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…
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.
/third-pass-deep-readContext 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
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
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.
Subagent — spawned via spawn-agent skill.
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.
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.
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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. |
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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
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…
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.…
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…
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…
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…
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…