reading-notes
Skill15 starsChanged 8 days agoMITView on GitHub
Write structured, dated, falsifiable reading notes for a paper (claims, parameters, numbers with page pointers, hidden assumptions, reusable tricks, open problems). Use whenever an agent reads a paper that may be cited, compared against, or mined for ideas.
In this repository29 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- abstract-craftSkill
- anonymize-checkSkill
- artifact-packSkill
- baseline-pinSkill
- bench-protocolSkill
- bib-verifySkill
- camera-readySkill
- eprint-searchSkill
- experiments-writingSkill
- falsifySkill
- figure-harvestSkill
- idea-mining-loopSkill
- lean-bridgeSkill
- lit-matrixSkill
- log-to-evidenceSkill
- mock-reviewSkill
- paper-figuresSkill
- paper-playbookSkill
- param-estimationSkill
- phase-gateSkill
- polish-writingSkill
- rebuttalSkill
- related-work-writingSkill
- sage-checkSkill
- technique-overviewSkill
- textbook-indexSkill
- venue-calibrationSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 42
Every number carries a source pointer.
- Very specific: line 45
If the note disagrees with another note, add it to the project's errata section and tell pi-orchestrator.
- Distinctive: line 46
Never paste long verbatim passages.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
reading-notes
Notes are the compressed layer every later agent reads before opening PDFs (retrieval order: own notes → papers → textbooks → baseline code). They must be trustworthy enough to cite from and honest about what was not read.
File and naming
notes/papers/<year>-<id-or-shortname>.md (e.g. notes/papers/2021-266-vole-psi.md). One file per paper;
append dated sections on re-reads instead of overwriting.
Template
---
key: RS21 # bib key in refs.bib (must pass bib-verify)
eprint: 2021/266 # or doi
read: 2099-01-31 (sections 1-4, 6; appendix skipped) # say what you did NOT read
reader: lit-scout
---
## One-line contribution
## Setting (model, parameters, security level, hardware)
## Claims (numbered, each falsifiable, with page/section)
C1. ... (Thm 3, p.12)
## Concrete numbers (value, unit, parameters, source pointer)
## Technique in 5 lines (the mechanism; which object is changed)
## Hidden assumptions / fixed parameters
(e.g. "power-of-two ring only", "sparse secret", "LAN only", "the base B is fixed to 2^k without justification")
## Representation tricks (one line each: "turns X from fixed into a free variable")
## Weak points / what a reviewer would attack
## Open problems stated by the authors (quote + page)
## Relevance to us: compare | reuse | mine | ignore — and whyRules
- Every number carries a source pointer; every claim is phrased so it could be wrong.
- Distinguish "the paper proves", "the paper measures", "the paper conjectures/heuristic".
- Record what you skipped. "Read all 28 papers" is a claim the PI may audit.
- If the note disagrees with another note, add it to the project's errata section and tell
pi-orchestrator. - Never paste long verbatim passages; paraphrase and point.
- Negative notes ("this line does not help because ...") are kept — they stop the next agent repeating the read.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

