# Dependency Aware Tool Chaining

> Skill: dependency-aware-tool-chaining

- Skill: `dingxingdi/dependency-aware-tool-chaining-2` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add dingxingdi/dependency-aware-tool-chaining-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dingxingdi/dependency-aware-tool-chaining-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: dingxingdi (https://skillmd.com/u/dingxingdi)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/dingxingdi/dependency-aware-tool-chaining-2

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# Skill: dependency-aware-tool-chaining
## 1. Capability Definition & Real Case
* **Professional Definition**: Dependency-aware tool chaining is the capability to decompose a task into a directed sequence of subtasks whose outputs, intermediate states, and control constraints are explicitly propagated to later steps. The key requirement is not merely using multiple tools, but using them in a causally valid order that respects prerequisites and data-flow constraints. For orchestration agents, this is the difference between a bag of tool calls and an executable workflow.
* **Dimension Hierarchy**: Workflow Orchestration->Dependency and Schedule Management->dependency-aware-tool-chaining
### Real Case
*Extract 1 to 3 concrete cases from the actual benchmark papers to demonstrate this capability, aiming for a maximum of 3 cases. CRITICAL HANDLING FOR TEMPLATES: If the paper provides concrete examples, extract them directly. However, if the paper ONLY provides abstract templates, you MUST NOT just output the template. Instead, you MUST instantiate the template into a highly specific, realistic, and logically coherent example according to the papers. Based on the papers, invent a specific entity, a specific environment, a concrete trajectory, and a final answer that perfectly perfectly reflects the benchmark's exact intent and difficulty.
Do NOT state that they are extracted from papers or benchmarks, and absolutely do NOT mention or reference the source papers.*

**[Case 1]**
* **Initial Environment**: A scientific-research environment contains chemistry, molecular-format conversion, captioning, patent search, and safety lookup tools. The agent is asked to analyze a synthesis route, but every downstream step depends on the output format of the upstream step. The environment also contains unrelated tools, so the chain must be assembled deliberately rather than by brute force.
* **Real Question**: Predict the product of the reaction between salicylic acid and acetic anhydride, describe the product in plain language, check whether it appears in patent records, and then retrieve safety information for the resulting compound.
* **Real Trajectory**: The agent first invokes a reaction-prediction tool to obtain the product structure, converts the structure into the representation required by the captioning tool, generates a textual description, then maps the structure into the identifier required by the patent and safety lookups. Each call consumes a transformed artifact from the prior call, and the chain only succeeds if every interface handoff is valid.
* **Why this demonstrates the capability**: This case isolates dependency-aware chaining because no step is independently sufficient. The orchestrator must understand not just which tools exist, but how one tool’s artifact becomes another tool’s admissible input. It also tests whether the agent avoids illegal reorderings, such as trying to search patents before a stable compound representation is available.

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**[Case 2]**
* **Initial Environment**: An EDA automation environment exposes setup, synthesis, floorplanning, placement, clock-tree synthesis, routing, and metric-evaluation functions. The flow is stateful: later tools assume that earlier stages have already produced the required design state. The agent receives only a natural-language design request and must generate an executable sequence.
* **Real Question**: For the design named aes on the asap7 platform, perform setup, synthesis with a five-unit clock period, floorplan the core, place with a density target, run clock-tree synthesis, route the design, and then report the route-stage power metric.
* **Real Trajectory**: The agent decomposes the request into setup, synthesis, floorplan, placement, CTS, global route, detail route, and metric extraction, then emits an executable script in that exact order. It does not attempt to evaluate metrics before routing or perform placement before the design has been initialized and synthesized.
* **Why this demonstrates the capability**: This demonstrates the orchestration capability of building a valid dependency chain over a process with irreversible stage assumptions. Every step mutates the design state and creates the preconditions for the next step. The skill being tested is therefore the coordinator’s ability to honor workflow topology, not merely to recall tool names.

## Pipeline Execution Instructions
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. **Do not hallucinate steps.** Read the corresponding reference file for each phase sequentially:

1. **Phase 1: Environment Exploration**
   Read the exploration guidelines to discover raw knowledge seeds:
   `references/EXPLORATION.md`

2. **Phase 2: Trajectory Selection**
   Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
   `references/SELECTION.md`

3. **Phase 3: Data Synthesis**
   Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
   `references/SYNTHESIS.md`