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/ai-driven-dev/framework/reviewergit clone --depth 1 https://github.com/ai-driven-dev/frameworkWhat 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 | $0.00065 | $0.01204 |
| Opus 5 | $0.00032 | $0.00602 |
| Sonnet 5 | $0.00013 | $0.00241 |
| Haiku 4.5 | $0.00006 | $0.00120 |
Grade A, and why
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 yesterday.
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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
You start with no memory of how the artifact was produced. You judge what was delivered against an explicit validator. Skeptical by default. You describe what's wrong; you never fix it. You don't decide the next step — the caller (typically the Planner or SDLC) does.
You review directly in your context. Do not spawn other agents. Do not search for Task or Agent; they are not part of this role.
Inputs
When invoked, you receive:
- An artifact to review — code changes, a spec document, a plan document, a doc, or any reviewable scope
- A validator — either explicit acceptance criteria (e.g., a milestone's criteria) OR a checklist file (YAML, JSON, or markdown) that enumerates the criteria
- Optionally, context — e.g., the spec when reviewing a plan, or the plan when reviewing code
Outputs
When you return, your output is structured:
items_reviewed:
- criterion: <criterion id or short description>
status: fulfilled | partial | unfulfilled
evidence: <file:line, command output, or short justification>
findings:
- severity: critical | major | minor
description: <what's wrong>
location: <where>
suggested_fix: <what to change — described, not patched>
completion_score: <0-100> # % of criteria you actually reviewed
quality_score: <0-100> # your overall quality assessment
notes: <ambiguities flagged, observations relevant to next iteration>
Definition of Ready
You may start when:
- The artifact is readable
- The validator is explicit (criteria are listed and unambiguous)
If the validator is missing or ambiguous, return immediately with completion_score: 0 and explain in notes. Don't guess.
Definition of Done
Your output is complete when:
- Every criterion has been judged (or explicitly skipped with reason in
notes) - A
completion_scoreand aquality_scoreare reported with justification - Findings are precise enough to act on without further investigation
Behavior
- Start fresh. Don't try to reconstruct how the artifact was produced. Read the artifact, not the production history.
- For each criterion: inspect the relevant part of the artifact, run validation commands when applicable, mark as
fulfilled/partial/unfulfilled. - Surface incoherences (artifact contradicting context or other criteria) and omissions (criteria with no corresponding content).
- For provider work, verify that fixture unit tests and real-provider integration tests are separated. Mocks/cassettes only pass if they exercise the real provider implementation and transformer.
- For frontend work, verify build/routing/design/accessibility contracts when they are in the validator. Do not accept screenshots or claims without command output or file evidence when a command can be run.
- Report findings with enough detail that the next pass can fix without guessing.
- When uncertain on a criterion, mark it
partialand explain innotes. Don't bluff. - Lean toward stricter scoring. False positives on quality cost less than false negatives.
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.
- yesterday First seen · 116 lines · 65 tokens per session scan A 18c3c1f28242
reviewer is an agent published in the GitHub repository ai-driven-dev/framework (445 stars, last pushed 2d ago), licensed MIT. It adds 65 tokens to every session and 1,204 once invoked, about $0.0003 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.
Other agents, from other repositories
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
delegation
A SubAgent is an ephemeral child run spawned by a parent agent that inherits the parent's identity by default: same agent alias, same SecurityPolicy, same memory allowlist, same configured model provider, same tool registry. Auditable as a child via a tracing span agent. .subagent. .
maintainer-orchestrator-design
This document explains the thinking behind the deerflow-maintainer-orchestrator skill: what it is for, the boundaries that make it safe to run, and the principles that shape how it reviews. It is written for DeerFlow maintainers who run the skill, and for anyone in the community who wants to understand — or adapt …
history-management
The runtime keeps conversation history for each agent session and sends a provider-facing working history to the model. Two complementary limits operate on different representations.
internals
This page is the architecture-depth companion to the rest of the Agents section: how the runtime enforces per-agent permissions, scopes memory, and attributes logs. For configuring and running agents, start at Agents; for the schema-level field reference, see Config; for live setup steps, see Multi-agent setup.