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.
npx skills add stark-ai-de/agent-skills --skill cursor-spec-interviewergit clone --depth 1 https://github.com/stark-ai-de/agent-skillsWrote 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.
[](https://agentmods.dev/skills/stark-ai-de/agent-skills/cursor-spec-interviewer)<a href="https://agentmods.dev/skills/stark-ai-de/agent-skills/cursor-spec-interviewer"><img src="https://agentmods.dev/badge/skills/stark-ai-de/agent-skills/cursor-spec-interviewer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/stark-ai-de/agent-skills/cursor-spec-interviewer"><img src="https://agentmods.dev/badge/skills/stark-ai-de/agent-skills/cursor-spec-interviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.02930 |
| Opus 5 | $0.00049 | $0.01465 |
| Sonnet 5 | $0.00020 | $0.00586 |
| Haiku 4.5 | $0.00010 | $0.00293 |
Grade A, and why
cursor-spec-interviewer 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cursor Spec Interviewer
Goal
Produce a user-verified implementation spec that Cursor Agent can execute with minimal ambiguity, minimal scope creep, explicit validation, explicit assumptions, a bounded source challenge, and ADRs for durable architectural decisions when needed. Save every final spec using the repo's clear convention or a confirmed destination; save ADR files only when the ADR gate requires one.
This is one end-to-end outcome, not a public multi-workflow skill. Do not invent review/save variants or add a workflow-selection checkpoint.
When to use
- The user has a rough idea but not a production-ready implementation spec.
- The task spans multiple files or concerns, or requires tradeoff decisions.
- The request needs acceptance criteria, validation commands, rollout notes, or risk handling.
- The user wants a reusable written artifact before implementation begins.
- Requirements, feature shape, ADR assumptions, or the implementation approach should be challenged against repo reality and current external sources before coding.
When not to use
- The user already provided a complete implementation spec with files, constraints, tests, and acceptance criteria.
- The task is a tiny one-file edit without meaningful ambiguity.
- The user wants brainstorming only and no concrete implementation artifact.
- The user only wants a Cursor rule, command, or other prompt-scope file authored, not an implementation spec.
- The task is primarily a policy, legal, or business-decision document.
- The user asks to audit or clean up Codex memory state; use a Codex memory skill instead.
Inputs to inspect
- The current user request and any follow-up answers.
- Relevant
AGENTS.md,.cursor/rules/**/*.mdc,README.md, issue descriptions, ADRs, repo docs, anddocs/agents/files. - Existing specs, plans, requirements, and PRDs the user wants preserved or challenged.
- File layout, naming conventions, scripts, package manager, lint/test/type-check commands, and CI expectations.
- Current framework, library, API, or platform documentation through available MCP tools or web search when a decision depends on up-to-date behavior.
- Error messages, screenshots, logs, PR feedback, or example files the user supplied.
- Cursor project skill folders such as
.agents/skills/or.cursor/skills/only when the spec depends on local Cursor skill behavior.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/cursor-execution-prompt.md 1.7 KB
- assets/example-repo-refactor.spec.md 10 KB
- assets/example-small-task.spec.md 3.5 KB
- assets/spec-template.compact.md 1.5 KB
- assets/spec-template.deep.md 4.1 KB
- assets/spec-template.standard.md 3.5 KB
- references/adr-gate.md 2.5 KB
- references/artifact-destinations.md 4.9 KB
- references/question-bank.md 4.4 KB
- references/rollout-checklist.md 2.7 KB
- references/source-challenge.md 2.6 KB
- references/spec-rubric.md 5.9 KB
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.
- 10d ago First seen · 142 lines · 99 tokens per session scan A df4eed4548d5
cursor-spec-interviewer is a skill published in the GitHub repository stark-ai-de/agent-skills (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 99 tokens to every session and 2,930 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
openlore-brainstorm
Transform a feature idea into an annotated story using a Domain Sketch or Constrained Option Tree. Use when asked to brainstorm, explore, or shape a feature before implementation.
openlore-execute-refactor
Apply a confirmed .openlore/refactor-plan.md with a test gate after each change. Use when asked to execute or continue an OpenLore refactoring plan.
openlore-plan-refactor
Identify a high-priority refactoring target, assess its blast radius, and write .openlore/refactor-plan.md without changing code. Use when asked to plan or prioritize a refactor.
openlore-debug
Debug with OpenLore structural context, an explicit root-cause hypothesis, and RED/GREEN verification. Use when a bug, failure, or regression needs diagnosis and repair.
openlore-implement-story
Implement a brownfield story with OpenLore orientation, risk checks, spec validation, tests, and drift detection. Use when asked to implement or continue a story in an existing codebase.
openlore-write-tests
Write and run real tests for a function or spec scenario after reading implementation and contract evidence. Use when asked to add, improve, or repair tests without stubs or placeholders.