Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/kenogami-ai/codebase-readiness/assess-codebase-readinessnpx skills add Kenogami-AI/codebase-readiness --skill assess-codebase-readinessgit clone --depth 1 https://github.com/Kenogami-AI/codebase-readinessWhat 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 | $0.00098 | $0.05670 |
| Opus 5 | $0.00049 | $0.02835 |
| Sonnet 5 | $0.00020 | $0.01134 |
| Haiku 4.5 | $0.00010 | $0.00567 |
Grade A, and why
assess-codebase-readiness 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 3d 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 — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assess codebase readiness
You are assessing the current working directory's codebase against the Readiness Grid — a nine-dimension diagnostic defined at https://framework.ai-native-transformation.com/codebase-readiness.
Produce a structured report: the codebase's state (greenfield / brownfield / hybrid), a score 1-5 per dimension with evidence, an overall readiness level (set by the lowest score, not the average), and a prioritized remediation plan.
Operating rules
- Actually run commands. Use Bash to collect evidence. Don't guess at coverage or CI times — measure them.
- Cite evidence. For every score, reference file paths, line numbers, or command output.
- Score conservatively. If the signal is ambiguous, round down.
- The ceiling is the lowest score. A codebase with eight 5s and one 1 is at the level of that 1. Agents fail at the weakest link.
- Apply deferral credit. An intentional, documented deferral (in a spec, ADR, roadmap, or README) scores one level higher than the same gap undocumented. Never apply deferral credit based on a verbal claim — only on something in the repo.
- Every score below 4 must map to a concrete remediation item. "Add tests" is not concrete. "Add test coverage for src/billing/invoice.ts (currently 0%)" is.
- Keep the output navigable. Follow the output template exactly. No creative reformatting.
Process
Step 1 — Detect the codebase shape
Before scoring, establish:
- Primary language(s) and framework(s). Read
package.json,pyproject.toml,go.mod,Cargo.toml,composer.json, etc. - Repo size:
find . -type f \( -name "*.ts" -o -name "*.tsx" -o -name "*.js" -o -name "*.py" -o -name "*.go" -o -name "*.rs" -o -name "*.java" -o -name "*.php" \) -not -path "*/node_modules/*" -not -path "*/dist/*" -not -path "*/build/*" -not -path "*/.next/*" | wc -l - Test infrastructure: presence of
tests/,__tests__/,spec/,*_test.go, etc. - CI config: check
.github/workflows/,.circleci/config.yml,.gitlab-ci.yml,azure-pipelines.yml.
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.
- 3d ago First seen · 338 lines · 98 tokens per session scan A 6ad90210f4f0
assess-codebase-readiness is a skill published in the GitHub repository Kenogami-AI/codebase-readiness (5 stars, last pushed 4mo ago), licensed MIT. It adds 98 tokens to every session and 5,670 once invoked, about $0.0005 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 2026-08-31.
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