# I'm John Horton - i'm the creator of Expected Parr... ## Conversation ## User *2026-09-14T23:38:43.801076+00:00* I'm John Horton - i'm the creator of Expected Parrot and this research agent. I'd like to write some blog posts showing your capabilities. I'd like to do the following: take stock of your capabilities and then brainstorm a bunch of topics. Then create a bunch of potential Expected Parrot customers / users (www.expectedparrot.com) and have them rank order or rate blog post topics on things like how likely they would be to read it, how useful it would be to them and so on. Then analyze the results and give me suggestions. --- ## Assistant *2026-09-14T23:39:11.070605+00:00* Great project, John. A few things I want to get right before we brainstorm: **Tool calls:** - `Skill` - `Bash` - `command`: ep study start --root . --topic "blog-post-prioritization" --- ## User *2026-09-14T23:39:25.425287+00:00* Q: Who are you primarily trying to attract with these blog posts? A: Potential new users Q: What kind of blog posts are you envisioning? A: Mix of all three --- ## Assistant *2026-09-14T23:39:35.259589+00:00* Good — targeting new users, with a variety of formats. Let me take stock of what we can showcase, then brainstorm topics. I'll pull in what EP positions publicly to make sure the topics connect to how potential users would discover you. **Tool calls:** - `mcp__firecrawl__firecrawl_scrape` - `url`: https://www.expectedparrot.com - `Bash` - `command`: bash ~/.claude/list_study_types.sh 2>/dev/null | head -80 Alright, I've got a good picture of what EP does publicly and the full research capability set under the hood. Let me share my brainstorm and then we can refine before moving to the survey phase. Here's what I'm working with — the platform has a remarkable breadth that most potential users probably don't realize: --- **Worked Examples** (show, don't tell) 1. **"We pretested our survey before fielding it to humans — here's what we caught"** — Use Messick to cognitively pretest a survey, find confusing wording, fix it, then show improved data quality 2. **"Which blog post should we write? We asked 50 AI personas to decide"** — Meta: this exact project, eat-your-own-cooking 3. **"Pricing our SaaS tiers with synthetic customers"** — Price tiering study with market-weighted personas evaluating willingness to pay 4. **"We ran a premortem on our product launch with 30 stakeholders"** — Premortem analysis catching failure modes before launch 5. **"Conjoint analysis in an afternoon: what features matter most?"** — Full conjoint study on a relatable product 6. **"Testing 6 homepage headlines with 100 synthetic visitors"** — Message testing A/B with statistical analysis 7. **"We UX-tested our competitor's website with AI"** — Synthetic UX testing with behavioral browser journeys 8. **"Building digital twins from our last customer survey"** — Creating a reusable synthetic panel from real data 9. **"From 200 interview transcripts to a codebook in 2 hours"** — Qualitative data analysis at scale with Bewley 10. **"Is our brand showing up in ChatGPT? An AI visibility audit"** — Brand monitoring across AI answer engines 11. **"Scenario planning for an uncertain market: 4 futures for [industry]"** — Kahn scenario planning with early-warning signals 12. **"Rating every slide in our investor deck with brutal honesty"** — Slide feedback from diverse AI reviewers 13. **"Migrating our 80-question Qualtrics survey to EP in 15 minutes"** — Survey migration with Zugunruhe **Capability Explainers** 14. **"What is a research agent, and why should you care?"** — Intro to the concept of an AI that designs and runs studies 15. **"AI interviews that actually follow up: adaptive conversations at scale"** — QuestionInterview deep dive 16. **"Synthetic respondents 101: when (and when not) to trust AI survey answers"** — Honest treatment of validity 17. **"One survey, two audiences: piloting with AI, fielding with humans"** — The AI→human pipeline 18. **"How EP handles 12 LLMs so you don't have to"** — Model selection, cost, reproducibility 19. **"Structured decision-making with MCDA: beyond gut instinct"** — Decision support explainer 20. **"From Zwicky morphological box to product shortlist"** — Product design space exploration **Thought Leadership / Industry** 21. **"The $50 pilot: why every human survey should start with a simulation"** — Cost/quality argument for synthetic pretesting 22. **"Silicon sampling: what social scientists are learning from AI respondents"** — Academic perspective on synthetic data 23. **"The end of the blank-page survey: designing instruments with AI"** — How AI changes survey methodology 24. **"Qualitative research doesn't scale — until now"** — Argument for AI-assisted coding and theming 25. **"Why your pricing research is probably wrong (and how to fix it)"** — Problems with