# Structure Of Knowledge > Evidence-grounded field-mapping and agent-collaboration harness for producing reusable knowledge scaffolds, literature ladders, curricula, report plans, visual knowledge maps, textbook architectures, and downstream research artifacts. Use when the user asks for SoK, structure of knowledge, a field map, research curriculum, literature ladder, deep research report, domain research delegation, or a… - **Type:** Skill - **Install:** `agentstack add skill-perhyper-sok-structure-of-knowledge` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [perhyper](https://agentstack.voostack.com/s/perhyper) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [perhyper](https://github.com/perhyper) - **Source:** https://github.com/perhyper/SoK/tree/main/structure-of-knowledge ## Install ```sh agentstack add skill-perhyper-sok-structure-of-knowledge ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Structure of Knowledge ## Overview Use this skill to turn a broad learning or research goal into a reusable map of a field. The scaffold should show how the field is organized, how claims are warranted, what literature anchors it, and how a learner or downstream system can progress from orientation to purposeful work. The core idea is inspired by Jerome Bruner and the structure-of-knowledge curriculum tradition: decompose a domain into the concepts, representations, methods, and warrants that make it intelligible, then recompose that structure into a scaffold for learning, research, and downstream generation. ## Reference Loading For a full SoK task, read these references before drafting the final answer: - `references/research-protocol.md` for source gathering, evidence ranking, and currentness checks. - `references/output-contract.md` for the report, curriculum, and visualization package. - `references/agent-collaboration.md` when coordinating agent work, creating a textbook plan, planning model-tuning data, or downloading source packs. For a narrow answer, use this file and load only the reference that matches the missing detail. ## Workflow 1. Frame the request. - Identify the field, learner profile, target depth, time budget, language preference, and desired output format. - If the field is broad, first produce a domain decomposition: major subfields, shared foundations, divergent methods, and possible scoped paths. Ask the user to choose only when the path would materially change the report; otherwise state a scoped assumption. - If the field or goal is ambiguous enough to change the curriculum, ask one concise clarifying question. Otherwise state assumptions and continue. - Build a preliminary domain profile rather than treating classification as a final label. Estimate the relevant lenses: formal/well-structured, ill-structured, professional/practice-based, empirical, computational, instrument- or infrastructure-bound, emerging, interdisciplinary, or mixed. - Treat this profile as a source-review hypothesis. It should reveal which knowledge-production modes must be inspected, not restrict the field to an existing curriculum or a single legacy category. 2. Research the field. - Use current web research or user-provided corpora by default because frontier issues, recent surveys, standards, and tools change. - Prefer primary sources, field handbooks, authoritative textbooks, major survey/review papers, seminal papers, official standards, respected syllabi, and venue or society materials. - For instrument- or infrastructure-bound fields, include official review bodies, facilities, datasets, collaborations, standards, strategic reports, and statistical conventions as part of the source stack. - Build a source role probe rather than enforcing a fixed source quota. Required roles usually include orientation or boundary evidence, canonical foundations, method or warrant evidence, and pedagogical sequence evidence. Conditional roles include recent frontier sources, datasets or standards, primary corpora, canonical cases, and infrastructure sources when the domain or user goal requires them. - Mark irrelevant source roles as waived with a short rationale. For example, a core Latin-language curriculum may waive recent frontier surveys, while a research-fluency path in number theory should include frontier or problem-list sources. - Treat existing syllabi and curricula as evidence of stabilized pedagogical consensus, not as the boundary of the field. - Build a source stack across roles and layers: scholarly orientation, canonical foundations, core methods, representative applications or cases, frontier surveys, debates, open problems, standards, datasets, or corpora as appropriate. - Track what role each source plays in the scaffold or learning sequence, not only its bibliographic details. - For every recommended source, record type, identifier, access route, free/paid/paywalled status, and budget estimate or `budget unknown; library preferred`. 