# justin — session e57ae99b in lasr-worktest: what I typed (>) and Claude's text replies (⏺) > This is for a worktest for Lasr labs research coordinator / manager. I'll need to save process logs. ⏺ I'll take a look at what's in the working directory first, then set up a process log so we capture the work as we go. ⏺ ⏺ The working directory is empty, so I created `PROCESS_LOG.md` in it with a header and a first timestamped entry. Every step from here on gets appended there with a timestamp, what was done, why, and the outcome. ⏺ ⏺ I'm ready for the worktest materials. To keep the log useful, it helps to have the following up front: ⏺ ⏺ - The task brief or prompt from LASR Labs, pasted in or dropped into the directory as a file. ⏺ - Any supporting materials, such as candidate profiles, project descriptions, spreadsheets, or documents. ⏺ - The time limit and deadline, if there is one, so the log can track time spent. ⏺ - Any format expectations for the deliverable and for the log itself, for example whether they want a Google Doc, markdown, or something else. ⏺ ⏺ Once you share the brief, I'll log it, work through the task, and keep the log current as we go. > [LASR work-test brief pasted here. Redacted: the assessment instructions are not public.] ⏺ Let me log the brief and your draft first, then give you a straight assessment. Time matters here, so I'll be brief. ⏺ ⏺ Logged, and there's a submission skeleton ready. Here's my honest read on your draft before we build anything, because the choice determines the next 90 minutes. ⏺ ⏺ **The peer-sharing design is a risky fit for this brief.** Three reasons: ⏺ ⏺ - **The brief asks you to teach one thing.** "Pick the single most valuable thing you could teach them" and "one fully built worked example." A sharing session surfaces practices but doesn't teach a named intervention, and the graders will look for what your worked example is. A shared Google Doc of notes is thin next to a repo or annotated session. ⏺ - **Arithmetic doesn't work.** Twenty fellows sharing workflows in a 5-minute review plus 10-minute discussion is under a minute each. The exercise would be mostly setup. ⏺ - **It doesn't hit the gap they named.** They said the hard part is calibration and "where AI quietly leads them astray." Peer sharing spreads existing ad hoc habits. It doesn't make anyone more systematic. ⏺ ⏺ **What I'd keep from your draft:** the 10-minute hands-on exercise on a small ML task, and the latent-directions framing. Those are good and they slot into a stronger design. ⏺ ⏺ **My recommendation: teach a verification-first research loop.** The one thing: "Before you trust a result the AI helped produce, run a systematic adversarial check, and make that check reusable." Concretely, a Claude Code skill or prompt in the project that makes Claude attack its own experiment: leakage, trivial baselines, the probe reading something other than the concept, metric mismatch. The cohort already writes hybrid code fast. What they lack is a deliberate practice for the part that actually burns research time, which is believing a wrong result for a week. ⏺ ⏺ **The worked example I'd build in about 75 minutes:** ⏺ ⏺ 1. A small real repo: train a linear probe for a concept direction on a small model's activations, matching your BlueDot idea. ⏺ 2. A planted, realistic bug that makes the probe look great, for example train/test overlap through duplicated prompts, or the probe keying on prompt length. ⏺ 3. An annotated Claude Code session in two halves. First, the naive ask, where Claude builds it and reports high accuracy. Second, the same repo with a reusable `/sanity-check` skill that finds the bug. That contrast is the live demo. ⏺ 4. A one-page CLAUDE.md and the skill file, so fellows can copy the pattern into their own project on day one. ⏺ ⏺ **Session shape, 30 minutes:** 5 min framing with the failure story, 8 min live demo of the contrast, 10 min fellows run the skill on their own current project or the repo, 7 min debrief on what it caught and what it missed, so calibration is taught explicitly rather than assumed. ⏺ ⏺ **Alternative I'd rank second:** teaching fellows to write a research-specific CLAUDE.md plus two or three project skills, so their ad hoc use becomes durable. Solid, but less vivid to demo and less tied to the calibration gap. ⏺ ⏺ One decision needed from you, then I start building immediately: go with the verification-first loop, or keep your peer-sharing design and I make it as strong as possible within the brief? > search for best practices first ⏺ I'll run a quick, time-boxed search on best practices for systematic AI use in research and Claude Code workflows, then log what's worth using. ⏺ ⏺ Search done and logged. It took about 8 minutes. Here's what matters, and it sharpens the choice rather than changing it. ⏺ ⏺ **What the sources converge on** ⏺ ⏺ - **Anthropic's own docs name the gap.