/reproducibility-third-party-verification
(Proposal, unverified) Attempt to verify a paper's reported results by actually executing its released code/scripts against its own reported configuration — the only SOP in this package whose action type is code execution rather than text reading/judgment. Use this after
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill reproducibility-third-party-verification --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 →
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/reproducibility-third-party-verification
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The summary Claude sees to decide when to auto-load this skill.
(Proposal, unverified) Attempt to verify a paper's reported results by actually executing its released code/scripts against its own reported configuration — the only SOP in this package whose action type is code execution rather than text reading/judgment. Use this after
SKILL.md
reproducibility-third-party-verification.SKILL.mdname: reproducibility-third-party-verification
description: (Proposal, unverified) Attempt to verify a paper's reported results by actually executing its released code/scripts against its own reported configuration — the only SOP in this package whose action type is code execution rather than text reading/judgment. Use this after unit-classification has extracted the paper's reported configuration/hyperparameters as classified units; "not_attempted" is a correct, common output when the paper's own reporting is too incomplete to run, not a failure of this SOP.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'classified_units (list of {unit_text, offset, label})'
output: 'verification_result (list of {claim, reproducible, notes})'
dependencies:
sops:
- spawn-agentReproducibility Third-Party Verification (Proposal)
Actually runs code to check reported results against the paper's own extracted configuration — unique action type (execution) in this package. Fills the evidence-verification × engineering-metadata gap in the evaluative-stance × content-layer matrix.
Execution
Subagent — spawned via spawn-agent skill.
Dependency: unit-classification, not raw full_text (graph correction L20)
This SOP needs the paper's reported configuration already pulled out in structured form before attempting to verify it — hence its input is `classified_units`, not `full_text` directly. An earlier graph draft had this SOP depending on nothing upstream, which meant it had no defined way to get the structured claims it needs to check.
Proposal Status — Read Before Modifying
No primary-source precedent, no inter-rater-reliability baseline. Keep "(Proposal, unverified)" in the description until real usage validates the method. Given the code-execution action type, treat any scope expansion here with more caution than the other 3 proposal SOPs.
<!-- BEGIN available-tables (generated) -->
Available SOPs
| SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. |
<!-- END available-tables (generated) -->
Read more
name: reproducibility-third-party-verification
description: (Proposal, unverified) Attempt to verify a paper's reported results by actually executing its released code/scripts against its own reported configuration — the only SOP in this package whose action type is code execution rather than text reading/judgment. Use this after unit-classification has extracted the paper's reported configuration/hyperparameters as classified units; "not_attempted" is a correct, common output when the paper's own reporting is too incomplete to run, not a failure of this SOP.
version: 1.0.0
category: paper-reading
type: sop
execution: subagent
prompt: ./prompt.md
input: 'classified_units (list of {unit_text, offset, label})'
output: 'verification_result (list of {claim, reproducible, notes})'
dependencies:
sops:
- spawn-agentReproducibility Third-Party Verification (Proposal)
Actually runs code to check reported results against the paper's own extracted configuration — unique action type (execution) in this package. Fills the evidence-verification × engineering-metadata gap in the evaluative-stance × content-layer matrix.
Execution
Subagent — spawned via spawn-agent skill.
Dependency: unit-classification, not raw full_text (graph correction L20)
This SOP needs the paper's reported configuration already pulled out in structured form before attempting to verify it — hence its input is `classified_units`, not `full_text` directly. An earlier graph draft had this SOP depending on nothing upstream, which meant it had no defined way to get the structured claims it needs to check.
Proposal Status — Read Before Modifying
No primary-source precedent, no inter-rater-reliability baseline. Keep "(Proposal, unverified)" in the description until real usage validates the method. Given the code-execution action type, treat any scope expansion here with more caution than the other 3 proposal SOPs.
<!-- BEGIN available-tables (generated) -->
Available SOPs
| 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

