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-plan/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-plan)<a href="https://agentmods.dev/skills/ariel-frischer/autospec/autospec-plan"><img src="https://agentmods.dev/badge/skills/ariel-frischer/autospec/autospec-plan/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-plan"><img src="https://agentmods.dev/badge/skills/ariel-frischer/autospec/autospec-plan.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 | $0.00012 | $0.01485 |
| Opus 5 | $0.00006 | $0.00743 |
| Sonnet 5 | $0.00002 | $0.00297 |
| Haiku 4.5 | $0.00001 | $0.00148 |
Grade A, and why
autospec-plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
autospec-plan
This Agent Skill is generated from autospec.plan. When the user invokes "$autospec-plan" or "/autospec.plan", load and follow these instructions directly. Treat the text after the skill or command name as "$ARGUMENTS". Do not route back through "autospec plan"; 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).
Pre-computed Context
The following paths have been pre-computed and are available for use:
- FEATURE_DIR:
{{.FeatureDir}} - FEATURE_SPEC:
{{.FeatureSpec}} - AUTOSPEC_VERSION:
{{.AutospecVersion}} - CREATED_DATE:
{{.CreatedDate}}
Outline
-
Load context:
- Read the spec file at
{{.FeatureSpec}} - Read project constitution if exists (
.autospec/constitution.yamlorAGENTS.md, falling back to agent-specific file likeCLAUDE.md) - Extract: feature description, user stories, requirements, constraints
- Read the spec file at
-
Execute plan workflow:
Phase 0: Outline & Research
a. Identify technical unknowns from the spec:
- For each unclear technology choice → research task
- For each dependency → best practices research
- For each integration → patterns research
b. Resolve unknowns through exploration:
- Examine existing codebase patterns
- Consider project constraints
- Make informed technology decisions
c. Document research findings for inclusion in plan
Phase 1: Design & Architecture
a. Define technical context based on spec and research:
- Language/framework (detect from existing code or choose)
- Primary dependencies
- Storage requirements
- Testing approach
- Target platform
b. Design project structure:
- Documentation files to create
- Source code organization
- Test file locations
c. Identify data model entities from spec requirements
d. Design API contracts if applicable
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 · 214 lines · 12 tokens per session scan A 9ae3778f0564
autospec-plan is a skill published in the GitHub repository ariel-frischer/autospec (141 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 1,485 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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