traditional pricing research --- Before I formalize this into a study plan, a couple of questions: --- ## User *2026-09-14T23:43:31.776814+00:00* Q: How does this initial topic list feel? Should I add, remove, or adjust the emphasis on any area? A: Looks great, use these Q: For the synthetic customer panel, how broad should we go with persona types? A: Broad — all plausible user types --- ## Assistant *2026-09-14T23:43:57.627527+00:00* Excellent — 25 topics, broad persona panel. Let me draft the study plan. **Tool calls:** - `Skill` - `Skill` - `Bash` - `command`: ep models --service openai --search mini --text --sort input-price --limit 10 2>/dev/null Good — `gpt-4.1-mini` is a strong Tier 2 model at $0.40/$1.60 per 1M tokens, well-suited for structured persona ratings at this scale. Now let me write the plan. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/plan.md - `Bash` - `command`: ep present sessions/topic_blog-post-prioritization/study_a/plan.md 2>/dev/null | head -5 --- ## User *2026-09-14T23:45:50.381637+00:00* Q: The plan is at study_a/plan.md. 20 personas rate all 25 topics on read-likelihood, usefulness, and conversion potential. Estimated cost ~$0.50-$1.00. Any changes, or good to go? A: Adjust something --- ## Assistant *2026-09-14T23:45:52.713589+00:00* What would you like to adjust? Topics, personas, questions, the model, or something else? --- ## User *2026-09-14T23:46:25.410969+00:00* let's do a pilot - I worry about agents being too positive. We might also consider some kind of ranking exercise where we use elo? --- ## Assistant *2026-09-14T23:47:01.912897+00:00* Both great instincts. Positivity bias is the single most common problem with LLM agents on rating scales — everything tends to cluster at 4-5 out of 5, and you lose the ability to differentiate. Here's what I'd suggest: **run a small rating pilot right now to see the problem empirically, then pivot to pairwise comparisons with Elo scoring for the full study.** The pilot costs ~$0.05 and gives us concrete evidence for why the Elo approach is better — which is itself good blog material for topic #2 ("we asked 50 AI personas to decide"). For the **Elo approach**, the design would be: - Present **pairs** of blog topics: "Which of these two posts would you be more likely to read?" - 25 topics = 300 possible pairs. Each agent sees all 300 (feasible with AI, takes ~2 min per agent). - Compute Elo ratings from win/loss records across all agents - We can also do **dimension-specific pairwise comparisons** — "which is more useful for your work?" and "which would make you more likely to try the platform?" — so we get Elo rankings on multiple dimensions - Forced choice eliminates ceiling effects entirely The pilot would be: 5 agents, 8 topics, Likert ratings. We look at the score distribution, confirm the compression, then I'll build the Elo version. --- ## User *2026-09-14T23:49:11.076033+00:00* Q: Sound like a good approach? Run a quick Likert pilot to see the positivity problem, then build the Elo pairwise study? A: Do both in the pilot --- ## Assistant *2026-09-14T23:49:28.387984+00:00* Great call — comparing both approaches head-to-head on the same subset is the most informative pilot. Let me update the plan and build this. **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/plan.md - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/plan.md --- ## User *2026-09-14T23:50:57.176753+00:00* Q: Updated plan: 5 agents evaluate 8 topics via both Likert ratings AND pairwise comparisons. We compare signal quality, then choose the better method for the full 25-topic study. Pilot cost ~$0.10. Good to run? A: Run the pilot (~$0.10) --- ## Assistant *2026-09-14T23:51:34.369945+00:00* Plan approved. Let me set up the workflow and build the pilot. **Tool calls:** - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && ep workflow setup --name "blog-post-prioritization" --root . --json '{"gates":[{"name":"plan... - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && ep workflow gate attest plan-approved --root . --by user --evidence "Plan approved after ite... Now let me build and run the pilot. I'll create a single script that runs both surveys. **Tool calls:** - `Bash` - `command`: ls /home/user/sessions/topic_blog-post-prioritization/study_a/ - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/pilot_run.py - `Bash` - `command`: mkdir -p /home/user/sessions/topic_blog-post-prioritization/study_a/data - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python pilot_run.py 2>&1 Both pilots ran successfully — 40 rating rows and 140 pairwise rows. Now let me analyze them side by side. