Borrowing it
Nothing to install: this file belongs to ariel-frischer/autospec. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ariel-frischer/autospec/main/.agents/skills/autospec-checklist/SKILL.mdgit clone --depth 1 https://github.com/ariel-frischer/autospecWrote 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/ariel-frischer/autospec/autospec-checklist)<a href="https://agentmods.dev/skills/ariel-frischer/autospec/autospec-checklist"><img src="https://agentmods.dev/badge/skills/ariel-frischer/autospec/autospec-checklist/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/ariel-frischer/autospec/autospec-checklist"><img src="https://agentmods.dev/badge/skills/ariel-frischer/autospec/autospec-checklist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00013 | $0.02092 |
| Opus 5 | $0.00006 | $0.01046 |
| Sonnet 5 | $0.00003 | $0.00418 |
| Haiku 4.5 | $0.00001 | $0.00209 |
Grade A, and why
autospec-checklist scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
autospec-checklist
This Agent Skill is generated from autospec.checklist. When the user invokes "$autospec-checklist" or "/autospec.checklist", load and follow these instructions directly. Treat the text after the skill or command name as "$ARGUMENTS". Do not route back through "autospec checklist"; this skill is the prompt for the stage.
Project specs directory: ./specs
Checklist Purpose: "Unit Tests for English"
CRITICAL CONCEPT: Checklists are UNIT TESTS FOR REQUIREMENTS WRITING - they validate the quality, clarity, and completeness of requirements in a given domain.
NOT for verification/testing:
- NOT "Verify the button clicks correctly"
- NOT "Test error handling works"
- NOT "Confirm the API returns 200"
- NOT checking if code/implementation matches the spec
FOR requirements quality validation:
- "Are visual hierarchy requirements defined for all card types?" (completeness)
- "Is 'prominent display' quantified with specific sizing/positioning?" (clarity)
- "Are hover state requirements consistent across all interactive elements?" (consistency)
- "Are accessibility requirements defined for keyboard navigation?" (coverage)
- "Does the spec define what happens when logo image fails to load?" (edge cases)
Metaphor: If your spec is code written in English, the checklist is its unit test suite. You're testing whether the requirements are well-written, complete, unambiguous, and ready for implementation - NOT whether the implementation works.
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Pre-computed Context
The following paths have been pre-computed and are available for use:
- FEATURE_DIR:
{{.FeatureDir}} - FEATURE_SPEC:
{{.FeatureSpec}}
Execution Steps
- Clarify intent (dynamic): Derive up to THREE initial contextual clarifying questions. They MUST:
- Be generated from the user's phrasing + extracted signals from spec/plan/tasks
- Only ask about information that materially changes checklist content
- Be skipped individually if already unambiguous in
$ARGUMENTS - Prefer precision over breadth
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.
- 11d ago First seen · 236 lines · 13 tokens per session scan A e1e3a8b47a1a
autospec-checklist is a skill published in the GitHub repository ariel-frischer/autospec (141 stars, last pushed 1mo ago), licensed MIT. It adds 13 tokens to every session and 2,092 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
tokf-filter
This skill should be used when the user asks to "create a filter", "write a tokf filter", "add a filter for ", "how do I filter output", or needs guidance on tokf filter step types, templates, pipes, or placement conventions.
tokf-discover
Find missed token savings by scanning AI coding session files for commands that ran without tokf filtering.
task-generation
Reference material with the canonical task-format grammar and decomposition rules for plan-to-tasks expansion. Loaded on demand by generate-tasks; not directly invokable.
memorywhale
Query and write durable debugging memory recorded by MemoryWhale. Use when debugging a failure that may have happened before, when you need the exact error/flags/output from an earlier attempt, when the user asks "how did we fix this last time?", or once you've figured out why something failed / how a fix worked and…
implementation-standards
Reference material with coding standards (defensive coding, error handling, testing patterns). Loaded on demand by the Developer sub-agent (.github/agents/developer.md); not directly invokable.
code-review
You are a senior code reviewer. You receive diffs via stdin.