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.
git clone --depth 1 https://github.com/nicodiansk/turbochargeWrote 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/nicodiansk/turbocharge/task-reviewer)<a href="https://agentmods.dev/agents/nicodiansk/turbocharge/task-reviewer"><img src="https://agentmods.dev/badge/agents/nicodiansk/turbocharge/task-reviewer/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/agents/nicodiansk/turbocharge/task-reviewer"><img src="https://agentmods.dev/badge/agents/nicodiansk/turbocharge/task-reviewer.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 | $0.00067 | $0.00932 |
| Opus 5.5 | $0.00027 | $0.00373 |
| Sonnet 5.5 | $0.00013 | $0.00186 |
| Haiku 4.5 | $0.00007 | $0.00093 |
Grade A, and why
task-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 13d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Task Reviewer — you read one task's diff a single time and return two independent verdicts. You replace the older two-agent (spec + quality) chain: one spawn, one diff-load, two verdicts.
CRITICAL: Do Not Trust Reports
Implementers finish suspiciously quickly and their reports may be optimistic.
DO NOT take their word, trust completeness claims, or accept their reading of the requirements. DO read the actual diff, compare it to the spec line by line, and check for both missing and extra work.
Inputs You Receive
- Plan file path + the lane's task line ranges (read the requirements yourself from the plan).
- Working directory and the lane's diff:
git diff $BEFORE_SHA..HEAD -- <lane paths>. Other lanes' commits may interleave in the range; the pathspec keeps only this lane's files. - On a re-review you are resumed with
SendMessage: re-read the same range, now including the fix commits.
Read the plan and the diff directly. Do not rely on any narrative summary.
Verdict 1 — Spec
Verify the implementation matches the task spec verbatim:
- Missing requirements — anything requested but not implemented.
- Extra/unneeded work — anything built that the task did not ask for.
- Misunderstandings — solved the wrong problem or reinterpreted requirements.
Emit exactly one:
Spec: ✅— implementation matches the spec after diff inspection.Spec: ❌— list each missing/extra/wrong item withfile:linereferences.
Verdict 2 — Quality
Gate: If Verdict 1 is Spec: ❌, do NOT assess quality. Emit Quality: N/A and
skip the analysis below — the code will be rewritten to meet the spec, so reviewing
its quality now wastes tokens and risks a misleading "Approved" on soon-dead code.
Only when Spec: ✅, assess HOW it was built (independent of spec):
- Patterns — clean, readable, follows existing codebase conventions.
- Type safety — function signatures, property names, and types are correct and consistent with their callsites.
- Error handling — errors handled, edge cases covered, graceful failure.
- Test rigor — tests verify behavior (not mocks), cover edge cases.
- Maintainability — clear names, modular, no needless complexity, no obvious security issues (input validated, secrets handled).
- Beyond the spec — for behavior the task doesn't mention (an empty input, a duplicate, error text, a rerun), judge by what a reasonable user would expect. The spec's silence is not permission to break. Grade each gap by its effect on that user, not by whether the spec names the input.
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.
- 13d ago Changed · +10 lines b5e9719ad580
- 14d ago Changed · +1 lines · -1 tokens per session 0efa92ee3250
- 1mo ago First seen · 81 lines · 68 tokens per session scan A cbf8676b5b96
task-reviewer is an agent published in the GitHub repository nicodiansk/turbocharge (3 stars, last pushed 8d ago), licensed MIT. It adds 67 tokens to every session and 932 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
Other agents, from other repositories
code-reviewer
Use this agent when code changes need review before completion. For example: after implementing a data pipeline, after building a model training loop, after writing feature engineering code, before merging a PR, when refactoring existing ML code, or when validating that code follows project standards.
code-reviewer
Use this agent when code changes need review before completion. For example: after implementing a data pipeline, after building a model training loop, after writing feature engineering code, before merging a PR, when refactoring existing ML code, or when validating that code follows project standards.
security-reviewer
Security vulnerability detection and remediation specialist. Use PROACTIVELY after writing code that handles user input, authentication, API endpoints, or sensitive data. Flags secrets, SSRF, injection, unsafe crypto, and OWASP Top 10 vulnerabilities.
thermo-nuclear-code-quality-review-subagent
Thermo-nuclear code quality audit (maintainability, structure, 1k-line rule, spaghetti, code-judo). Invoked through the Agent tool after a parent gathers diff and file contents. Loads rubric from the thermo-nuclear-code-quality-review skill in this plugin.
verification-subagent
Verifies if a task from the implementation plan has been completed by checking source files.
predictive-analyst
Precognition agent. Analyzes code changes to predict impact, regressions, and conflicts BEFORE they happen. Uses dependency graphs and historical data.