/component-decomposition
Decompose system into functional components, identify dependencies, and
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill component-decomposition --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
/component-decomposition
Context preview
The summary Claude sees to decide when to auto-load this skill.
Decompose system into functional components, identify dependencies, and
SKILL.md
component-decomposition.SKILL.mdname: component-decomposition
description: Decompose system into functional components, identify dependencies, and
surface trimming candidates.
execution: tactic
dependencies:
sops:
- function-model-construction
- trimming-execution
Component Decomposition
Decompose a system into its functional components, map dependencies, and identify candidates for trimming or surgical modification.
Stages
Stage 1: Function Model Construction
Build complete functional model using function-model-construction SOP. Identify all components and their interactions (useful, harmful, insufficient, excessive).
Stage 2: Parameter Identification
For each component, identify key parameters using parameter-identification SOP. Map which parameters are shared, conflicting, or independent.
Stage 3: Trimming Candidate Selection
Evaluate each component for trimming potential via trimming-execution SOP criteria: high harmful-function ratio, function redistributable to neighbors, low integration cost.
Minimum Yield
| Metric | Floor | |--------|-------| | Components identified | ≥5 | | Interactions mapped | ≥8 | | Trimming candidates | ≥3 | | Parameters per component | ≥2 |
Available SOPs
| SOP | Role | |-----|------| | function-model-construction | Stage 1 — build functional model | | parameter-identification | Stage 2 — extract component parameters | | trimming-execution | Stage 3 — evaluate trimming feasibility | | surgery-operation | Post — apply surgical operations to candidates |
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | function-model-construction | Build substance-field functional model of a system, annotating useful, harmful, insufficient, and excessive interactions. | | trimming-execution | Progressively remove components from a system while verifying function preservation through redistribution. |
<!-- END available-tables (generated) -->
Read more
name: component-decomposition description: Decompose system into functional components, identify dependencies, and surface trimming candidates. execution: tactic dependencies: sops: - function-model-construction - trimming-execution
Component Decomposition
Decompose a system into its functional components, map dependencies, and identify candidates for trimming or surgical modification.
Stages
Stage 1: Function Model Construction
Build complete functional model using function-model-construction SOP. Identify all components and their interactions (useful, harmful, insufficient, excessive).
Stage 2: Parameter Identification
For each component, identify key parameters using parameter-identification SOP. Map which parameters are shared, conflicting, or independent.
Stage 3: Trimming Candidate Selection
Evaluate each component for trimming potential via trimming-execution SOP criteria: high harmful-function ratio, function redistributable to neighbors, low integration cost.
Minimum Yield
| Metric | Floor | |--------|-------| | Components identified | ≥5 | | Interactions mapped | ≥8 | | Trimming candidates | ≥3 | | Parameters per component | ≥2 |
Available SOPs
| SOP | Role | |-----|------| | function-model-construction | Stage 1 — build functional model | | parameter-identification | Stage 2 — extract component parameters | | trimming-execution | Stage 3 — evaluate trimming feasibility | | surgery-operation | Post — apply surgical operations to candidates |
<!-- BEGIN available-tables (generated) -->
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use | | --- | --- | | function-model-construction | Build substance-field functional model of a system, annotating useful, harmful, insufficient, and excessive interactions. | | trimming-execution | Progressively remove components from a system while verifying function preservation through redistribution. |
<!-- 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

