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 skills add OneWave-AI/claude-skills --skill tech-due-diligencegit clone --depth 1 https://github.com/OneWave-AI/claude-skillsWrote 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/onewave-ai/claude-skills/tech-due-diligence)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/tech-due-diligence"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/tech-due-diligence/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/onewave-ai/claude-skills/tech-due-diligence"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/tech-due-diligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00091 | $0.00887 |
| Opus 5 | $0.00046 | $0.00443 |
| Sonnet 5 | $0.00018 | $0.00177 |
| Haiku 4.5 | $0.00009 | $0.00089 |
Grade A, and why
tech-due-diligence 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.
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Due Diligence Agent
Read a target company's codebase and produce a technical due diligence report that a non-technical investment committee member can act on, with the depth a CTO or VP Engineering expects.
Contents
references/investigation-protocol.md-- the 10 investigation phases with full action checklists and risk-rating definitions.references/output-template.md-- the exacttech-dd-report.mdreport structure, all tables, and the glossary.
Workflow
- Resolve the target. Accept a local codebase path, or clone a GitHub URL first. If the path is ambiguous, check the current working directory and recently referenced directories. Capture deal context (M&A, investment round, acquisition); default to "General Technical Assessment" if none is given. Begin immediately -- do not ask for confirmation.
- Run the full investigation. Execute all 10 phases in order per
references/investigation-protocol.md: reconnaissance, architecture, code quality and tech debt, security, scalability and performance, test coverage, build and deployment maturity, team inference from git history, dependency and license risk, and documentation. Read representative samples, not every file. Concentrate effort where risk signals appear. - Generate the report. Write
tech-dd-report.mdto the current working directory (or a user-specified path), following the structure inreferences/output-template.mdexactly.
Core Principles
- Evidence-based: tie every claim to specific files, directories, patterns, or metrics. Label any speculation as such.
- Quantified: attach numbers wherever possible -- lines of code, file counts, dependency counts, commit recency, test-to-code ratios, complexity estimates, vulnerability counts.
- Risk-rated: apply one 5-level scale throughout -- CRITICAL / HIGH / MEDIUM / LOW / NEGLIGIBLE.
- Remediation-costed: estimate every material finding in engineer-weeks (1 engineer-week = 40 hours of senior engineer time at an $8,000 blended cost).
- Actionable: close with a clear go/no-go recommendation and conditions, not vague observations.
What ships with it
2 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.
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 · 43 lines · 91 tokens per session scan A 61e6159feedf
tech-due-diligence is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 91 tokens to every session and 887 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 2026-09-03.
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