user-story

user-story is a skill for Claude Code, Codex from tmj-90/gaffer. It costs 53 tokens per session (810 once invoked), scanned A, original, Apache-2.0.

A guide for writing user stories: short descriptions of user value paired with clear, testable acceptance criteria. It also explains how to estimate story size for sprint planning.

In plain words
What is it for?
Writing feature stories, adding acceptance criteria, splitting large stories, and estimating sprint capacity.
Why use it?
It prevents vague requests, implementation-focused tickets, and stories that are too large to deliver reliably. Teams can agree on what done means before work starts.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Writing feature stories, adding acceptance criteria, splitting large stories, and estimating sprint capacity.

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Install with agentmods
npx agentmods add skills/tmj-90/gaffer/user-story
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.

Any agent
npx skills add tmj-90/gaffer --skill user-story
Clone the repo
git clone --depth 1 https://github.com/tmj-90/gaffer

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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agentmods badge for user-story

README.md
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Your own site
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agentmods 80×15 button for user-story

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Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 810 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00053 $0.00810
Opus 5 $0.00026 $0.00405
Sonnet 5 $0.00011 $0.00162
Haiku 4.5 $0.00005 $0.00081

Measured 6d ago against content hash e9f9ceb8787f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

user-story 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 6d 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.

runner/skills/user-story/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Generate structured user stories with acceptance criteria

A user story is not a task list — it's a unit of user value that can be independently delivered and tested.

Story format

As a [specific user role],
I want to [action / capability]
so that [outcome / value].

Acceptance criteria:
- Given [context], when [action], then [observable outcome].
- Given [context], when [error condition], then [error handling].

Anti-patterns to reject:

  • "As a user" (too vague — which user?)
  • Stories without AC ("it should work")
  • Stories that describe implementation ("add a button") instead of value ("so that I can...")
  • Stories > 8 story points (split them)

Story sizing heuristic (Fibonacci)

Points Scope
1 Trivial — confident implementation; no unknowns
2 Small — clear implementation; minor unknowns
3 Medium — clear approach; some complexity
5 Large — approach known; meaningful unknowns or cross-cutting
8 Extra large — approach uncertain; split if possible
13+ Epic — must be split before sprint planning

INVEST principles (every story should satisfy all six)

  • Independent — can be delivered without depending on another in-progress story.
  • Negotiable — the how is open; the what and why are fixed.
  • Valuable — delivers value to a real user, not just to engineering.
  • Estimable — the team can size it; unknowns are identified.
  • Small — fits in a sprint; ≤ 8 points.
  • Testable — ACs allow a tester to confirm done from not-done.

Sprint planning

Given a capacity (in story points) and a prioritised backlog:

  1. Sort stories by priority (impact × confidence / effort).
  2. Take from the top until capacity is consumed.
  3. Flag any story ≥ 8 points — must be split before it enters the sprint.
  4. Confirm each story in the sprint satisfies INVEST.

Steps

  1. Gather context. Feature or epic description; user personas available; any existing requirements or PRD.
  2. Identify the user roles involved. For each distinct role, generate stories independently.
  3. Write stories in standard format. One value unit per story; INVEST check for each.
  4. Write at least two ACs per story. Happy path + at least one error/edge case.
  5. Size each story. Flag 8+ for splitting.
  6. For sprint planning — sort by priority; fill to capacity; confirm no 13+ point stories in the sprint.

Read the full file on GitHub · 80 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. 6d ago First seen · 80 lines · 53 tokens per session scan A e9f9ceb8787f

Subscribe to this mod's changes

user-story is a skill published in the GitHub repository tmj-90/gaffer (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 53 tokens to every session and 810 once invoked, about $0.0003 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 2026-09-03.

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