// Peripheral vision audit for empirical output. Finds what the author cannot see — problems hiding in plain sight (vices) and opportunities being overlooked (virtues). Inspired by Viktor Shklovsky's defamiliarization and conversations with Jason Fletcher. Use when output exists and interpretation is about to happen.
Warrant-first research GTD system. Manages the capture-clarify-organize-reflect-engage cycle for causal inference research. Scaffolds hypotheses/, insights/, decisions/ directories. Interrogates conjectures, files results, tracks binding decisions, checks pipeline freshness, drives the courtroom checklist.
Quick visual-collision check for figures — TikZ inside .tex files OR rendered .png/.jpg/.pdf figures from R/Python. Three checks only. (1) Bezier-curve label collisions via gap math. (2) Label-to-object whitespace. (3) Labels touching or running off the figure edge. These are the visual errors that compile cleanly — pdflatex never warns about them, ggsave never refuses to write them, so the agent that produced them does not know they are wrong. Use after generating any figure where visual correctness matters and you cannot eyeball it yourself.
Scaffold a new book project — folders, memoir-based LaTeX skeleton, custom style with voiced sidebars, bibliography stub, CLAUDE.md, README, and a chapter-per-file structure that converts cleanly to HTML later. Use at the start of a book project to get a compileable skeleton in one step. Parallel to /newproject and /bibcheck.
Many-agent bibliography audit. Verify each citation in a .bib file by spawning narrow-focus agents that confirm DOI/URL and cross-check that all fields belong to the same paper. Catches mixed-up entries (one paper's title with another's authors), wrong years, journal misattributions, and unverifiable references. Use when reviewing a manuscript's bibliography for accuracy before submission, after literature review, or when inheriting a .bib from a coauthor.
End-to-end beautiful Beamer deck creation. Designs an original Beamer theme tailored to a specific audience, restructures existing content via the Rhetoric of Decks (ethos / pathos / logos), generates figures and tables from R/Python/Stata code first, embeds code blocks in the deck, produces standalone walkthrough scripts, compiles to zero warnings, runs /tikz for visual collision cleanup, and dispatches a graphics-only audit agent for label and coordinate checks. Use when creating a presentation from scratch or restructuring existing content into a new beautiful deck.
Download, split, and deeply read academic PDFs. Use when asked to read, review, or summarize an academic paper. Splits PDFs into 4-page chunks, reads them in small batches, and produces structured reading notes — avoiding context window crashes and shallow comprehension.
| name | blindspot |
| description | Peripheral vision audit for empirical output. Finds what the author cannot see — problems hiding in plain sight (vices) and opportunities being overlooked (virtues). Inspired by Viktor Shklovsky's defamiliarization and conversations with Jason Fletcher. Use when output exists and interpretation is about to happen. |
| allowed-tools | Read, Bash(ls*), Bash(cat*), Glob, Grep |
| argument-hint | [path-to-figure, table, or results file] [brief description of what you think the main finding is] |
Viktor Shklovsky, the Soviet literary theorist, argued that art exists to restore perception. A man who walks barefoot up a mountain eventually cannot feel his feet. Everything becomes habitual, automatic, unconscious. Art exists to make the stone stony again — to force you to feel what you have stopped noticing.
Research has the same problem. By the time you've spent months on a paper, you can't feel the stones under your feet. The main finding has collapsed your attention. Everything else in the output — the spike at t=1, the missing subgroup, the heterogeneity richer than the average, the identification strategy stronger than you argued — has become invisible. Not because it's hidden, but because you stopped looking.
Blindspot makes the stone stony again.
This skill audits your perception of your own output. It is not checking whether your code is correct — that's /referee2. It is checking whether you can see what's right in front of you.
Both should be run. Neither replaces the other.
| Blindspot | Referee 2 | |
|---|---|---|
| Question | Can you see what's in front of you? | Is your code correct? |
| Timing | When output first appears, before writing begins | After the project is complete, in a fresh session |
| Persona | Shklovsky — restoring perception | Health inspector with a checklist |
| Catches | Overlooked problems (vices) and overlooked opportunities (virtues) | Coding errors, replication failures, bad controls |
| Would have caught the t=1 spike? | Yes | No |
| Would have caught a merge error? | Maybe | Yes |
The workflow:
/blindspot → interpret and write/referee2Invoke Blindspot when you have:
The trigger is: output exists and interpretation is about to happen.
Do not invoke after the writing is done. Invoke before.
Every finding falls into one of four quadrants. Two are vices — problems hiding in plain sight. Two are virtues — opportunities being overlooked.
| What's there but unseen | What's absent but unnoticed | |
|---|---|---|
| Problems | Vice 1: The Unexplained Feature | Vice 2: The Convenient Absence |
| Opportunities | Virtue 1: The Unasked Question | Virtue 2: The Unexploited Strength |
Work through all four quadrants in order. For each finding: state what you found, then mark it DONE or FLAG. A FLAG means something doesn't have a clean explanation yet.
Something in the output that doesn't fit the story, but nobody asked about it.
The t=1 spike. A coefficient that flips sign in one spec. A sample size that drops by 30% between columns 2 and 3. The author has trained themselves not to see it because they're focused on the main result.
