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.
git 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/commands/ariel-frischer/autospec/autospec.clarify)<a href="https://agentmods.dev/commands/ariel-frischer/autospec/autospec.clarify"><img src="https://agentmods.dev/badge/commands/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/commands/ariel-frischer/autospec/autospec.clarify"><img src="https://agentmods.dev/badge/commands/ariel-frischer/autospec/autospec.clarify.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 | $0.00016 | $0.01763 |
| Opus 5 | $0.00008 | $0.00881 |
| Sonnet 5 | $0.00003 | $0.00353 |
| Haiku 4.5 | $0.00002 | $0.00176 |
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 9d 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
Integration & External Dependencies:
- External services/APIs and failure modes
- Data import/export formats
- Protocol/versioning assumptions
Edge Cases & Failure Handling:
- Negative scenarios
- Rate limiting / throttling
- Conflict resolution (e.g., concurrent edits)
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.
- 9d ago First seen · 173 lines · 16 tokens per session scan A 4b7dbce25fac
autospec.clarify is a command published in the GitHub repository ariel-frischer/autospec (141 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 1,763 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 commands, from other repositories
speckit.companion.implement
Companion implement — execute tasks.md in dependency order, then mark complete.
speckit.companion.plan
Companion plan — implementation plan with research & design artifacts.
speckit.companion.doctor
Report on a spec's run health — unfinished steps, unjournaled tasks, step bleed, drift you can judge, a step that closed having verified nothing, a step that closed without the file it promised, and why completion did not land (read-only, retroactive, never halts).
speckit.companion.living-move
Move a living spec between central and colocated storage — file, tiers, and registry together (opt-in, reversible).
speckit.companion.living-validate
Check the shape of living specs and a feature spec's deltas — a requirement with no scenario, a scenario missing WHEN or THEN, a duplicate heading, a delta pointing at nothing (opt-in, read-only, never halts).
speckit.companion.after-specify
Capture the current spec-kit step into .spec-context.json for the Companion GUI.