Systematically evaluate completed short stories or novel chapters to identify strengths, weaknesses, and improvement opportunities. Use after drafting to assess whether the piece achieves its narrative goals.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionstory-analysisExecute the skills CLI command in your project's root directory to begin installation:
Fetches story-analysis from jwynia/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate story-analysis. Access via /story-analysis in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Systematically evaluate completed short stories or novel chapters to identify strengths, weaknesses, and improvement opportunities. Use after drafting to assess whether the piece achieves its narrative goals.
| Mode | Use For | Key Focus |
|---|---|---|
| Short Story | Complete standalone pieces | Unity, shattering moment, resolution |
| Chapter | Sections within larger work | Continuity, momentum, arc contribution |
Evaluate:
Questions:
Evaluate:
Questions:
Red Flags:
Evaluate:
Questions:
Red Flags:
Definition: The pivotal point that transforms everything—a moment that cannot be undone.
Must function through one approach:
For Science Fiction:
Questions:
Evaluate:
Questions:
Economy of Detail:
Point of View:
Language and Voice:
Tension Management:
Reader Engagement:
Questions:
Evaluate:
Questions:
Evaluate:
Questions:
Evaluate:
Questions:
Red Flags:
Evaluate:
Track:
| Thread | Status This Chapter |
|---|---|
| Main plot | Advanced / Setup / Resolved |
| Subplot A | Advanced / Setup / Resolved |
| Subplot B | Advanced / Setup / Resolved |
Evaluate:
Questions:
Evaluate:
Evaluate:
Evaluate:
Previous Chapter:
Next Chapter:
Broader Novel:
Pattern: Treating all checklist items as equally important. Flagging every missed item as a problem requiring fix. Why it fails: Not all issues matter equally. A missing shattering moment is critical; a slight POV wobble might be stylistic choice. Checklists identify possibilities, not mandates. Fix: Prioritize by impact. Ask "Does this actually hurt the story?" before recommending changes. Some rules can be broken effectively.
Pattern: Applying one genre's standards to all stories. Evaluating literary fiction by thriller pacing standards, or action stories by literary depth requirements. Why it fails: Genres have different contracts with readers. What's a flaw in one genre is a feature in another. Fix: Identify genre expectations first. Evaluate against what the story is trying to be, not a universal standard.
Pattern: Focusing on micro-issues while missing macro problems. Catching comma splices while the story has no conflict. Why it fails: Sentence-level perfection can't save structural failure. Fixing small things first is procrastination from hard work. Fix: Diagnose at structure level first. Only move to prose-level analysis after structure is sound.
Pattern: Evaluating against published bestsellers or classics. "This isn't as good as [famous work]." Why it fails: Drafts aren't finished works. Different stories serve different purposes. Unfair comparison discourages rather than guides. Fix: Evaluate against the story's own goals. What is this trying to do? Does it succeed at that?
Pattern: Delivering a comprehensive list of all issues without prioritization or encouragement. Why it fails: Overwhelming. Writers shut down when facing 50 problems. No sense of what matters most. Fix: Identify 3-5 highest-impact issues. Note what's working. Create actionable, prioritized guidance.
Inbound:
drafting: After completing draftrevision: As part of revision processOutbound:
revision: For identified issuesscene-sequencing: For pacing problemsdialogue: For conversation issuescharacter-arc: For character problemsComplementary:
flash-fiction: For stories under 1500 wordsprose-style: For sentence-level issuesstory-sense: For diagnosing what's wrongMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
jwynia/agent-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in story-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: story-analysis is focused, and the summary matches what you get after install.
story-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: story-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
story-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in story-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
story-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.
story-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: story-analysis is focused, and the summary matches what you get after install.
Registry listing for story-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
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