Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/datahub-project/datahub-skillsnpx agentmods add commands/datahub-project/datahub-skills/comprehensive-reviewWrote 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/commands/datahub-project/datahub-skills/comprehensive-review)<a href="https://agentmods.dev/commands/datahub-project/datahub-skills/comprehensive-review"><img src="https://agentmods.dev/badge/commands/datahub-project/datahub-skills/comprehensive-review/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/commands/datahub-project/datahub-skills/comprehensive-review"><img src="https://agentmods.dev/badge/commands/datahub-project/datahub-skills/comprehensive-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00020 | $0.02091 |
| Opus 5 | $0.00010 | $0.01045 |
| Sonnet 5 | $0.00004 | $0.00418 |
| Haiku 4.5 | $0.00002 | $0.00209 |
Grade A, and why
comprehensive-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 10d 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comprehensive Multi-Agent Connector Review
You are orchestrating a comprehensive code review using specialized agents. This provides deeper analysis than a single-pass review by using focused experts for different aspects of code quality.
Available Agents
| Agent | Focus Area | Key Findings |
|---|---|---|
| silent-failure-hunter | Error handling, logging | Silent failures, missing error context |
| test-analyzer | Test coverage, quality | Trivial tests, coverage gaps (priority 1-10) |
| type-design-analyzer | Types, Pydantic models | Type safety issues (scores 1-10 per dimension) |
| code-simplifier | Complexity, readability | Refactoring opportunities |
| comment-resolution-checker | Review comment follow-through | Unaddressed comments, suspicious resolutions |
Execution Workflow
Step 1: Determine Scope
If connector specified, validate the name is alphanumeric (letters, digits, hyphens, underscores only) before use:
# Quote the connector name to prevent shell injection
./scripts/gather-connector-context.sh "{{connector}}" [datahub_repo_path]
If no connector (PR review):
# Get changed files
git diff --name-only main
Step 2: Launch Agents
Mode: Parallel (default) Launch all selected agents simultaneously using the Task tool with multiple tool calls in a single message. This is faster but produces separate reports.
Mode: Sequential Run agents one at a time, allowing each to complete before the next. This allows findings from one agent to inform others.
Step 3: Agent Selection
Based on agents argument:
all→ Run all 5 agentssilent-failures→ Run silent-failure-hunter onlytests→ Run test-analyzer onlytypes→ Run type-design-analyzer onlysimplify→ Run code-simplifier onlycomments→ Run comment-resolution-checker onlysilent-failures,tests→ Run specified combination
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.
- 10d ago First seen · 269 lines · 20 tokens per session scan A f00150d7f33e
comprehensive-review is a command published in the GitHub repository datahub-project/datahub-skills (38 stars, last pushed 12d ago), licensed Apache-2.0. It adds 20 tokens to every session and 2,091 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
sdd-init
Initialize SDD context — detects project stack and bootstraps persistence backend.
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
init
Install the formatters this repository needs, with every command visible before it runs.
merge-conflict-analysis
You are analyzing merge conflicts for PR #${{ pr-number }}.
repo-audit
Audit a codebase (local or remote GitHub/GitLab) against architecture principles and requirements, surfacing drift, risk, and missing decisions.
argos
A command for checking whether an implementation matches its design deliverables. Its Korean description compares the work to the design as part of a completion inspection.