List every visible feature of the output before interpreting any of them. Every coefficient and its sign. Every spike, dip, or discontinuity. Every pattern across columns. Every sample size. Every number that appears anywhere. The main finding is just one item on this list.
For each feature, ask: what would generate this? Not "what does this mean for my hypothesis." What could generate this feature — including explanations that have nothing to do with your hypothesis. Work through the mundane explanations first:
Identify the single hardest feature to explain under the preferred interpretation. State it explicitly. Attempt to explain it. If you cannot:
The rule: If you can't explain every feature, you don't yet understand your output.
Something that should be there but isn't. The dog that didn't bark.
A robustness check that was never run. A subgroup that was never examined. A time period dropped without comment. A placebo test that doesn't exist. A pre-trend that was never plotted.
What would a hostile referee demand to see? List the robustness checks, falsification tests, and specification variants that a skeptical reader would consider essential. Which of them are missing?
What subgroups were never examined? Does the identification strategy apply to subpopulations that the paper never checks? Are there natural splits (by gender, by region, by time period, by treatment intensity) that should be in the table but aren't?
What was dropped without comment? Time periods excluded from the sample. Observations trimmed. Specifications that were tried and abandoned. Covariates that appeared in early drafts but disappeared. Any silent exclusion is a potential vice.
Does N change across columns without explanation? A sample size mismatch between specifications is almost never random. It traces to a decision — often an undocumented one.
The rule: The absence of evidence is not evidence of absence. If something should be there and isn't, that's a finding.
A pattern in the output that suggests something more interesting than the finding being reported.
The heterogeneity that's richer than the average effect. The mechanism visible in the data but absent from the hypothesis. The secondary finding hiding in the appendix or the descriptive statistics. The story the data is trying to tell you that you haven't heard yet because you came in with your own story.
Look at the heterogeneity. Is the average effect hiding a more interesting pattern? Does the treatment work differently for different groups in a way that tells you something about why it works?
Look for mechanism evidence. Is there a how, not just a that? Do intermediate outcomes move? Do the dynamics suggest a pathway the paper doesn't discuss?
Look at secondary outcomes and descriptive statistics. Are there patterns in the descriptives that are more interesting than the main regression? Is there a finding in the appendix that deserves the main text?
Is there a paper inside this paper? Sometimes the secondary finding is the real contribution. Is the author reporting the second-most-interesting thing in their data?
The rule: Don't just check whether the paper is right. Check whether it's missing the best version of itself.
Something about the research design, data, or results that the author is underselling.
Natural variation they haven't leveraged. A falsification test that would demolish the main objection but was never run. An identification argument that's stronger than the paper claims. Descriptive statistics that make the case more powerfully than the regression but are buried in a footnote.
Is the identification strategy stronger than the paper argues? Is there variation being left on the table? Is there a natural experiment within the natural experiment?
Is there a falsification test that would crush the main objection? Something easy to run that the author hasn't thought of — a placebo outcome, a placebo treatment group, a different time window where the effect should be zero?
Are the descriptive statistics undersold? Would a figure make the case more clearly than a table? Is there a visual that would land the argument in one image?
Is the paper positioned too narrowly? Does the finding speak to a larger literature the authors aren't citing? Is the contribution bigger than the paper claims?
The rule: A paper that undersells its strengths is leaving credibility on the table. Find what the author is too close to see.
After working through all four quadrants, produce a Blindspot Report:
## Blindspot Report
**Output:** [what was audited]
**Date:** YYYY-MM-DD
### Vice 1: The Unexplained Feature
- Features listed: [count]
- Hardest to explain: [what it is]
- Resolved? [yes / FLAG]
- Findings: [details]
### Vice 2: The Convenient Absence
- Missing checks identified: [list]
- Missing subgroups: [list]
- Unexplained N changes: [if any]
- Findings: [details]
### Virtue 1: The Unasked Question
- Heterogeneity opportunities: [list]
- Mechanism evidence: [list]
- Secondary findings: [list]
- Findings: [details]
### Virtue 2: The Unexploited Strength
- Undersold design features: [list]
- Unused falsification tests: [list]
- Positioning opportunities: [list]
- Findings: [details]
### Ruling
[ ] CLEAR — proceed to interpretation. No vices found; virtues noted for consideration.
[ ] CONDITIONAL — proceed but acknowledge open questions explicitly. Vices flagged but manageable.
[ ] HOLD — do not interpret or publish until flagged vices are resolved.
This skill was inspired by conversations with Jason Fletcher (University of Wisconsin), who commented on Scott Cunningham's Substack post (Claude Code 35, March 2026) and asked about the spikes at t=1 and t=3 in a figure where Scott had focused entirely on the spike at t=2. The spike at t=1 was the tell — it was inconsistent with the p-hacking interpretation and pointed immediately to rounding.
Fletcher described this as a habit from graduate training — stepping back from the main coefficient and asking about something else in the table. He wrote about it publicly in "Owning All the Numbers" (March 2026).
The theoretical frame comes from Viktor Shklovsky's "Art as Device" (1917): the purpose of art is to restore perception, to make the stone stony again. Blindspot applies that principle to empirical research — forcing you to see your own output as a stranger would, before the story you want to tell has automated your perception.
See the full origin story on Substack for the account of how this skill came to exist.