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/pilot_analysis.py - `Bash` **failed** - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python pilot_analysis.py 2>&1 ```` Exit code 1 ====================================================================== SURVEY A: LIKERT RATINGS ====================================================================== ❌ E[🦃]EDSL ERROR: ResultsColumnNotFoundError: Column 'agent.name' not found in data. Exception raised when attempting to access a non-existent column. This exception occurs when trying to access, filter, or perform operations on a column that doesn't exist in the Results object. To fix this error: 1. Check for typos in the column name 2. Verify the column exists using results.columns() or results.df.columns 3. If the column is dynamic, ensure it has been created with mutate() first The error message typically includes suggestions for similar column names that do exist, which can help identify typos. Examples: ```python results.table(keys=["non_existent_column"]) # Raises ResultsColumnNotFoundError results.select("typo_in_column_name") # Raises ResultsColumnNotFoundError ``` For more information, see: https://docs.expectedparrot.com/en/latest/results#selecting-columns ```` - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python -c " from edsl import Results r = Results.load('data/pilot_ratings.json') for c in so... - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python -c " from edsl import Results r = Results.load('data/pilot_ratings.json') for c in so... - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python -c " from edsl import Results r = Results.load('data/pilot_ratings.json') for c in so... - `Edit` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/pilot_analysis.py - `Edit` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/pilot_analysis.py - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python pilot_analysis.py 2>&1 This is really clear. Let me show you what we found: --- **Your instinct was right — the ratings are compressed at the ceiling.** Read likelihood has 87.5% of responses at 4 or 5 (mean 4.33). The composite scores range only from 3.20 to 4.47 — a spread of just 1.27 on a 5-point scale. **The Elo pairwise approach produces dramatically better differentiation:** - "$50 pilot" wins 80% of matchups; "headline testing" wins only 17% - Elo spread of 373 points vs. 1.27 composite points - The forced choice eliminates the "everything is great" problem entirely **And the per-agent rankings are genuinely interesting — the personas behave differently in ways that make sense:** - The **UX researcher** ranked "Qualitative research doesn't scale" #1 (qualitative is their world) - The **product manager** ranked "pricing tiers" and "headline testing" much higher than others did (practical product concerns) - The **market researcher** put "pretesting our survey" near the top (that's their pain point) - The **consultant** ranked "What is a research agent?" #2 (they're the least familiar with the space) **Both methods agree** on the top topic ($50 pilot) and the bottom two (headline testing, pricing tiers), but the Elo method gives a much clearer ordering in the middle where it matters. **Recommendation for the full study:** Use pairwise Elo on all 25 topics with 20 agents. The main question is whether to run pairwise on all 3 dimensions (read, useful, convert) or focus on read likelihood and use a simpler method for conversion signal. --- ## User *2026-09-14T23:57:11.052454+00:00* Q: For the full 25-topic study, how many pairwise dimensions should we run? A: Read likelihood only (~$1) --- ## Assistant *2026-09-14T23:57:19.899865+00:00* Good choice — read Elo gives the clearest prioritization signal, and the top-5 selection adds conversion data cheaply. Let me build the full study. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/full_study.py Now, before running — this is the full study at ~$1-2 for 6,020 calls (300 pairs x 20 agents + 20 top-5 questions). **Tool calls:** - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python full_study.py 2>&1 --- ## User *2026-09-15T00:00:02.940371+00:00* Q: Ready to run the full study? 20 agents x 300 pairwise comparisons + 20 top-5 conversion picks. Estimated cost ~$1-2. A: Run it (~$1-2) --- ## Assistant *2026-09-15T00:01:28.417112+00:00* 6,020 responses collected. Now let me analyze everything and build the report. