/keshav-three-pass
Tactic: Read one paper by Keshav''s three-pass method — a shallow skim, a contribution-grasping full read, then a deep virtual re-implementation. Use when the goal is understanding a paper rather than extracting a fixed schema.
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill keshav-three-pass --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/keshav-three-pass
Context preview
The summary Claude sees to decide when to auto-load this skill.
Tactic: Read one paper by Keshav''s three-pass method — a shallow skim, a contribution-grasping full read, then a deep virtual re-implementation. Use when the goal is understanding a paper rather than extracting a fixed schema.
SKILL.md
keshav-three-pass.SKILL.mdname: keshav-three-pass
description: 'Tactic: Read one paper by Keshav''s three-pass method — a shallow skim, a contribution-grasping full read, then a deep virtual re-implementation. Use when the goal is understanding a paper rather than extracting a fixed schema.'
version: 1.0.0
category: paper-reading
type: tactic
execution: tactic
input: 'paper_ref (string — title, arXiv ID, DOI, URL, or local .md/.txt/.pdf path)'
output: 'three markdown files under context/papers/<dir>/keshav-three-pass/'
sops:
- paper-fetch
- first-pass-skim
- second-pass-grasp
- third-pass-deep-read
dependencies:
sops:
- paper-fetch
- first-pass-skim
- second-pass-grasp
- third-pass-deep-read
Keshav Three-Pass
Read one paper in three passes of increasing depth. The outputs accumulate as prose rather than fixed fields; use a different tactic when cross-paper alignment matters more than understanding.
Orchestration Pattern
1. Call `paper-fetch` with `paper_ref`. Stop on `not_found`. Create `context/papers/<dir>/keshav-three-pass/` on success. 2. Call `first-pass-skim` with `source_path` and `meta_path`. Write `01-first-pass-skim.md`, recording `read_deeper` in frontmatter. 3. If `read_deeper` is false, stop by default. Continue only on explicit caller override and record `gate_overridden: true`. 4. Call `second-pass-grasp` with the paths and `skim_notes`; write `02-second-pass-grasp.md`. 5. Call `third-pass-deep-read` with the paths and `grasp_summary`; write `03-third-pass-deep-read.md`.
Do not collapse pass 3 into a recap of pass 2. It must surface implicit assumptions, virtual re-implementation mismatches, and concrete improvements.
Output Layout
context/papers/<timestamp>-<title-slug>/
source.md
source.meta.json
keshav-three-pass/
01-first-pass-skim.md
02-second-pass-grasp.md
03-third-pass-deep-read.mdEach output carries `sop`, `tactic`, and `written_at` frontmatter. Report the gate outcome, core claim, most consequential implicit assumption, unresolved flags, and all output paths.
Read more
name: keshav-three-pass description: 'Tactic: Read one paper by Keshav''s three-pass method — a shallow skim, a contribution-grasping full read, then a deep virtual re-implementation. Use when the goal is understanding a paper rather than extracting a fixed schema.' version: 1.0.0 category: paper-reading type: tactic execution: tactic input: 'paper_ref (string — title, arXiv ID, DOI, URL, or local .md/.txt/.pdf path)' output: 'three markdown files under context/papers/<dir>/keshav-three-pass/' sops: - paper-fetch - first-pass-skim - second-pass-grasp - third-pass-deep-read dependencies: sops: - paper-fetch - first-pass-skim - second-pass-grasp - third-pass-deep-read
Keshav Three-Pass
Read one paper in three passes of increasing depth. The outputs accumulate as prose rather than fixed fields; use a different tactic when cross-paper alignment matters more than understanding.
Orchestration Pattern
1. Call `paper-fetch` with `paper_ref`. Stop on `not_found`. Create `context/papers/<dir>/keshav-three-pass/` on success. 2. Call `first-pass-skim` with `source_path` and `meta_path`. Write `01-first-pass-skim.md`, recording `read_deeper` in frontmatter. 3. If `read_deeper` is false, stop by default. Continue only on explicit caller override and record `gate_overridden: true`. 4. Call `second-pass-grasp` with the paths and `skim_notes`; write `02-second-pass-grasp.md`. 5. Call `third-pass-deep-read` with the paths and `grasp_summary`; write `03-third-pass-deep-read.md`.
Do not collapse pass 3 into a recap of pass 2. It must surface implicit assumptions, virtual re-implementation mismatches, and concrete improvements.
Output Layout
context/papers/<timestamp>-<title-slug>/
source.md
source.meta.json
keshav-three-pass/
01-first-pass-skim.md
02-second-pass-grasp.md
03-third-pass-deep-read.mdEach output carries `sop`, `tactic`, and `written_at` frontmatter. Report the gate outcome, core claim, most consequential implicit assumption, unresolved flags, and all output paths.
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

