Skip to content
Automation
Skill

/identify-critical-chain

Identify the longest/limiting dependency path while accounting for resource contention and convergence points.

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill identify-critical-chain --agent claude-code

How 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/identify-critical-chain

Context preview

The summary Claude sees to decide when to auto-load this skill.

Identify the longest/limiting dependency path while accounting for resource contention and convergence points.

SKILL.md

identify-critical-chain.SKILL.md
name: identify-critical-chain
description: "Identify the longest/limiting dependency path while accounting for resource contention and convergence points."

identify-critical-chain

Purpose

Identify the longest/limiting dependency path while accounting for resource contention and convergence points.

Input contract

required: [dependency_graph, resource_constraints, convergence_points]
optional: [evidence, assumptions, prior_results]
constraints: [use named scientific objects; retain provenance and missingness; $\alpha$ = 0.05 and power = 0.8 where applicable]

Procedure

1. Validate the typed inputs and state the decision this operation must support. 2. Apply the declared operation to the named object; record intermediate values that affect interpretation. 3. Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.

Output contract

produces: [identify_critical_chain_result, evidence_trace, uncertainties]
delta_fields: [evidence_updates, uncertainties]

Quality gates

  • Inputs are named scientific objects with compatible schemas.
  • Every material result has a derivation or source reference.
  • Fixed statistical criteria remain exact where applicable: $\alpha$ 0.05 and power 0.8.

Failure and counterexamples

Return a failed operation with the violated precondition when inputs are incomplete, assumptions are unsupported, or a counterexample defeats the result.

Provenance map

  • intermediate: experiment-execution/critical-chain-identification
Read more
Ships withde-anthropocentric-research-engine

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.

Get the whole plugin
Stats
499
Stars
41
Forks
Active
Maintenance
Python
Language
Apache-2.0
License
6h ago
Last commit
7mo ago
Created

Repo: yogsoth-ai/de-anthropocentric-research-engine