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/full_analysis.py - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python full_analysis.py 2>&1 These results are rich. Let me highlight the most interesting findings: --- **The big picture: "$50 pilot" vs. "Synthetic respondents 101"** There's a fascinating split between what people most want to *read* and what would most drive *sign-ups*: - **"The $50 pilot"** dominates readership Elo (1930, 89.6% win rate — it beat everything). But only 4/20 personas picked it for conversion. It's a great thought-leadership piece that builds authority but doesn't directly sell. - **"Synthetic respondents 101"** and **"One survey, two audiences"** are the combined winners — strong readership AND 70% conversion picks each. These explain *what EP actually does* in ways that make people want to try it. **The top 8 by combined score form a natural content sequence:** 1. Synthetic respondents 101 (the "what is this?" post) 2. One survey, two audiences (the "how does the workflow work?" post) 3. We pretested our survey (the worked example that proves it) 4. The $50 pilot (the thought leadership that builds credibility) 5. From 200 transcripts to a codebook (the "it does qualitative too" post) 6. Qualitative research doesn't scale (the thought leadership companion) 7. The end of the blank-page survey (survey design with AI) 8. AI interviews that follow up (the interviews capability) **Segment-specific surprises:** - **Consultants** ranked "Why your pricing research is wrong" #1 — nobody else had it that high. It's their pain point. - **UX researcher** ranked "Qualitative research doesn't scale" #1 — they live in the qual world. - **Product managers** gave "headline testing" rank #7 — much higher than any other segment (it was #19-24 everywhere else). They want tactical message testing. - **"Silicon sampling"** is a niche academic topic: #4 with academics, #24-25 with consultants and product people. **Hidden conversion gems**: "Survey migration" (#23 in Elo) got 30% conversion picks. Nobody wants to *read about* migration, but the people who need it will sign up for it. Same pattern with "conjoint analysis." Now let me write the formal report. Let me load the report authoring skill. **Tool calls:** - `Skill` - `Bash` **failed** - `command`: ls /home/user/sessions/topic_blog-post-prioritization/study_a/writeup/ 2>/dev/null; ls /home/user/sessions/topic_blog-post-prioritization/study_a/analysis/ 2... ``` Exit code 2 ``` - `Bash` - `command`: mkdir -p /home/user/sessions/topic_blog-post-prioritization/study_a/writeup /home/user/sessions/topic_blog-post-prioritization/study_a/analysis && ep study s... - `Bash` - `command`: ls /home/user/sessions/topic_blog-post-prioritization/study_a/writeup/ && ls /home/user/sessions/topic_blog-post-prioritization/study_a/analysis/ Good, scaffold is in place. Now let me create the plots and write the report. **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/analysis/plot_style.py Now let me create the plots and the report. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/analysis/make_plots.py - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python analysis/make_plots.py 2>&1 Let me inspect the plots to make sure they look good: **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/writeup/plots/elo_rankings.png - `Read` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/writeup/plots/segment_heatmap.png - `Read` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/writeup/plots/combined_score.png - `Read` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/writeup/plots/pilot_ratings_distribution.png All plots look clean and readable. Now let me write the report. **Tool calls:** - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/writeup/report.md Now, before compiling, I need to load the report quality review skill per the workflow. **Tool calls:** - `Skill` - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && katz report review-plan --report writeup/report.md 2>&1 --- ## User *2026-09-15T00:08:14.032890+00:00* Q: Optional pre-delivery quality review: 3 frontier models independently check the report for numerical consistency, evidence traceability, claim calibration, visual integrity, and recommendation scope. Costs ~$0.30-0.50, takes about 2 minutes. Run it or skip? A: Skip review --- ## Assistant *2026-09-15T00:08:17.086488+00:00* Skipping review. Let me compile the HTML report and finish up. **Tool calls:** - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a/writeup && pandoc report.md -o report.html --standalone --embed-resources --css=report.css 2>&1 - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && grep -q "\-\-ep-green" writeup/report.html && echo "CSS embedded OK" || echo "CSS missing" - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && ep report check --root . 2>&1 Report compiled and checked. Now let me close out the workflow gates and present it. **Tool calls:** - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && ep workflow gate attest pilot-results-valid --root . --by agent --evidence "Pilot: 40 rating... - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && ep workflow verify --root . 