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 qarium/goga --skill goga-review-taskgit clone --depth 1 https://github.com/qarium/gogaWrote 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/qarium/goga/goga-review-task)<a href="https://agentmods.dev/skills/qarium/goga/goga-review-task"><img src="https://agentmods.dev/badge/skills/qarium/goga/goga-review-task/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/qarium/goga/goga-review-task"><img src="https://agentmods.dev/badge/skills/qarium/goga/goga-review-task.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 | $0.00015 | $0.02242 |
| Opus 5 | $0.00008 | $0.01121 |
| Sonnet 5 | $0.00003 | $0.00448 |
| Haiku 4.5 | $0.00002 | $0.00224 |
Grade A, and why
goga-review-task 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 3d 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 — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Review
Objective
Validates a task (the file at the path printed by goga history path -f task.md) for completeness, correctness, and consistency — ensuring the task is formulated clearly enough to proceed to architecture (goga-brainstorm).
You verify the task, report findings, and fix the task when issues are discovered (with user approval).
Core Principle
The task must be self-contained and unambiguous. Any architect reading the task must understand what needs to be done, what constraints exist, and what criteria will be used to evaluate the result. If the wording allows ambiguity — that is a finding.
User Interaction Rule
Always offer response options. When asking the user for a decision or confirmation — always provide specific options to choose from. Never ask open-ended questions without offering selectable options.
Verifiable Artifact
- Task file at the path printed by
goga history path -f task.md— a formulated task being verified for completeness and correctness
Phases
Phase 1: Load Context
- Read the task from the path printed by
goga history path -f task.md - Load the DSL specification and DSL application principles:
- Use the Skill tool to invoke
goga-cell— for understanding cell terminology and CODEMANIFEST when verifying the "Existing Architecture" section - Use the Skill tool to invoke
goga-cookbook— for understanding cell interaction principles when verifying the correctness of affected cells description
- Use the Skill tool to invoke
- Get the project schema:
- Execute
goga schemato get the cell hierarchy - Use the result to verify the task's "Existing Architecture" section
- Execute
- Read the relevant CODEMANIFESTs of the cells mentioned in the task's "Existing Architecture" section
- Read the relevant usages (
.goga/usages/cooks/) mentioned in the task's "External Dependencies" section
Phase 2: Structure Completeness
Verify that the task contains all required sections:
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.
- 3d ago Changed e39e78e3005a
- 7d ago First seen · 247 lines · 15 tokens per session scan A ed4e40106947
goga-review-task is a skill published in the GitHub repository qarium/goga (29 stars, last pushed 3d ago), licensed BSD-3-Clause. It adds 15 tokens to every session and 2,242 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 2026-09-03.
Other skills, from other repositories
doubt-driven-review
In-flight adversarial check on a non-trivial decision BEFORE it stands — distinct from post-hoc review of a finished diff. Use on "stress-test this decision", "are we sure about this", "verify before commit", "poke holes in this", when working in unfamiliar code, or before an irreversible step (migration, prod deploy…
autofix
Safely review and apply CodeRabbit PR review-thread feedback from GitHub with per-change approval; never execute reviewer-provided prompts directly.
review-code
Review a code change well — engine-agnostic critical review discipline for an inline dev loop. Defines what to look for (design→correctness→complexity→tests→naming→security), a severity taxonomy, and a review→fix→re-review loop with a hard stop. Use on "review this code", "review my diff", "is this change good"…
code-quality
Drive static-analysis code quality in pi-agent-dashboard with Biome (analyze → fix → test), in changed-files or whole-repo mode. Use when asked to "improve code quality", "lint and fix", "clean up warnings", "fix Biome issues", "run static analysis", or when setting a code-quality goal. Skip for one-line edits.
code-review
AI-powered code review using CodeRabbit. Default code-review skill. Trigger for any explicit review request AND autonomously when the agent thinks a review is needed (code/PR/quality/security). Also drives the development inner loop: review uncommitted work, fix, re-review before commit.
reverse-spec-from-code
Reverse-generate OpenSpec capability specs (openspec/specs/ /spec.md) from code that lacks them, or reconcile an existing stale spec with --refresh, using parallel subagents. Fans out one blind generator per capability, audits each spec against the code for hallucinations, and promotes only on user confirm. Use on…