Claude Skill

automatic-freeform-graphs-design

Use when a user wants a looser conceptual graph for exploratory work. Not for remote, credential, publish, deploy, or irreversible changes.

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Part of outlinedriven/outline-driven-development — 145 skills

Install

skills CLI npx skills add https://github.com/OutlineDriven/outline-driven-development/tree/main/.devin/skills/automatic-freeform-graphs-design
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install outlinedriven-outline-driven-development@llmmart
Git git clone https://github.com/OutlineDriven/outline-driven-development.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole outlinedriven/outline-driven-development collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Automatic freeform graphs design

Contract

Field Bound contract
Trigger User wants a looser conceptual graph for exploratory work.
Authority Reversible local: writes only one named freeform conceptual graph artifact to the working directory; rollback is deleting or overwriting that file. No remote mutation.
Side effect A freeform conceptual graph artifact for exploration, written to the local filesystem.
Done A freeform conceptual graph is generated and can be used for exploration.

Inputs

  • Exploratory topic or problem statement (required): the subject area to map. May be a question, a half-formed idea, a domain, or a set of related concerns.
  • Existing notes or fragments (optional): prior concepts, questions, or connections the graph should incorporate.
  • Output path (optional): where to write the graph artifact. Defaults to a file in the working directory.

Procedure

  1. Read the exploratory topic and any supplied notes. Identify the kind of exploration: open-ended question, design-space survey, concept mapping, or unknown-territory scouting. Done when: the exploration type is identified from the topic and notes.
  2. Extract concepts, questions, unknowns, and hypotheses as candidate nodes. Do not force them into a dependency order: this is exploration, not execution planning. Done when: candidate nodes are extracted without imposing a dependency order.
  3. Map relationships between nodes as labeled edges. Use relationship types suited to exploration: influences, tensions, supports, contradicts, depends-on-maybe, raises-question-of, and unknown-link. Allow cycles, bidirectional edges, and self-references where the exploration calls for them. Done when: edges are mapped with exploration-suited relationship labels, allowing cycles.
  4. Mark each node and edge with a confidence marker: certain, suspected, or unknown. Mark open questions explicitly so the graph surfaces what is not yet known. Done when: every node and edge has a confidence marker and open questions are marked.
  5. Identify clusters of tightly connected nodes and label them as provisional themes. Identify bridges between clusters as high-value exploration targets. Done when: clusters are labeled as themes and bridges are identified as high-value targets.
  6. Write the graph as a text artifact: a node list with confidence markers, an edge list with relationship labels, a cluster summary, the high-value bridge targets identified in step 5, and a list of open questions. Use a plain-text or markdown format that a human can read and revise without tooling. Done when: the graph artifact is written with all five sections in human-readable format.
  7. Review the graph against the original topic. Check that it surfaces the key unknowns without imposing a false dependency order. If a region is sparse or missing, add nodes and edges rather than leaving gaps. Done when: the graph surfaces key unknowns and has no false dependency order or sparse gaps.

Failure and recovery

  • Topic too vague to extract nodes: ask the human for one concrete anchor (a question, a constraint, or a stakeholder concern), then proceed from that anchor. Do not fabricate concepts to fill the graph.
  • Graph collapses into a linear chain or strict DAG: the procedure drifted toward execution planning. Restart at step 3 and deliberately use non-dependency relationship types (tensions, unknowns, contradictions) to break the chain.
  • Graph too dense to read: collapse low-confidence peripheral nodes into a summary node and keep the high-value bridges visible. Preserve the full node list in an appendix section.
  • Partial result: if the procedure stops before step 7, deliver the graph as-is with an explicit note on which review step was not completed. Do not claim the done predicate holds.
  • Rollback: the artifact is a single local file. Delete or overwrite it to revert. No other state is mutated.

Output

A single freeform conceptual graph artifact ordered: node list (with confidence markers), labeled edge list (allowing cycles), provisional cluster summary, high-value bridge targets, open-questions list, human-readable, revisable without tooling.

