every quarter.
  • The "and one more thing." A small tweak added to a major feature that doubles its scope.
  • The unfinished long tail. Features shipped but never polished, growing into a maintenance burden.
  • The roadmap by request. Building features customers asked for without checking whether they reflect underlying needs (often the underlying need is met by a different feature you already have).

Worked example: pruning a settings page

A SaaS product's settings page has 38 controls. Usage analytics:

  • 7 controls used by ≥50% of users monthly.
  • 14 used by 10–50%.
  • 17 used by <10%.

Of the trailing 17:

  • 5 are needed for compliance (audit log, data export, account deletion). Keep.
  • 4 are integrations with services the product team is winding down. Remove from settings; deprecate.
  • 8 are legacy options most users have never touched. Move to "Advanced" disclosure; review for removal in a future cycle.

Result: settings page now shows ~7+5 (12 controls) primary; 14 secondary; rest tucked. Maintenance burden drops; common use is faster.

Heuristics

  1. The "what does the data say?" check. Usage analytics before opinion. If the data isn't available, instrument first; decide later.
  2. The "who would scream?" test. For features in the trailing 80%, ask. Each scream is a candidate to retain (or relocate); silence is permission to cut.
  3. The "would we build this today?" test. For each existing feature, imagine you were starting fresh. Would you build it? If no, it's a candidate for deprecation.
  4. The retention attribution. Pair feature usage with retention/expansion data. Features used by retained users may be more valuable than raw usage suggests.

Related sub-skills

  • 80-20-rule (parent).
  • 80-20-redesign-targeting — the within-existing-product version of the same prioritization.
  • hicks-law — every feature is an option; cutting features cuts decision cost.
  • progressive-disclosure — alternative to cutting: tucking.

04 同仓库更多 Skills

hashgraph-online
hashgraph-online/awesome-codex-plugins

migrate-to-e2e

Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for /migrate-to-e2e, "migrate our tests to e2e", "port these Maestro flows", "move our TesterArmy tests", or when a project has an e2e config and an older suite still running.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

e2e

Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

create-verification-skill

Generate a project-local verify-<app> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for /create-verification-skill, "make a verification skill for this repo", or when a project has no scripted way to prove UI behavior.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

deja-search

deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

deja-reflect

End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.

★ 1.6k
hashgraph-online
hashgraph-online/awesome-codex-plugins

revid

Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.

图像与多媒体Skill与智能体开发
★ 1.6k