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-tasks/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-tasks)<a href="https://agentmods.dev/skills/ariel-frischer/autospec/autospec-tasks"><img src="https://agentmods.dev/badge/skills/ariel-frischer/autospec/autospec-tasks/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-tasks"><img src="https://agentmods.dev/badge/skills/ariel-frischer/autospec/autospec-tasks.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 | $0.02320 |
| Opus 5 | $0.00006 | $0.01160 |
| Sonnet 5 | $0.00003 | $0.00464 |
| Haiku 4.5 | $0.00001 | $0.00232 |
Grade A, and why
autospec-tasks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
autospec-tasks
This Agent Skill is generated from autospec.tasks. When the user invokes "$autospec-tasks" or "/autospec.tasks", load and follow these instructions directly. Treat the text after the skill or command name as "$ARGUMENTS". Do not route back through "autospec tasks"; 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}} - IMPL_PLAN:
{{.ImplPlan}} - AUTOSPEC_VERSION:
{{.AutospecVersion}} - CREATED_DATE:
{{.CreatedDate}}
Outline
-
Load design documents: Read from the feature directory:
- Required:
{{.ImplPlan}}(plan.yaml) containing:technical_context: tech stack, libraries, constraintsdata_model: entities and relationshipsapi_contracts: API endpoints and schemasresearch_findings: technical decisionsproject_structure: file organization
- Required:
{{.FeatureSpec}}(spec.yaml) containing:user_stories: with priorities (P1, P2, P3)requirements: functional and non-functionalkey_entities: initial entity identification
- Required:
-
Execute task generation workflow:
- Extract tech stack, libraries, project structure from plan.yaml
technical_context - Extract user stories with their priorities from spec.yaml
user_stories - Extract entities from plan.yaml
data_modeland map to user stories - Map endpoints from plan.yaml
api_contractsto user stories - Extract decisions from plan.yaml
research_findingsfor setup tasks - Generate tasks organized by user story (see Task Generation Rules below)
- Generate dependency graph showing user story completion order
- Create parallel execution opportunities per phase
- Validate task completeness (each user story has all needed tasks)
- Extract tech stack, libraries, project structure from plan.yaml
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 · 283 lines · 13 tokens per session scan A 9030a106b02f
autospec-tasks 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 2,320 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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