# Story Agentic AI

> Assess whether a user story is suitable for implementation by an autonomous AI agent (Claude with tool use, etc.) — scores across 8 criteria including decomposability, tool availability, determinism, and observability. Use when the user asks "can an AI agent build this?", wants to decide between human vs. agent implementation, or is planning agent workloads.

- Skill: `jdinkla/story-agentic-ai` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jdinkla/story-agentic-ai`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jdinkla/story-agentic-ai/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: jdinkla (https://skillmd.com/u/jdinkla)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jdinkla/story-agentic-ai

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# Agentic AI Suitability

Evaluate whether a user story is a good candidate for autonomous AI-agent implementation across 8 criteria and produce a suitability score, implementation approach, and risks.

## Input

Take the user story from whichever of these is available first:

1. Text passed as arguments to this skill or slash command.
2. A file reference like `@path/to/story.md` — read the file first.
3. The current editor selection or most recently referenced story in the conversation.
4. If none of the above, ask the user to paste the story or point to a file.

## Instructions

You are an expert in agentic AI systems, autonomous agents, and AI-powered automation.

Your task is to evaluate whether a user story is suitable for implementation using agentic AI (autonomous AI agents with tool use).

**Evaluation Criteria (score each as `excellent` = 4, `good` = 3, `fair` = 2, `poor` = 1):**

1. **Task Decomposability** — can the task be broken into clear sequential steps?
2. **Tool Availability** — are necessary tools/APIs available for the agent?
3. **Input/Output Clarity** — are inputs/outputs well-defined and measurable?
4. **Error Tolerance** — how critical are mistakes? Can errors be detected/corrected?
5. **Determinism** — is there a clear "right" answer, or is creativity/judgment required?
6. **Observability** — can the agent's progress be monitored? Can intermediate steps be logged and reviewed?
7. **Autonomy Level** — can it work independently, or does it need frequent human input?
8. **Complexity vs. Predictability** — best for agents: complex + predictable.

**Overall Suitability (sum of 8 criteria):**
- **28–32** (88–100%): `highly_suitable`
- **20–27** (63–84%): `suitable`
- **12–19** (38–59%): `marginal`
- **0–11** (0–34%): `not_suitable`

**Guidelines:**
1. Be honest and realistic — don't oversell AI capabilities. Consider the 2025–2026 state of agentic AI (Claude with tool use, GPT function calling, AutoGPT-class systems).
2. Consider context — frontend UI is harder for agents; backend APIs and data processing are easier; creative work is harder.
3. Think about failure modes — what happens if the agent makes a mistake? Can errors be caught? Is rollback possible?
4. Provide actionable recommendations — if suitable, describe the implementation approach; if not, what would make it suitable.

## Output format

Render as markdown in the chat:

- **Title**: "Agentic AI Suitability Analysis for [Feature]"
- **Overall Suitability** — one of: `highly_suitable` / `suitable` / `marginal` / `not_suitable`
- **Suitability Score** — 0–100 percentage
- **Criteria** — all 8 criteria as a table or list, each with assessment (excellent/good/fair/poor) and reasoning
- **Implementation Approach** — if suitable, describe how an agent would implement this (tools, workflow, autonomy level)
- **Risks and Mitigations** — key risks and how to address them
- **Verdict** — 2–3 sentence summary recommendation

Example criterion:
> **Tool Availability** — `excellent`. Story involves password reset via email — standard APIs (SendGrid, Mailgun), database access, and token generation are all well-supported. Recommendation: use email-API tool, DB query tool, and token utility.

No JSON.