Files (outline-driven-development)
  • agents
    • openai.yaml 155 B
      interface:
        display_name: "Automatic Freeform Graphs Design"
        short_description: "Use when a user wants a looser conceptual graph for exploratory work."
      
  • SKILL.md 4.3 KB
    ---
    name: automatic-freeform-graphs-design
    description: 'Use when a user wants a looser conceptual graph for exploratory work. Not for remote, credential, publish, deploy, or irreversible changes.'
    ---
    
    # Automatic freeform graphs design
    
    ## Contract
    
    | Field | Bound contract |
    |---|---|
    | Trigger | User wants a looser conceptual graph for exploratory work. |
    | Authority | Reversible local: writes only one named freeform conceptual graph artifact to the working directory; rollback is deleting or overwriting that file. No remote mutation. |
    | Side effect | A freeform conceptual graph artifact for exploration, written to the local filesystem. |
    | Done | A freeform conceptual graph is generated and can be used for exploration. |
    
    ## Inputs
    
    - Exploratory topic or problem statement (required): the subject area to map. May be a question, a half-formed idea, a domain, or a set of related concerns.
    - Existing notes or fragments (optional): prior concepts, questions, or connections the graph should incorporate.
    - Output path (optional): where to write the graph artifact. Defaults to a file in the working directory.
    
    ## Procedure
    
    1. Read the exploratory topic and any supplied notes. Identify the kind of exploration: open-ended question, design-space survey, concept mapping, or unknown-territory scouting. Done when: the exploration type is identified from the topic and notes.
    2. Extract concepts, questions, unknowns, and hypotheses as candidate nodes. Do not force them into a dependency order: this is exploration, not execution planning. Done when: candidate nodes are extracted without imposing a dependency order.
    3. Map relationships between nodes as labeled edges. Use relationship types suited to exploration: influences, tensions, supports, contradicts, depends-on-maybe, raises-question-of, and unknown-link. Allow cycles, bidirectional edges, and self-references where the exploration calls for them. Done when: edges are mapped with exploration-suited relationship labels, allowing cycles.
    4. Mark each node and edge with a confidence marker: certain, suspected, or unknown. Mark open questions explicitly so the graph surfaces what is not yet known. Done when: every node and edge has a confidence marker and open questions are marked.
    5. Identify clusters of tightly connected nodes and label them as provisional themes. Identify bridges between clusters as high-value exploration targets. Done when: clusters are labeled as themes and bridges are identified as high-value targets.
    6. Write the graph as a text artifact: a node list with confidence markers, an edge list with relationship labels, a cluster summary, the high-value bridge targets identified in step 5, and a list of open questions. Use a plain-text or markdown format that a human can read and revise without tooling. Done when: the graph artifact is written with all five sections in human-readable format.
    7. Review the graph against the original topic. Check that it surfaces the key unknowns without imposing a false dependency order. If a region is sparse or missing, add nodes and edges rather than leaving gaps. Done when: the graph surfaces key unknowns and has no false dependency order or sparse gaps.
    
    ## Failure and recovery
    - Topic too vague to extract nodes: ask the human for one concrete anchor (a question, a constraint, or a stakeholder concern), then proceed from that anchor. Do not fabricate concepts to fill the graph.
    - Graph collapses into a linear chain or strict DAG: the procedure drifted toward execution planning. Restart at step 3 and deliberately use non-dependency relationship types (tensions, unknowns, contradictions) to break the chain.
    - Graph too dense to read: collapse low-confidence peripheral nodes into a summary node and keep the high-value bridges visible. Preserve the full node list in an appendix section.
    - Partial result: if the procedure stops before step 7, deliver the graph as-is with an explicit note on which review step was not completed. Do not claim the done predicate holds.
    - Rollback: the artifact is a single local file. Delete or overwrite it to revert. No other state is mutated.
    
    ## Output
    A single freeform conceptual graph artifact ordered: node list (with confidence markers), labeled edge list (allowing cycles), provisional cluster summary, high-value bridge targets, open-questions list, human-readable, revisable without tooling.
    

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