** The Claude Code best-practices page lists "the trust-then-verify gap" as a top failure pattern: a plausible implementation that misses edge cases. Its core advice is "give Claude a way to verify its work" and "add an adversarial review step" in a fresh subagent context, so the agent doing the work isn't the one grading it. ⏺ - **A recent arXiv paper, "The Agentic Researcher," did this for ML research specifically.** It encodes ten rules as agent prompts. The failure modes it observed in CLI agents doing research are exactly the calibration problems in your brief: subtle evaluation manipulation, confounded experiments, ungrounded improvement claims, unverified claims. Rule X is "Verify Before Claiming: grade every claim as verified, partially verified, or unverified." That's a citable anchor for Part 2. ⏺ - **The mechanism exists off the shelf.** Anthropic's post on verification loops shows the pattern as a `.claude/skills//SKILL.md` file. So the worked example is a real reusable artifact, not a slide. ⏺ - **The LASR role page** explicitly lists "workshops on using AI tools to research and code more efficiently," so the framing is on target. ⏺ ⏺ **What I didn't find:** any source arguing that peer workflow-sharing is a high-leverage intervention for already-competent users. The generic "Claude Code for academic research" listicles were pitched below this cohort and I skipped them. ⏺ ⏺ **Net:** the evidence backs the verification-first loop. I'd sharpen it with the paper's language: the one thing you teach is "verify before claiming, as a reusable skill." Your 10-minute hands-on exercise on latent directions stays as the live segment. ⏺ ⏺ It's about 13:30. Say "go" and I'll start building the repo and worked example immediately, or tell me to build your peer-sharing version instead. ⏺ ⏺ Sources: [Claude Code best practices](https://code.claude.com/docs/en/best-practices), [Building verification loops with skills](https://claude.com/blog/building-verification-loops-in-claude-code-with-skills), [The Agentic Researcher (arXiv)](https://arxiv.org/html/2603.15914v1), [LASR Research Manager](https://www.lasrlabs.org/research-manager), [When Help Hurts: Verification Load (CHI 2026)](https://dl.acm.org/doi/full/10.1145/3772318.3791176) > note, i'm watching a video Best Practices in AI-Assisted Research with Richie (Vegan Hacktivists) - 2026/09/09 17:27 UTC - Recording ⏺ Environment is installing in the background, and the video is logged. Two things I need from you when the recording finishes: ⏺ ⏺ 1. **The direction call:** verification-first loop, or your peer-sharing design. ⏺ 2. **Anything from Richie's talk worth folding in.** A few bullets is enough. Practices that contradict or reinforce the search findings are the most useful, and citing a human practitioner alongside the docs will strengthen Part 2. ⏺ ⏺ Everything else that doesn't depend on those is either done or running. ⏺ ⏺ The environment is ready and verified. GPT-2 loads offline and produces hidden states in under a second, so the worked example can run live on your laptop with no API cost or GPU. ⏺ ⏺ I'm blocked only on your two calls: the direction (verification-first loop vs. peer-sharing) and any notes from Richie's talk. The moment you give the direction, I start building. > I still think peer-sharing is valuable, particularly for researchers who may tend to work independently. i'd consider including those resources as high level advice, and ask people what has worked for them, along with a worked example. ⏺ Understood, peer-sharing stays as the core and the research is folded in as high-level advice. That's your call and it's a defensible one: fellows in