3. Extract the structure of knowledge. - Name the field's generative questions, core objects, representations, transformations, methods, standards of evidence, canonical examples, and failure modes. - Separate substantive structure (concepts, models, claims, objects) from syntactic structure (how the field warrants, disputes, and revises claims). - For formal domains, capture proof techniques, canonical constructions, counterexample practices, classification problems, and problem-list traditions. - For empirical or infrastructure-bound domains, capture instruments, measurement regimes, data products, collaborations, uncertainty/statistical practices, and review infrastructures. - Distinguish prerequisites from recurring fundamental ideas. A prerequisite is needed before a topic; a fundamental idea returns throughout the spiral at higher sophistication. - Identify threshold concepts, common misconceptions, and places where newcomers to the field confuse vocabulary with understanding. 4. Design the learning spiral. - Organize the curriculum into repeated passes over core ideas: orientation, core grammar, research fluency, and frontier participation. - Use advance organizers, concept maps, and worked scholarly performances to make the field's structure visible before asking for independent synthesis. - For each module, include purpose, essential questions, readings, practice tasks, artifacts to produce, dependencies, and criteria for progress. - For ill-structured domains, use cases, schools of thought, debates, and interpretive lenses rather than forcing a single hierarchy. 5. Deliver the SoK package. - Keep internal scaffold artifacts and human-facing reports separate. Scaffolds may include learner profile, original goal, assumptions, placeholders, and quality notes; final reports must not expose that machinery. - Treat `sok-report.json` as the structured source of truth for downstream tooling. Markdown scaffolds and canonical Markdown reports are bounded inputs; HTML is a rendered public view. - Provide a narrative report and visualization only when the structured relations justify it. Graph views should be selected from `relations` and `visual_views`; Mermaid is optional and not required. 6. Quality check. - Verify that claims about current literature, active debates, standards, or tools are sourced with dates. - Verify that recommended readings include access metadata and usable acquisition routes. - Ensure the curriculum starts from the learner's actual entry point but still preserves the field's intellectual integrity. - Confirm the output is not a flat topic list: it must explain relations, hierarchy or network structure, inquiry methods, and progression. ## Helper Script Use the Rust CLI for agent-facing workspaces, source manifests, access audits, scaffolds, and safe downloads: ```bash "${CODEX_HOME:-$HOME/.codex}/skills/structure-of-knowledge/bin/sok" init \ --field "computational neuroscience" \ --learner "software engineer new to neuroscience" \ --goal "build a source-backed path into the field" \ --mode research \ --weeks 12 \ --out /tmp/sok-computational-neuroscience ``` From a repository checkout, run `make build` once and use `structure-of-knowledge/bin/sok`. Useful subcommands: - `brief`: create an agent handoff brief. - `scaffold`: create a domain-aware SoK Markdown starter inferred from field type. - `handoff-report`: wrap a scaffold in next-agent instructions for completing a polished human-reader report. - `source-template`: create a source manifest. - `audit-sources`: verify access metadata and curricular roles. - `download-sources`: download only sources with default-allowed access statuses by default. - `ingest last`: normalize the current run's source manifest into cataloged evidence JSONL without treating it as reviewed claim support. - `lint`: check scaffold or final Markdown plus source/evidence files before JSON export. - `export-json`: convert bounded scaffold or final Markdown plus source/evidence files into `sok-report.json`. - `validate-report`: validate `sok-report.json` reliability gates; use `--strict` before rendering or publication. - `render-html`: render a validated `human_report` JSON file to self-contained local HTML. - `specificity`: make an underspecified domain task concrete enough to execute. The CLI is not a substitute for research. It provides a stable execution frame so the agent can fill it with sourced findings. Recommended bounded lanes: - Scaffold lane: create or receive scaffold Markdown, maintain `sources.csv`, optionally run `sok ingest last`, run `sok lint --stage scaffold`, then `sok export-json --stage scaffold`. This JSON may contain `internal_context`; do not present it as a finished report and do not render it to HTML. - Final-report lane: complete a canonical human report Markdown file, review evidence into JSONL entries, run `sok lint --stage final`, `sok export-json --stage final`, `sok validate-report --strict`, then `sok render-html`. Only this lane produces human-facing HTML. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [perhyper](https://github.com/perhyper) - **Source:** [perhyper/SoK](https://github.com/perhyper/SoK) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-perhyper-sok-structure-of-knowledge - Seller: https://agentstack.voostack.com/s/perhyper - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.