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 makifbaysal/tasktrooper --skill root-cause-reviewgit clone --depth 1 https://github.com/makifbaysal/tasktrooperWrote 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/makifbaysal/tasktrooper/root-cause-review)<a href="https://agentmods.dev/skills/makifbaysal/tasktrooper/root-cause-review"><img src="https://agentmods.dev/badge/skills/makifbaysal/tasktrooper/root-cause-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/skills/makifbaysal/tasktrooper/root-cause-review"><img src="https://agentmods.dev/badge/skills/makifbaysal/tasktrooper/root-cause-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 | $0.00034 | $0.00808 |
| Opus 5.5 | $0.00014 | $0.00323 |
| Sonnet 5.5 | $0.00007 | $0.00162 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
root-cause-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 8d 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Root-Cause Review
Overview
When a task returns to code_review after a need_revision cycle, the question is not "did something change" but "was the ROOT CAUSE addressed". Symptom patches come back as bugs; catching them here is cheaper than in QA or production.
The Process
- Find the prior feedback first. A re-submission enters code_review from
in_progress, so the earlier need_revision comment is NOT injected into this run automatically —list_task_commentson this task is how you find it (also checkget_task_pull_requestfor human reviewer comments on the PR itself). - Re-read the prior feedback: your previous need_revision comment, the QA failure report, or the pipeline failure — list every numbered point.
- Map each point to the new diff: for every point, find the exact change that addresses it. A point with no matching change means the revision is incomplete → back to need_revision citing the unaddressed point.
- Judge root cause vs symptom for each fix:
- Symptom patch: special-casing the failing input, catching-and-ignoring the error, widening a timeout, deleting the failing assertion.
- Root-cause fix: the change explains WHY the failure happened and removes that mechanism at its source.
- Check the guard test: a test now exists that reproduces the original failure and passes with the fix. No guard test → Important finding.
- Watch for regressions: revision diffs are written under pressure — re-check the surrounding code paths the fix touches.
Worked Example (symptom vs root cause)
Prior finding: "export returns empty CSV on DB error (swallowed error)."
- Resubmission A (symptom patch): the diff wraps the export call in the handler with
if len(csv)==0 { return 500 }. Rejected → back to need_revision: "Point 1: the swallowed error at service.go:24 is still there; you inferred failure from an empty result instead of propagating the error. An empty project also returns empty CSV, so this now 500s a valid empty project. Fix the source." - Resubmission B (root cause):
ListByProject's error is propagated and mapped to 500; a test injects a repo error and asserts 500; the empty-project test still asserts 200 + header-only CSV. Approved → ready_for_qa.
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.
- 8d ago First seen · 43 lines · 34 tokens per session scan A ceea41b53008
root-cause-review is a skill published in the GitHub repository makifbaysal/tasktrooper (113 stars, last pushed yesterday), licensed Apache-2.0. It adds 34 tokens to every session and 808 once invoked, about $0.0001 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 2026-10-05.
Other skills, from other repositories
code-review
Use this skill to systematically review code changes for correctness, security, performance, maintainability, and test coverage. Activates after implementation, before merging, or when asked to review a pull request or code change. Produces a structured review report with findings categorized by severity.
github-pr-workflow
Prepare a GitHub pull request from a feature branch — branch hygiene, commit shape, title/body, verification notes, screenshots for UI work, and replies to review comments.
Verification & Quality Assurance
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
agent-code-review-swarm
Agent skill for code-review-swarm - invoke with $agent-code-review-swarm.
agent-swarm-pr
Agent skill for swarm-pr - invoke with $agent-swarm-pr.
agent-reviewer
Agent skill for reviewer - invoke with $agent-reviewer.