Automatic freeform graphs design

SkillCloud & infra

Use when a user wants a looser conceptual graph for exploratory work. Generates a freeform conceptual graph that the user can explore. Not for remote, credential, publish, deploy, or irreversible changes.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Automatic freeform graphs design skill

What this skill tells your AI

The instructions your AI receives, as published by outlinedriven/outline-driven-development in .devin/skills/automatic-freeform-graphs-design/SKILL.md and read by ahel’s review.

Contract

FieldBound contract
TriggerUser wants a looser conceptual graph for exploratory work.
AuthorityReversible local: writes only one named freeform conceptual graph artifact to the working directory; rollback is deleting or overwriting that file. No remote mutation.
Side effectA freeform conceptual graph artifact for exploration, written to the local filesystem.
DoneA 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.

Signals

GitHub stars
52
Forks
9
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
automatic-freeform-graphs-design
Source
github.com/outlinedriven/outline-driven-development
Automatic freeform graphs design: Skill · ahel