Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/boshi-xixixi/TraeSkillnpx agentmods add skills/boshi-xixixi/traeskill/acreadiness-policyWrote 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/boshi-xixixi/traeskill/acreadiness-policy)<a href="https://agentmods.dev/skills/boshi-xixixi/traeskill/acreadiness-policy"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/acreadiness-policy/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/boshi-xixixi/traeskill/acreadiness-policy"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/acreadiness-policy.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 | $0.00073 | $0.00999 |
| Opus 5 | $0.00036 | $0.00500 |
| Sonnet 5 | $0.00015 | $0.00200 |
| Haiku 4.5 | $0.00007 | $0.00100 |
Grade A, and why
acreadiness-policy 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- acreadiness-policy — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/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
{
"name": "my-policy",
"criteria": {
"disable": ["env-example", "observability", "dependabot"],
"override": {
"readme": { "impact": "high", "level": 2 },
"lint-config": { "title": "Linter required" }
}
},
"extras": {
"disable": ["pre-commit"]
},
"thresholds": {
"passRate": 0.9
}
}
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:
- What to disable — irrelevant pillars or extras for their stack (e.g. disable
observabilityfor a static site). - What to raise — override
impacttohighorcriticalfor must-haves (e.g.readme,codeowners). - Pass-rate threshold — typical org baselines:
0.7(lenient),0.85(standard),1.0(strict). - Reference the policy from
agentrc.config.json:{ "policies": ["./policies/<name>.json"] }
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 · 97 lines · 73 tokens per session scan A e563c5e334e1
acreadiness-policy is a skill published in the GitHub repository boshi-xixixi/TraeSkill (262 stars, last pushed 3mo ago), licensed MIT. It adds 73 tokens to every session and 999 once invoked, about $0.0004 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 skills, from other repositories
<skill-name>
A template for defining a coding-agent skill, including its title, trigger situations, overview, workflow, common mistakes, and optional references.
spec-writing
A method for writing a software specification: a document that records decisions, reasons, boundaries, and ways to judge whether implementation succeeded. It first checks whether important unknowns require user clarification or technical research.
onboarding-unknown-codebase
A method for quickly understanding an unfamiliar codebase, meaning a software project whose structure and behavior you do not yet know. It builds a project map by examining overview files, directories, and one main execution path.
commit-message
A guide for writing clear, traceable Git commit messages using the Conventional Commits format, which labels changes such as features, bug fixes, documentation, and refactoring.
clarifying-questions
Guidance for clarifying vague or assumption-heavy requests before making changes.
debugging
A systematic method for finding the underlying cause of a software bug by observing the failure, forming a hypothesis, and testing it.