/second-pass-grasp
Keshav's second pass — a careful full read (ignoring proof/derivation detail) producing prose-level understanding sufficient to explain the paper's main contribution and evidence to a colleague. Use this after first-pass-skim, as the main content-grasping pass of the Keshav
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill second-pass-grasp --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
/second-pass-grasp
Context preview
The summary Claude sees to decide when to auto-load this skill.
Keshav's second pass — a careful full read (ignoring proof/derivation detail) producing prose-level understanding sufficient to explain the paper's main contribution and evidence to a colleague. Use this after first-pass-skim, as the main content-grasping pass of the Keshav
SKILL.md
second-pass-grasp.SKILL.mdname: second-pass-grasp
description: Keshav's second pass — a careful full read (ignoring proof/derivation detail) producing prose-level understanding sufficient to explain the paper's main contribution and evidence to a colleague. Use this after first-pass-skim, as the main content-grasping pass of the Keshav three-pass method; do not force its output into a structured data schema.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'source_path (string), meta_path (string), skim_notes (string)'
reads: 'full paper body; skips proofs and derivations by instruction, not by omission'
output: 'grasp_summary (string)'
dependencies:
sops:
- spawn-agent
Second Pass Grasp
Keshav's second pass: full read, proofs/derivations deferred, output is accumulated prose understanding (not a structured artifact — this is the one method in this package's whole method set that deliberately does not produce a persisted structured object).
Execution
Subagent — spawned via spawn-agent skill.
Why Subagent
Full-text reading toward a genuine "could explain this to a colleague" understanding benefits from an uninterrupted context, distinct from the shallow first pass and the exhaustive third pass.
Do not port v1's bundle schema here
An earlier version of this package (`staged/wechat-article-v1/skills/second-pass-grasp/`) produced a `draft_bundle` + `uncertain_fields` structure for its own WeChat-article pipeline. That was correct for v1's purpose but is NOT Keshav's original second pass — this v2 SOP's output is prose, per the graph's explicit correction (`context/2026-08-07-13-42-sop-pipeline-graph.html`, node `second-pass-grasp`, "S2修订"). Do not reintroduce the bundle schema here.
<!-- 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: second-pass-grasp description: Keshav's second pass — a careful full read (ignoring proof/derivation detail) producing prose-level understanding sufficient to explain the paper's main contribution and evidence to a colleague. Use this after first-pass-skim, as the main content-grasping pass of the Keshav three-pass method; do not force its output into a structured data schema. version: 1.0.0 category: paper-reading type: sop execution: subagent prompt: ./prompt.md input: 'source_path (string), meta_path (string), skim_notes (string)' reads: 'full paper body; skips proofs and derivations by instruction, not by omission' output: 'grasp_summary (string)' dependencies: sops: - spawn-agent
Second Pass Grasp
Keshav's second pass: full read, proofs/derivations deferred, output is accumulated prose understanding (not a structured artifact — this is the one method in this package's whole method set that deliberately does not produce a persisted structured object).
Execution
Subagent — spawned via spawn-agent skill.
Why Subagent
Full-text reading toward a genuine "could explain this to a colleague" understanding benefits from an uninterrupted context, distinct from the shallow first pass and the exhaustive third pass.
Do not port v1's bundle schema here
An earlier version of this package (`staged/wechat-article-v1/skills/second-pass-grasp/`) produced a `draft_bundle` + `uncertain_fields` structure for its own WeChat-article pipeline. That was correct for v1's purpose but is NOT Keshav's original second pass — this v2 SOP's output is prose, per the graph's explicit correction (`context/2026-08-07-13-42-sop-pipeline-graph.html`, node `second-pass-grasp`, "S2修订"). Do not reintroduce the bundle schema here.
<!-- 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