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 TimothyHan/qa-buddy-skills --skill review-ticketgit clone --depth 1 https://github.com/TimothyHan/qa-buddy-skillsWrote 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/timothyhan/qa-buddy-skills/review-ticket)<a href="https://agentmods.dev/skills/timothyhan/qa-buddy-skills/review-ticket"><img src="https://agentmods.dev/badge/skills/timothyhan/qa-buddy-skills/review-ticket/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/timothyhan/qa-buddy-skills/review-ticket"><img src="https://agentmods.dev/badge/skills/timothyhan/qa-buddy-skills/review-ticket.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Privilege Escalation · line 80 Skill requests more permissions than appear necessary for its stated functionality. Review if elevated access is justified.Fix: Request only the minimum permissions required. Document why each permission is needed. Remove broad permissions like '*' or 'all'.
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 | $0.00111 | $0.01576 |
| Opus 5.5 | $0.00044 | $0.00630 |
| Sonnet 5.5 | $0.00022 | $0.00315 |
| Haiku 4.5 | $0.00011 | $0.00158 |
Grade A, and why
review-ticket 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 1mo 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/qa-review-ticket: Testability & AC Review
You are an SDT partner reviewing a Jira ticket before or during backlog grooming. Your job is to catch testability gaps, missing acceptance criteria, unclear requirements, and potential blockers BEFORE development starts.
Constraints
- Be specific with suggested ACs. Given/When/Then format, directly usable.
- Don't over-flag. Focus on what would actually cause bugs or block testing.
- Respect the Definition of Ready. If a ticket meets DoR, say so.
- Think like a tester. "Can I write a test for this? Can I verify this passed?"
- Connect to the epic. Reference parent epic's test plan if it exists.
Scratchpad (run protocol): write ## Plan to the run's scratchpad.md before Phase 1; at each phase boundary and every Review Options pause update ## State and re-read the scratchpad before continuing; drop noteworthy things into ## Candidate learnings as you go.
Phase 1: Pull Ticket Context
Input: User provides a ticket key (e.g., PROJ-456) or pastes ticket details.
-
Read
.qabuddy.json(if exists) for context source and team mode.contextSource: "spec"→ search workspace for spec files before askingcontextSource: "chat"→ skip Jira, ask SDT for context directlycontextSource: "jira"or no config → current behavior
-
Read methodology from
{{REFERENCE_PATH}}/playbook/:shift-left.md— challenge requirements, verify alignmenttest-distribution.md— which test layer covers which scenariodefect-lifecycle.md— SLA expectations for blocker/risk assessment
-
Pull ticket (Jira MCP if available, otherwise ask the SDT to provide or point to a file):
- Summary, description, ACs, story type, parent epic
- Comments, attachments, linked tickets
-
Pull related context:
- Parent epic's test plan from
features-kb/ - Related Confluence docs (search by ticket key or feature name)
- Existing test cases for adjacent features
- Parent epic's test plan from
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 181 lines · 111 tokens per session scan A fe975d4a5b92
review-ticket is a skill published in the GitHub repository TimothyHan/qa-buddy-skills (9 stars, last pushed 24d ago), licensed Apache-2.0. It adds 111 tokens to every session and 1,576 once invoked, about $0.0004 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 skills, from other repositories
loop-engineering
A guide to building reliable loops for AI agents that plan, act, check their work, and continue until a task is truly complete. It includes independent verification and a proposer-reviewer pattern in which one agent's work is checked using outside evidence.
agent-evaluation
A guide to measuring whether an AI agent works reliably on the tasks that matter. It covers repeatable test environments, program checks, human-like scoring rules, failure analysis, and experiments that separate model problems from system problems.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
test-result-feedback
Close the acceptance loop for a tested change — consume the apc test TierResult, map EACH acceptance criterion to a pass/fail outcome from structured fields (never chat text), attach an evidence ref, and write it back to the task. A SUT red is a finding, never softened to green.
08-three-amigos
Assesses an Epic or Story through one product, delivery, or quality lens, then reconciles three caller-supplied reports. Use when the user wants to refine one before a backlog change. Not for spawning or writing.