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-clarify/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-clarify)<a href="https://agentmods.dev/skills/ariel-frischer/autospec/autospec-clarify"><img src="https://agentmods.dev/badge/skills/ariel-frischer/autospec/autospec-clarify/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-clarify"><img src="https://agentmods.dev/badge/skills/ariel-frischer/autospec/autospec-clarify.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 | $0.00022 | $0.01852 |
| Opus 5 | $0.00011 | $0.00926 |
| Sonnet 5 | $0.00004 | $0.00370 |
| Haiku 4.5 | $0.00002 | $0.00185 |
Grade A, and why
autospec-clarify scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
autospec-clarify
This Agent Skill is generated from autospec.clarify. When the user invokes "$autospec-clarify" or "/autospec.clarify", load and follow these instructions directly. Treat the text after the skill or command name as "$ARGUMENTS". Do not route back through "autospec clarify"; this skill is the prompt for the stage.
Project specs directory: ./specs
User Input
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Outline
Goal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec.yaml file.
Note: This clarification workflow should run BEFORE $autospec-plan. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.
Pre-computed Context
The following paths have been pre-computed and are available for use:
- FEATURE_DIR:
{{.FeatureDir}} - FEATURE_SPEC:
{{.FeatureSpec}}
-
Load and analyze the spec file at
{{.FeatureSpec}}. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing.Functional Scope & Behavior:
- Core user goals & success criteria
- Explicit out-of-scope declarations
- User roles / personas differentiation
Domain & Data Model:
- Entities, attributes, relationships
- Identity & uniqueness rules
- Lifecycle/state transitions
- Data volume / scale assumptions
Interaction & UX Flow:
- Critical user journeys / sequences
- Error/empty/loading states
- Accessibility or localization notes
Non-Functional Quality Attributes:
- Performance (latency, throughput targets)
- Scalability (horizontal/vertical, limits)
- Reliability & availability (uptime, recovery expectations)
- Observability (logging, metrics, tracing signals)
- Security & privacy (authN/Z, data protection, threat assumptions)
- Compliance / regulatory constraints (if any)
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.
- 12d ago First seen · 182 lines · 22 tokens per session scan A c240b1afbb2c
autospec-clarify is a skill published in the GitHub repository ariel-frischer/autospec (141 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 1,852 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.
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