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 genudo-ai/genudo_mcp --skill plan-qa-sprintgit clone --depth 1 https://github.com/genudo-ai/genudo_mcpWrote 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/genudo-ai/genudo_mcp/plan-qa-sprint)<a href="https://agentmods.dev/skills/genudo-ai/genudo_mcp/plan-qa-sprint"><img src="https://agentmods.dev/badge/skills/genudo-ai/genudo_mcp/plan-qa-sprint/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/genudo-ai/genudo_mcp/plan-qa-sprint"><img src="https://agentmods.dev/badge/skills/genudo-ai/genudo_mcp/plan-qa-sprint.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 | $0.00086 | $0.00638 |
| Opus 5.5 | $0.00034 | $0.00255 |
| Sonnet 5.5 | $0.00017 | $0.00128 |
| Haiku 4.5 | $0.00009 | $0.00064 |
Grade A, and why
plan-qa-sprint 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 6d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan QA Sprint
Convert findings into tracked, testable work. Every story closes on a test, not an opinion.
1. Collect the inputs
- Failed steps from the latest run (
run-agent-tests/evaluate-agent-replies→results.json). - Gaps from
mine-customer-chatsreports. - Change requests from the user or client.
- Remediations already applied and verified (
remediate-agent-defectslog) → statusDone.
Merge duplicates: several failed steps with one root cause become one story.
2. Write the stories
One object per story:
| Field | Rule |
|---|---|
id |
BUG-nn for defects, US-nn for new behaviour |
priority |
P0 blocks go-live (sales path, orders, payments, safety, high-volume intent); P1 important; P2 polish |
category |
e.g. Order journey, Objection handling, Guardrail, Integration, Knowledge |
story |
"As a , I want so that " |
component |
Global instructions, Stage instruction, Enter condition, Knowledge table, Action/webhook, Platform |
points |
Fibonacci only: 1, 2, 3, 5, 8, 13 — split anything bigger |
acceptance |
Observable and testable; name the assertion |
linked_test |
Case ID(s) that prove it (J04), or NEW if a journey must be written first |
status |
To Do, In Progress, Verified, Done |
Keep client data out of the stories: no customer names, phone numbers, or account numbers.
3. Allocate and build the sheet
Save the stories as stories.json and run the bundled script from this skill's directory:
python3 build_backlog.py stories.json -o sprint_backlog.xlsx --capacity 35
It places every P0 in Sprint 1 (even past capacity — it flags the overflow on the Summary
tab), then fills P1 and P2 first-fit into later sprints. Needs openpyxl
(pip install openpyxl). python3 build_backlog.py --self-check verifies the allocator.
4. Review with the user
Show the Summary tab numbers, call out any over-capacity sprint, and list stories marked
NEW that need a journey before they can be verified (generate-test-journeys).
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.
- 6d ago First seen · 54 lines · 86 tokens per session scan A 2825f16a2a92
plan-qa-sprint is a skill published in the GitHub repository genudo-ai/genudo_mcp (0 stars, last pushed 6d ago), licensed MIT. It adds 86 tokens to every session and 638 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 2026-10-11.
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