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/dgalarza/claude-code-workflows/codebase-readinessnpx skills add dgalarza/claude-code-workflows --skill codebase-readinessgit clone --depth 1 https://github.com/dgalarza/claude-code-workflowsWhat 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 | $0.00070 | $0.03523 |
| Opus 5 | $0.00035 | $0.01761 |
| Sonnet 5 | $0.00014 | $0.00705 |
| Haiku 4.5 | $0.00007 | $0.00352 |
Grade B, and why
codebase-readiness scanned grade B with 1 finding 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 2d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
4. **Compose the prompt** — role preamble + codebase snapshot + dimension content + quality-gates content + language content + output instructions How it starts
The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Readiness Assessment
Run an Agent-Ready Codebase Assessment — a scored evaluation of how well a codebase supports autonomous AI agent work, framed against the Stripe benchmark of 1,000+ AI-generated pull requests per week.
Work through the following phases sequentially.
Phase 1: Codebase Reconnaissance
Execute the reconnaissance script to gather project metadata before launching assessment agents. Run directly — no agent needed.
bash scripts/recon.sh
The script is located at scripts/recon.sh relative to this skill's directory.
After reviewing the output, format a Codebase Snapshot:
## Codebase Snapshot: [Project Name]
- **Primary language/framework**: [detected]
- **Language tier**: [statically-typed | dynamically-typed | gradually-typed]
- Statically-typed: TypeScript, Go, Java, Scala, Rust, C#, Kotlin
- Dynamically-typed: Ruby, Python (unannotated), JavaScript/Node.js, PHP
- Gradually-typed: Python with mypy/Pydantic, TypeScript with strict:false
- **Commit count**: [X]
- **Contributors**: [X]
- **Source files**: [X]
- **Test files**: [X] (ratio: X%)
- **CI/CD**: [platform(s) found or none]
- **CLAUDE.md**: [present at path, X lines / absent]
- **Linting config**: [tools found or none]
- **Quality gates**: [copy the `Suggested Gate Maturity Level: Lx -- ...` line from recon output, e.g. "L0 -- no complexity, duplication, or dead-code tooling detected"]
- **README**: [present, X lines / absent]
The Quality gates line is required. recon.sh ends its === QUALITY GATES === section with Suggested Gate Maturity Level: Lx -- <reason>. Copy that level and reason onto the snapshot's Quality gates line. Then read references/quality-gates.md and check the suggestion against the detailed evidence above it (L0 none, L1 report-only, L2 threshold, L3 regression-aware blocking, L4 governed); change the line only when the evidence contradicts the heuristic, and say why. Do not launch the assessment agents with a snapshot that lacks this line -- the agents score only their own slice of the gate evidence and read the level from the snapshot; if it is missing, each agent re-derives it independently and the four dimensions drift apart.
What ships with it
20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/report-template.md 8.9 KB
- references/dimensions/architecture-clarity.md 6.0 KB
- references/dimensions/change-safety.md 7.2 KB
- references/dimensions/code-clarity.md 7.7 KB
- references/dimensions/consistency.md 6.6 KB
- references/dimensions/documentation.md 14 KB
- references/dimensions/feedback-loops.md 7.7 KB
- references/dimensions/test-foundation.md 5.4 KB
- references/dimensions/type-safety.md 4.0 KB
- references/languages/go.md 6.5 KB
- references/languages/java.md 7.9 KB
- references/languages/javascript.md 7.0 KB
- references/languages/php.md 13 KB
- references/languages/python.md 7.2 KB
- references/languages/ruby.md 6.4 KB
- references/languages/rust.md 12 KB
- references/languages/scala.md 16 KB
- references/languages/typescript.md 7.2 KB
- references/quality-gates.md 11 KB
- scripts/recon.sh 8.0 KB runs code
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.
- 2d ago First seen · 341 lines · 70 tokens per session scan B 5a1d0388eb43
codebase-readiness is a skill published in the GitHub repository dgalarza/claude-code-workflows (59 stars, last pushed 5d ago), licensed MIT. It adds 70 tokens to every session and 3,523 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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