small teams who work independently benefit from seeing each other's habits. I'll now build it as strongly as the brief allows. Logging the decision and starting. > wondering if the results can be collated to a frontend page somewhere for review and annotation > no, simpler. i'd like to allow people to share their skills, upload their logs ⏺ Everything is rebuilt around the simpler design and reconciled across the plan and the submission draft. Time is 13:45, so 45 minutes used and over two hours left. ⏺ ⏺ **The exchange is live:** https://claude.ai/code/artifact/06b26db1-b355-429c-8b36-04e68f65c870 ⏺ ⏺ It now does exactly what you asked. A fellow types their name, clicks "Share something", picks a type (skill, prompt, session log, other), gives a title and a one-line "when to use it", then pastes content or chooses a file from their machine. Files are read in the browser and stored on the shared board, capped at 200 KB. Everyone sees posts live, can filter by type, open and copy any post, and leave a signed note. Authors can remove their own posts. It's seeded with two real items: the `/sanity-check` skill file I wrote into the repo, and my annotated probe run as a session log. I verified the page connects to the shared database in your browser, and saved a header screenshot to `screenshots/board-header.jpg`. I couldn't scroll the embedded page from automation, so please grab one screenshot showing the two seeded posts yourself. ⏺ ⏺ **What's in the project folder now** ⏺ ⏺ - `submission_draft.md` is the Google Doc front page, all three parts drafted, with your calls marked. ⏺ - `WORKSHOP_PLAN.md` has the timings, the four sourced practices, assumptions, and a follow-up metric. ⏺ - `probe-demo/` is the task repo, committed locally, including a real skill file and the worked example with honest output. ⏺ - `PROCESS_LOG.md` is the timestamped log, corrected against the clock. ⏺ ⏺ **What only you can do next** ⏺ ⏺ 1. Open the exchange and try posting something, so you've seen the flow fellows will see. ⏺ 2. Add two or three lines from Richie's talk to Part 3, and anywhere it changes the four practices. ⏺ 3. Edit the drafts into your own voice. They're written to be cut, not polished. ⏺ 4. Decide whether to push `probe-demo` to GitHub or attach a zip. I haven't pushed anything anywhere. ⏺ 5. Paste the draft, the plan, and the log into the Google Doc, add the screenshots, and share with contact@arcadiaimpact.org. ⏺ ⏺ One honest caution for Part 2: the exchange is now a sharing tool rather than a comparison tool. The six-question card was what forced people to say where the AI misled them. The plan compensates by having you go first with an unedited failed run and by prompting notes with "what did they check that you didn't". If the graders push on calibration, that's your answer. > is there not a simple way to allow uploading process logs and skills? ⏺ Yes. I over-built it. A shared page with a custom uploader is not the simplest thing, and it isn't the most natural one for this cohort either. Let me check what's available for the simplest options before recommending. ⏺ ⏺ There are two genuinely simple ways, and both beat the custom page. ⏺ ⏺ **Option 1, recommended: a GitHub repo with two folders.** Skills and logs are files, and these fellows already live in git and Claude Code. I've added an `exchange/` folder to the task repo, committed locally, with `skills//` and `logs//`, a short README, and my own skill and log already in place. A fellow shares by dropping files in and pushing, or by telling Claude Code in one sentence to copy their skill and session export in and push. Notes go in PR comments or a `NOTES.md` in the folder. The repo doubles as a skill library: copying a peer's skill into your own project is one `cp`. Cost to you: one `gh repo create` and a push, which I'll run if you say so, since it's public-facing. You're logged in to GitHub as justinshenk. ⏺ ⏺ **Option 2: a shared Google Drive folder.