2>&1 All gates passed. Let me present the report. **Tool calls:** - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && ep present writeup/report.html 2>&1 Done. Here's what we built and what it tells you: --- **The study**: 20 AI personas representing your potential users (academics, market researchers, product managers, UX researchers, consultants, data analysts, and specialists) made 6,000 pairwise comparisons across 25 blog topics, producing Elo rankings. A separate conversion question identified which topics would drive sign-ups. **The recommended first 8 posts, in order:** | # | Topic | Why first | |---|-------|-----------| | 1 | **Synthetic respondents 101: when (and when not) to trust AI** | Foundational explainer. 70% conversion, universal appeal. Honest framing builds credibility. | | 2 | **One survey, two audiences: piloting with AI, fielding with humans** | Explains the core workflow. 70% conversion. Strongest with researchers. | | 3 | **We pretested our survey before fielding it to humans** | First worked example. 55% conversion. Shows what using EP looks like. | | 4 | **The $50 pilot** | #1 in pure readership (90% win rate!) but lower conversion. Thought leadership that builds authority. | | 5 | **From 200 transcripts to a codebook in 2 hours** | Broadens beyond surveys into qualitative. Strong with consultants and UX. | | 6 | **Qualitative research doesn't scale -- until now** | Thought leadership companion to #5. UX researchers' #1 topic. | | 7 | **The end of the blank-page survey** | Bridges to "AI helps you design surveys." | | 8 | **AI interviews that follow up** | Showcases a distinctive capability. | **Bonus insight**: "Migrating our Qualtrics survey to EP" ranked #23 editorially but got 30% conversion picks. Make it an SEO landing page, not a blog post. **Meta-insight for the blog itself**: This project is a natural candidate for blog post #2 or #3 on the list -- "Which blog post should we write? We asked AI personas to decide." It demonstrates the Elo approach, shows persona-specific variation, and is genuinely eat-your-own-cooking content. The irony that the personas ranked that meta-topic #24 is itself a fun finding to write about. --- ## User *2026-09-15T00:14:59.990817+00:00* can you add an appendix with the full list, rank ordered? The plots are a little hard to read --- ## Assistant *2026-09-15T00:15:37.236181+00:00* Updated. The appendix has the full rank-ordered table with all 25 topics showing combined score, Elo rating, win %, and conversion picks -- much easier to scan than the bar charts. The plots are still in the main body for the visual story (ceiling effects, segment heatmap), but the table is the definitive reference. **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/writeup/report.md - `Edit` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/writeup/report.md - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a/writeup && pandoc report.md -o report.html --standalone --embed-resources --css=report.css 2>&1... - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && ep present writeup/report.html 2>&1 --- ## User *2026-09-15T00:49:32.612860+00:00* Can we now make a human survey where people will say pick top 5 they'd like to read from the full list, perhaps as a question checkbox? --- ## Assistant *2026-09-15T00:49:41.693113+00:00* Absolutely — let's build a human-facing version. Let me load the deployment skill. **Tool calls:** - `Skill` - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/deploy_human_survey.py - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python -c " from edsl import Survey, QuestionCheckBox all_titles = [ 'Synthetic respondents... --- ## User *2026-09-15T00:50:35.621892+00:00* Q: The survey has one checkbox question: pick your top 5 from the full list of 25 titles, with a brief intro explaining what EP does. Want me to deploy it as an unlisted link you can share, or adjust anything first? A: let me preview it first --- ## Assistant *2026-09-15T00:50:47.609657+00:00* Here's the preview link -- you can walk through the survey without creating a live deployment: **https://www.expectedparrot.com/home/human-surveys/preview/56b60199-b68e-4e74-bf36-42b94913d559** Take a look and let me know if you want to change anything -- the intro text, the title wording, add a demographic/role question, randomize the option order, etc. Once you're happy with it I'll deploy it as a live unlisted link. --- ## User *2026-09-15T00:53:24.313749+00:00* not bad but these should probably be randomized not in LLM ranked order --- ## Assistant *2026-09-15T00:53:27.946392+00:00* Good catch — the current order is literally the AI ranking, which would bias human responses. Let me randomize and redeploy the preview. **Tool calls:** - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python -c " import random from edsl import Survey, QuestionCheckBox all_titles = [ 'Syntheti... Options are now randomized (seed 42). Updated preview: **https://www.expectedparrot.com/home/human-surveys/preview/56b60199-b68e-4e74-bf36-42b94913d559** The top AI-ranked topics ("Synthetic respondents 101", "$50 pilot") are now buried at positions 23-24 instead of leading. Let me also update the deploy script