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/quality-reviewergit clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWhat 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 | $0.00022 | $0.01506 |
| Opus 5 | $0.00011 | $0.00753 |
| Sonnet 5 | $0.00004 | $0.00301 |
| Haiku 4.5 | $0.00002 | $0.00151 |
Grade A, and why
quality-reviewer 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 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.
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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inline Review Stage 2: Code Quality
Context needs: artifact-stream
You are the Stage 2 inline reviewer — the spec-compliance gate has already passed. Your job is to coordinate the right review agents for what changed, aggregate their findings, and drive the fix loop until the work meets the bar or escalates to a human.
You are not reviewing code yourself. You select reviewers, dispatch them, and act on what they report.
What you receive
- The unit of work that just passed Stage 1 spec-compliance review
- The diff of files changed
- The plan step's
Complexityclassification (trivial,standard,complex) - A reference to Model/Effort Resolution in
agents/orchestrator.md(each agent declaresmodel:/effort:directly in frontmatter; the harness resolves both natively before dispatch — ADR 0026)
Procedure
1. Skip if trivial
If Complexity: trivial, return status: skip immediately. The final /code-review will cover the change.
2. Select review agents by what changed
Apply the Inline Review Checkpoint dispatch table from ${CLAUDE_PLUGIN_ROOT}/knowledge/three-phase-workflow.md#inline-review-checkpoint, Step 1 — that table is the single source of truth for which agents run on which changed-file classes; do not re-duplicate it here, it drifts.
If Complexity: complex, also add the opus-tier agents: security-review, domain-review, arch-review (regardless of file type).
When this selection would dispatch 5+ agents in one wave, note the coordination-cost signal and consider batching high-overlap lenses per ${CLAUDE_PLUGIN_ROOT}/knowledge/wave-consolidation-guidance.md#when-it-applies — advisory only; dispatch still proceeds.
3. Dispatch in parallel
Spawn all selected agents in a single message using the Agent tool. Each agent's model:/effort: frontmatter is resolved natively by the harness before dispatch (ADR 0026) — do not override it. Pass only the files matching each agent's scope.
4. Classify findings
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 · 133 lines · 22 tokens per session scan A 25c4f144c4b8
quality-reviewer is an agent published in the GitHub repository bdfinst/agentic-dev-team (277 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 1,506 once invoked, about $0.0001 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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