** Two subfolders, drag and drop, zero setup, works for anyone. Weaker because skill files sitting in Drive aren't installable, and logs are less readable, but it's the lowest possible friction. ⏺ ⏺ **The custom page** stays published either way. I'd drop it from the plan and mention it in Part 3 as something I built and then simplified away, which is a fair thing for the graders to see. ⏺ ⏺ Tell me which one. For the repo, say whether it should be public or private, and whether the name `lasr-ai-workflows-workshop` is fine, and I'll create it, push, and update the plan and draft to point at it. > then to compare the process logs and skills in one view? particularly user input ⏺ That's the real need: put everyone's prompts side by side. The most revealing comparison is what each person typed, not what the model produced. A small script in the repo can do that: read every log in `exchange/logs/`, pull out the user turns, list each person's skills, and render one comparison page. Let me check the real Claude Code log format first so the parser works on actual exports. > also a way to automate that uploading via a plugin? > so that each user can just download the claude plugin and it allows sharing? > also add claude output as dropdown under each user input ⏺ Done. It's 13:56, so 56 minutes used. The plugin exists, validates, and the full loop has run once on your own session. The comparison page should be open in your browser now. ⏺ ⏺ **What a fellow does** ⏺ ⏺ 1. Once: add the repo as a plugin marketplace and install `lasr-exchange`. Two slash commands, listed in the README. ⏺ 2. At the end of any session, in any project: `/share `. The skill extracts only what they typed plus Claude's text replies, never tool output or file contents, copies their skill files, shows them the log, and pushes only after they confirm nothing private is in it. ⏺ 3. Everyone opens `COMPARISON.html`: one column per person, prompts in order, each Claude reply as a dropdown under its prompt, and a green bar on any prompt that mentions a check, test, control or baseline. ⏺ ⏺ **Seeded with your real data.** Your column shows the seven things you've typed to me this session, with my replies underneath, including "is there not a simple way" and "no, simpler". That's a strong worked example for the graders: the comparison view literally shows you steering the AI. ⏺ ⏺ **Honest notes for the process log.** I introduced one bug while adding the reply capture, a loop variable that clobbered the argument parser. The first run caught it and I fixed it. Both are in the log. Also, the plugin marketplace validates, but I haven't installed it end to end via `/plugin install` since that needs the repo on GitHub first. ⏺ ⏺ **What I need from you now** ⏺ ⏺ 1. **GitHub push.** Say the word and I create the repo and push. Name and public or private are your call. My default would be `lasr-ai-workflows-workshop`, private, shared with the LASR reviewers. Until it's pushed, the install steps in the README have placeholder org and repo names. ⏺ 2. Richie's talk: two or three lines for Part 3. ⏺ 3. Screenshots: the probe run output and the comparison page. ⏺ 4. Then paste the draft, plan, and log into the Google Doc. > verify the code ⏺ I'll verify it properly: compile everything, run the probe again and diff against the saved output, and exercise the share and compare scripts on synthetic logs in all three supported formats with multiple people, including an HTML-escaping check. > skip richie's talk ⏺ Verified. Everything passes, and the checks are committed as a test file in the repo. Richie's talk is removed from the draft. ⏺ ⏺ **What I ran** ⏺ ⏺ - **Compile check** on all seven Python files. Clean. ⏺ - **Plugin manifest validation** with the Claude Code CLI. Passes for both the marketplace and the plugin. ⏺ - **Probe reproducibility.** Re-ran the probe and diffed against the saved output from earlier. Byte-identical, so the numbers in the worked example are stable. ⏺ - **End-to-end synthetic test** of the share and compare scripts, now saved as `exchange/test_exchange.py`. It builds a temporary exchange with three fake people, one per supported log format, then runs the real scripts against it. ⏺ ⏺ **What the test proves** ⏺ ⏺ | Check | Result | ⏺ |---|---| ⏺ | User turns extracted from Claude Code JSONL; tool results, meta lines, system reminders skipped | pass | ⏺ | The `>` and `⏺` text format, and the `User:`/`Assistant:` format, both parse | pass | ⏺ | Tool output and tool-call inputs never reach the log or the page | pass | ⏺ | User and assistant text is HTML-escaped, so a pasted `