/third-pass-deep-read
Keshav's third pass — the heaviest of the three, a full sentence-by-sentence re-read including proofs/derivations, attempting a virtual re-implementation of the paper to surface implicit assumptions and concrete improvement points. Use this after second-pass-grasp, as the
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill third-pass-deep-read --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
/third-pass-deep-read
Context preview
The summary Claude sees to decide when to auto-load this skill.
Keshav's third pass — the heaviest of the three, a full sentence-by-sentence re-read including proofs/derivations, attempting a virtual re-implementation of the paper to surface implicit assumptions and concrete improvement points. Use this after second-pass-grasp, as the
SKILL.md
third-pass-deep-read.SKILL.mdname: third-pass-deep-read
description: Keshav's third pass — the heaviest of the three, a full sentence-by-sentence re-read including proofs/derivations, attempting a virtual re-implementation of the paper to surface implicit assumptions and concrete improvement points. Use this after second-pass-grasp, as the terminal step of the Keshav three-pass method, whenever genuine mastery of a paper (not just a summary) is needed. This is not a skippable recap — treat "nothing new to add" as suspicious, not a default outcome.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'source_path (string), meta_path (string), grasp_summary (string)'
reads: 'full paper body including proofs and derivations'
output: 'deep_read_notes (string)'
dependencies:
sops:
- spawn-agent
Third Pass Deep Read
Keshav's third pass: sentence-by-sentence re-read with proofs/derivations included, attempting virtual re-implementation. The heaviest pass of the three — terminal step of the Keshav cascade.
Execution
Subagent — spawned via spawn-agent skill.
Why renamed from `third-pass-verify` (v1's name)
v1's version of this SOP (`staged/wechat-article-v1/skills/third-pass-verify/`) treated this as a "targeted re-check of uncertain_fields, no-op if none flagged" step — which, per the coverage audit's S2 finding, effectively deleted Keshav's real third pass (a 4-5+ hour re-implementation attempt) and replaced it with a cheap verification step serving v1's own pipeline. This v2 SOP restores the actual third pass; the rename to `third-pass-deep-read` marks that this is not the same behavior as the old `third-pass-verify`, even though both sit in the same cascade position.
Why Subagent
A genuine re-implementation attempt needs a context that can hold the full paper and reason through design alternatives without being anchored to how pass 2 already framed the contribution.
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
<!-- END available-tables (generated) -->
Read more
name: third-pass-deep-read description: Keshav's third pass — the heaviest of the three, a full sentence-by-sentence re-read including proofs/derivations, attempting a virtual re-implementation of the paper to surface implicit assumptions and concrete improvement points. Use this after second-pass-grasp, as the terminal step of the Keshav three-pass method, whenever genuine mastery of a paper (not just a summary) is needed. This is not a skippable recap — treat "nothing new to add" as suspicious, not a default outcome. version: 1.0.0 category: paper-reading type: sop execution: subagent prompt: ./prompt.md input: 'source_path (string), meta_path (string), grasp_summary (string)' reads: 'full paper body including proofs and derivations' output: 'deep_read_notes (string)' dependencies: sops: - spawn-agent
Third Pass Deep Read
Keshav's third pass: sentence-by-sentence re-read with proofs/derivations included, attempting virtual re-implementation. The heaviest pass of the three — terminal step of the Keshav cascade.
Execution
Subagent — spawned via spawn-agent skill.
Why renamed from `third-pass-verify` (v1's name)
v1's version of this SOP (`staged/wechat-article-v1/skills/third-pass-verify/`) treated this as a "targeted re-check of uncertain_fields, no-op if none flagged" step — which, per the coverage audit's S2 finding, effectively deleted Keshav's real third pass (a 4-5+ hour re-implementation attempt) and replaced it with a cheap verification step serving v1's own pipeline. This v2 SOP restores the actual third pass; the rename to `third-pass-deep-read` marks that this is not the same behavior as the old `third-pass-verify`, even though both sit in the same cascade position.
Why Subagent
A genuine re-implementation attempt needs a context that can hold the full paper and reason through design alternatives without being anchored to how pass 2 already framed the contribution.
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
<!-- END available-tables (generated) -->
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