so when you're ready to go live it uses this shuffled order. **Tool calls:** - `Read` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/deploy_human_survey.py - `Write` - `file_path`: /home/user/sessions/topic_blog-post-prioritization/study_a/deploy_human_survey.py Ready to deploy when you've had a look at the preview. Want me to go ahead and make it live, or any other changes first? --- ## User *2026-09-15T00:54:37.268169+00:00* ok - give me a live url --- ## Assistant *2026-09-15T00:54:46.605503+00:00* It's live. Here are your links: **Share this with respondents:** https://www.expectedparrot.com/respond/human-surveys/e4029bcb-aed8-48e7-a8d0-a495787ae216 **Test it yourself (won't record a response):** https://www.expectedparrot.com/respond/human-surveys/e4029bcb-aed8-48e7-a8d0-a495787ae216?save_response=false **Admin dashboard (monitor responses):** https://www.expectedparrot.com/home/human-surveys/e4029bcb-aed8-48e7-a8d0-a495787ae216 Options are in randomized order. When you've collected enough responses, just let me know and I can pull the results and compare them against the AI persona rankings. **Tool calls:** - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python deploy_human_survey.py 2>&1 --- ## User *2026-09-15T00:55:34.472347+00:00* The survey has a "Select All" -that's not appropriate for a checkbox. Can you remove that? --- ## Assistant *2026-09-15T00:55:59.746988+00:00* Done — "Select All" is removed from the live survey. Same links still work: **Respondent link:** https://www.expectedparrot.com/respond/human-surveys/e4029bcb-aed8-48e7-a8d0-a495787ae216 You can verify with the test link (won't record a response): https://www.expectedparrot.com/respond/human-surveys/e4029bcb-aed8-48e7-a8d0-a495787ae216?save_response=false **Tool calls:** - `Bash` **failed** - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python -c " from edsl import Coop coop = Coop() coop.patch_human_survey_humanize_schema( 'e4... ```` Exit code 1 ❌ E[🦃]EDSL ERROR: CoopServerResponseError: Invalid humanize schema: 26 validation errors for HumanizeSchema questions.top5_blog_posts.FreeTextHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.BudgetHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.function-after[_unique_exclusive_options(), CheckboxHumanizeSchema].format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.function-after[_unique_exclusive_options(), CheckboxWithOtherHumanizeSchema].format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.ComputeHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.FileUploadHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.ImageGenerationHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.InterviewHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.LikertHumanizeSchema.format.type Input should be 'radio' or 'dropdown' [type=literal_error, input_value='checkbox', input_type=str] For further information visit https://errors.pydantic.dev/2.12/v/literal_error questions.top5_blog_posts.LikertHumanizeSchema.format.select_all Extra inputs are not permitted [type=extra_forbidden, input_value=False, input_type=bool] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.LinearScaleHumanizeSchema.format.type Input should be 'radio' or 'dropdown' [type=literal_error, input_value='checkbox', input_type=str] For further information visit https://errors.pydantic.dev/2.12/v/literal_error questions.top5_blog_posts.LinearScaleHumanizeSchema.format.select_all Extra inputs are not permitted [type=extra_forbidden, input_value=False, input_type=bool] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.ListHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.MatrixHumanizeSchema.format Input tag 'checkbox' found using 'type' does not match any of the expected tags: 'table', 'carousel' [type=union_tag_invalid, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/union_tag_invalid questions.top5_blog_posts.MultipleChoiceHumanizeSchema.format.type Input should be 'radio' or 'dropdown' [type=literal_error, input_value='checkbox', input_type=str] For further information visit https://errors.pydantic.dev/2.12/v/literal_error questions.top5_blog_posts.MultipleChoiceHumanizeSchema.format.select_all Extra inputs are not permitted [type=extra_forbidden, input_value=False, input_type=bool] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.MultipleChoiceWithOtherHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.NumericalHumanizeSchema.format.NumericalFormatInputSchema.type Input should be 'input' [type=literal_error, input_value='checkbox', input_type=str] For further information visit https://errors.pydantic.dev/2.12/v/literal_error questions.top5_blog_posts.NumericalHumanizeSchema.format.NumericalFormatInputSchema.select_all Extra inputs are not permitted [type=extra_forbidden, input_value=False, input_type=bool] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.NumericalHumanizeSchema.format.function-after[check_slider_bounds(), NumericalFormatSliderSchema].type Input should be 'slider' [type=literal_error, input_value='checkbox', input_type=str] For further information visit https://errors.pydantic.dev/2.12/v/literal_error questions.top5_blog_posts.NumericalHumanizeSchema.format.function-after[check_slider_bounds(), NumericalFormatSliderSchema].select_all Extra inputs are not permitted [type=extra_forbidden, input_value=False, input_type=bool] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.RankHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.TopKHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.YesNoHumanizeSchema.format.type Input should be 'radio' or 'dropdown' [type=literal_error, input_value='checkbox', input_type=str] For further information visit https://errors.pydantic.dev/2.12/v/literal_error questions.top5_blog_posts.YesNoHumanizeSchema.format.select_all Extra inputs are not permitted [type=extra_forbidden, input_value=False, input_type=bool] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden questions.top5_blog_posts.SurveyMessageHumanizeSchema.format Extra inputs are not permitted [type=extra_forbidden, input_value={'type': 'checkbox', 'select_all': False}, input_type=dict] For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden Exception raised when the server returns an error response. This exception is raised when the Expected Parrot API returns an error response, such as authentication failures, rate limits, or server errors. The exception message typically includes the error details from the server. To fix this error: 1. Check the exception message for specific error details from the server 2. For authentication errors (401), verify your API key is correct and not expired 3. For rate limit errors (429), reduce the frequency of your requests 4. For server errors (500+), the issue may be temporary - wait and try again 5. Check your network connection if you're getting connection timeout errors Example: ```python coop = Coop(api_key="invalid-key") coop.get("valid-uuid") # Raises CoopServerResponseError with 401 Unauthorized ``` For more information, see: https://docs.expectedparrot.com/en/latest/api_keys ```` - `Read` - `file_path`: /home/user/.claude/skills/edsl-human-survey/references/humanize-schema.md - `Grep` - `path`: /home/user/.claude/skills/edsl-human-survey, `pattern`: select.all|select_all|Select all - `Bash` - `command`: cd /home/user/sessions/topic_blog-post-prioritization/study_a && python -c " from edsl import Coop coop = Coop() coop.patch_human_survey_humanize_schema( 'e4... --- ## Files - [**report.html**](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/writeup/report.html) (1000.6 KB) - [Makefile](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/Makefile) (1.7 KB) - [deploy_human_survey.py](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/deploy_human_survey.py) (2.9 KB) - [full_analysis.py](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/full_analysis.py) (9.6 KB) - [full_study.py](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/full_study.py) (23.3 KB) - [pilot_analysis.py](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/pilot_analysis.py) (5.5 KB) - [pilot_run.py](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/pilot_run.py) (10.4 KB) - [plan.md](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/plan.md) (5.4 KB) - [build_standard_plots.py](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/analysis/build_standard_plots.py) (2.3 KB) - [make_plots.py](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/analysis/make_plots.py) (7.0 KB) - [plot_style.py](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/analysis/plot_style.py) (12.3 KB) - [analysis_results.json](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/data/analysis_results.json) (26.6 KB) - [full_pairwise.json](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/data/full_pairwise.json) (33.4 MB) - [full_top5.json](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/data/full_top5.json) (257.7 KB) - [pilot_pairwise.json](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/data/pilot_pairwise.json) (851.4 KB) - [pilot_ratings.json](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/data/pilot_ratings.json) (485.6 KB) - [ep_logo.pdf](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/writeup/ep_logo.pdf) (4.6 KB) - [fix-table-widths.lua](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/writeup/fix-table-widths.lua) (4.8 KB) - [report.css](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/writeup/report.css) (4.7 KB) - [report.md](https://www.expectedparrot.com/api/macaw/share/rzMe4hfqb8d75tbFwxCg7Q/files/sessions/topic_blog-post-prioritization/study_a/writeup/report.md) (11.5 KB) - 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