assess
FreeAssess source material against the ASDLC Knowledge Base and produce an evidence-calibrated Content Review Report.
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Dev-Critic loop: implement, review, fix — repeat until clean
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Review the instructions the agent will receive before you install.
Orchestrate a Dev -> Critic feedback loop, driving a task from implementation to a clean, verified state without human intervention — unless genuine blocking questions arise.
When a task needs full implementation AND review in one go.
Run dev and critic in alternating cycles until the critic issues a PASS verdict. Each cycle runs in its own sub-agent with a fresh context window. Only interrupt the user when a question genuinely blocks progress.
ship), authoring or modifying specs (spec), creating Linear issues (lead).Before starting the loop, gather the context the sub-agents will need:
$ARGUMENTSspecs/ for the task domain.AGENTS.md / CLAUDE.md for project constraints.Compile this into a Task Brief — a self-contained description that each sub-agent receives.
Spawn a sub-agent (model: sonnet / pro) with the full dev skill instructions and the Task Brief.
The dev agent prompt must include:
dev skill instructions (from .agents/skills/dev/SKILL.md or .claude/skills/dev/SKILL.md)Wait for the dev agent to complete. Capture its summary of changes made.
Spawn a sub-agent (model: sonnet / pro) with the full critic skill instructions.
The critic agent prompt must include:
critic skill instructions (from .agents/skills/critic/SKILL.md or .claude/skills/critic/SKILL.md)Wait for the critic agent to complete. Parse the verdict.
Based on the critic's verdict:
Circuit breaker: If you have completed 3 full cycles without reaching PASS, stop and present the remaining findings to the user. Ask whether to continue or adjust the approach. Do not loop forever.
Report to the user:
/ship to commit and pushFollow the standard model policy for sub-agent spawning:
$ARGUMENTS
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Assess source material against the ASDLC Knowledge Base and produce an evidence-calibrated Content Review Report.
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Perform an adversarial review of the current uncommitted changeset, validating it against project contracts and architectural intent.
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Triage the content corpus against the latest GSC snapshot and produce a prioritized refresh report. Read-only: reports, never edits.
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Implement features, fix bugs, and keep the codebase healthy and maintainable. Works from a defined PBI, issue, or task description.
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GEO+SEO audit of asdlc.io — analyzes the codebase for Generative Engine Optimization across AI citability, technical infrastructure, content quality, schema markup, and knowledge base discoverability. Produces a composite GEO Score (0-100) with prioritized action plan.
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Produce written specs and atomic PBIs that an implementation agent can execute independently. Authors specs and Linear issues.
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