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 agents/bdfinst/agentic-dev-team/ai-provenance-reviewgit clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWrote 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/agents/bdfinst/agentic-dev-team/ai-provenance-review)<a href="https://agentmods.dev/agents/bdfinst/agentic-dev-team/ai-provenance-review"><img src="https://agentmods.dev/badge/agents/bdfinst/agentic-dev-team/ai-provenance-review.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.01509 |
| Opus 5 | $0.00016 | $0.00754 |
| Sonnet 5 | $0.00006 | $0.00302 |
| Haiku 4.5 | $0.00003 | $0.00151 |
Grade A, and why
ai-provenance-review 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 6d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Provenance & Regeneration Safety Review
Scope: on-demand Cites: [adversarial-review-protocol]
Dispatched by the whole-tree /repo-review command, never by
/code-review's per-diff panel (#1733). "Verification debt" and
"regeneration risk" are trend/accumulation metrics by definition — best
judged by sweeping the whole codebase for AI-authored artifacts, not by
re-deriving the same judgment call independently on every diff.
select_lenses.py's resolver reads this Scope: on-demand declaration
directly and never selects it for the per-diff roster — the agent body is
the single source of truth for this exclusion, same as any other Scope:
kind.
Context needs: full-file
File scope: whole repository (see /repo-review) — never the per-diff changed-file list
What This Agent Checks
This agent answers two questions for every artifact that appears AI-generated:
- Verification debt: Is there evidence a human verified AI-authored test assertions and non-obvious production decisions against a spec — a commit note, ADR, PR review comment, or in-code rationale? Or were they accepted on trust?
- Regeneration risk: Which values, field orderings, or structural choices look like noise to a future AI cleanup pass but are load-bearing (magic constants, unusual ordering, encoding assumptions)?
This agent does not substitute for spec-compliance-review (criteria coverage) or mutation-kill (survivor elimination). It audits provenance of what those agents produce — particularly the tests mutation-kill generates and the fixes build autonomously commits.
Dependencies and overlap
adr-author.md— intent-anchoring ADRs are the primary evidence of human verification for non-obvious decisions; absence of an ADR for a non-obvious choice is a signal.spec-compliance-review.md— criteria coverage is a precondition, not a substitute; a test that covers a criterion but was generated without human review of its assertion value is still verification debt.mutation-kill.md— primary source of AI-authored fix tests to audit; its commits often lack rationale comments.
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.
- 6d ago First seen · 121 lines · 32 tokens per session scan A d60a26d55820
ai-provenance-review is an agent published in the GitHub repository bdfinst/agentic-dev-team (280 stars, last pushed today), licensed MIT. It adds 32 tokens to every session and 1,509 once invoked, about $0.0002 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.
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