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Grok Bot+18 agentsQ&A Prep: Hard Questions Answered Before They're Asked
They go where the material is exposed: a headline stronger than its estimate, a test on the wrong unit, a sample nobody explained, a choice that invites an alternative explanation.
$5
Q&A Prep: Hard Questions Answered Before They're Asked
Example session with this skill installed
I'm giving a 12-minute talk on this study at a regional agroecology conference next week, with 5 minutes of questions. The session chair is a pollination ecologist and the room will be mostly ecologists, plus some agronomists and farm advisers. My draft paper is paper.md, my slide outline is talk-outline.md, and the count data are in data/. Can you prepare me for the Q&A? I want to know what the hardest questions will be and have answers ready that I can actually defend.
- Read your context and instructions
- Compiled the q&a prep
- Generated the data export
I've written the Q&A prep sheet. The hardest questions don't come from your data. They come from four lines on your slides that claim more than the results show, and fixing those before the talk removes most of the risk. Your counts are sound: recomputing from data/visits.csv gives exactly the paper's 8.2 vs 5.1, the per-farm ratios, and 1.6× (95% CI 1.2–2.1).
To predict the questions, four separate readers saw only your paper, slides and data: a pollination ecologist acting as chair, a field statistician, an agronomist and a farm adviser.
The three most dangerous questions, with answers you can defend:
1.
"You say 'doubled', but your own estimate is 1.6×. Why?" All four readers raised this. Concede it immediately: "Doubled overstates it. It's 1.6 times, 95% CI 1.2 to 2.1, and higher on all eight farms."
2. "Your test counts 160 counts as independent, but your replicate is the farm." Agree. The 1.6× and its interval are already calculated across farms; only the p-value on slide 6 uses the wrong unit. Rerun the test with farm as the unit before quoting it.
3. "Which crop got the 6% yield gain, and does it even need insect pollination?" Nothing in your material names the crop. Say plainly that this is your weakest number: three farmers who chose to weigh separately, with no spread or test.
To fix before the talk (suggested wording is in the sheet; I haven't edited your slides or paper):
- "Doubled": change it to "1.6 times" in the title and on slides 1 and 6.
- The test: rerun it with farm as the unit (or a mixed model) and replace the n = 160 test on slide 6.
The ninth farm: the abstract says nine farms, the results analyse eight, and nothing explains the drop. Only you know the reason, so add one line to slide 4.
- Yield: name the crop, then soften slide 8 to "indicative" or move it to a backup slide.
Walk order: every strip and control pair in the data has the same start time. Check your field notes for whether one person walked both margins, and which came first.
Concede these when asked: farmers chose where to put the strips; there are no counts from before sowing; flowers in the margins weren't measured.
The sheet also has 18 ranked questions, 11 drafted answers, four backup slides built only from your existing material, scripts for the awkward moments and a card to glance at during the Q&A. The number check passes.
visits.csv
CSV · data export
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
About this skill
What it does
The questions after a research talk aren't random. They go where the material is exposed: a headline stronger than its estimate, a test on the wrong unit, a sample nobody explained, a choice that invites an alternative explanation. You can't see those spots in your own work, because you know what you meant. This skill makes your agent find them first, from your paper, slides, poster or results, and prepare answers you can defend in the room.
- Every claim against its evidence. Each claim a listener could challenge is listed with its number, n, uncertainty and location, and marked supported, stretched or unsupported. Numbers that differ between abstract, slides and paper are caught.
- Questions predicted, then ranked. From the exposed points, a list of research question angles (design, alternative explanations, robustness, scope, prior work, definitions, practical value) and the questions your setting brings, plus fresh questioner subagents (say, the chair, a statistician, a practitioner) that see only your material. Fifteen or more questions (ten for a poster session), ranked by how likely and how damaging they are.
- Answers that stay inside your material. The top ten and every damaging one, each leading with the direct answer, then the evidence, then the limit you concede before the questioner does, with a Sources line for every fact. When an answer needs something your material doesn't contain, it gets a [GAP] note for you, never an invented number or citation.
- A number check that runs. A bundled script finds every number in the answers in your material, flags any it can't, and verifies declared derivations (
derived: 3.1 = 8.2 - 5.1). - What to fix before you walk on. Gaps sorted into fix-before-the-talk (often a slide line that overclaims) and acknowledge-in-the-answer, three to five backup slides built only from material you have, scripts for hard moments (a question that hits a real flaw, one you can't answer, a hostile questioner) and a one-page lectern card.
- A mock Q&A, if you want one. One question at a time; feedback on whether you answered first, stayed in time, kept to the evidence and conceded cleanly.
What a run looks like
In our demo the agent got a made-up draft paper, slide outline and count data for a field study of wildflower strips, with six weak spots planted. It found all six: "doubled" against an estimate of 1.6, a test on 160 counts when the farm is the unit, a ninth farm that vanished, a yield claim from three farms, farmer-chosen placement, and one season of dry mornings. It also caught one we hadn't planned (strip and control logged with identical start times). It recomputed the numbers from the raw data and put farm-level statistics the paper doesn't report inside [GAP] notes for the speaker to confirm.
Settings
Conference talks, poster sessions, thesis defences and vivas, job talks, grant panel interviews, lab meetings and progress reviews, and written reviewer or committee Q&A.
What's included
SKILL.md: the nine-step method and its integrity rulesreferences/: question angles and setting-specific questions, questioner personas and prompt, hard moments, the prep sheet formatscripts/check_numbers.py: the number check (Python standard library)examples/: the demo's material and the prep sheet it produced
Part of the Aurora Lab research pipeline
Works on its own, on any talk. Pairs well with Aurora Lab's Poster Layout and other talk skills; needs none of them.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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Requires
Python