Agent skill · product strategy · github
acreadiness-policy
Help the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
Why this skill is useful
Provides domain-specific knowledge on configuring AgentRC policies that the AI wouldn't reliably generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
/acreadiness-policy — AgentRC policies Use this skill when the user asks about policies, strict mode, custom scoring, disabling checks, org standards, or CI gating of readiness. A policy is a small JSON file with three optional sections — criteria, extras, thresholds — that customise how AgentRC scores readiness. Built-in examples AgentRC ships with three example policies in examples/policies/: Policy What it does --- --- strict.json 100% pass rate, raises impact on key criteria ai-only.json Disables all repo-health checks, focuses on AI tooling repo-health-only.json Disables AI checks, focuses on traditional quality Recommend these as starting points before writing a custom policy. Policy schema Impact weights Impact Weight --- --- critical 5 high 4 medium 3 low 2 info 0 Score = 1 − (deductions / max possible weight). Grades: A ≥ 0.9, B ≥ 0.8, C ≥ 0.7, D ≥ 0.6, F < 0.6. Sub-commands show List policies currently in effect (from agentrc.config.json policies array, or none). new <name> Scaffold policies/<name>.json with sensible defaults. Walk the user through: 1. What to disable — irrelevant pillars or extras for their stack (e.g. disable observability for a static site). 2. What to raise — override impact to high or critical for must-haves (e.g. readme, codeowners). 3. Pass-rate threshold — typical org baselines: 0.7 (lenient), 0.85 (standard), 1.0 (strict). 4. Reference the policy from agentrc.config.json: apply <path-or-pkg> Run agentrc readiness --json --policy <source> and re-render the report by handing off to the assess skill / ai-readiness-reporter agent. Supports chaining: CI gating Combine policies with --fail-level to enforce a minimum maturity level in CI: Advanced JSON policies can disable, override, and set thresholds — but cannot add new criteria. For new detection logic, point users at AgentRC's TypeScript plugin system (docs/dev/plugins.md). Operating rules Never silently disable a pillar. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/acreadiness-policy