Agent skill

Knowledge Construction

by JetXu-LLM in JetXu-LLM/DocMason

Write bilingual Phase 3 knowledge objects for staged DocMason sources from rendered evidence and extracted structure.

Apache-2.0Auto-check passedKnowledge Management

Install Knowledge Construction

skills CLI
$ npx skills add JetXu-LLM/DocMason --skill knowledge-construction -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install JetXu-LLM/DocMason knowledge-construction --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/JetXu-LLM/DocMason.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/canonical/knowledge-construction .claude/skills/knowledge-construction && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
knowledge-construction
GitHub stars
147
Token cost
~1.8k tokens
SKILL.md length
906 words
Files
2
Skills in repo
15
Repo updated
First seen
Licence
Apache-2.0

At a glance

Write bilingual Phase 3 knowledge objects for staged DocMason sources from rendered evidence and extracted structure.

  • Works in 12 steps: If the current sync result or workflow… → Read… → If… → …
  • Tasks that involve Translation
  • SKILL.md covers Required Capabilities, Procedure, Escalation Rules and Completion Signal, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Knowledge Construction is an agent skill from JetXu-LLM/DocMason. Write bilingual Phase 3 knowledge objects for staged DocMason sources from rendered evidence and extracted structure.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `workflow.json`).

It sits in Knowledge Management, covering Translation. The repository describes itself as: DocMason is a repo-native agent that turns your complex office files into a local LLM knowledge base and your second brain. The repo is the app. Codex is the runtime. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Translation

Example prompts

  • “/knowledge-construction”

Workflow steps

12 steps, taken from the first numbered list in SKILL.md.

  1. If the current sync result or workflow handoff provides a governed follow-up packet path such as lane_b_follow_up.work_path, open that…
  2. Read knowledge_base/staging/pending_work.json.
  3. If knowledge_base/staging/hybrid_work.json exists, treat that file as the authoritative hard-artifact overlay queue inside the current…
  4. Work only on the staged items selected by the governed packet when one exists; otherwise work only on the staged items listed in…
  5. For each assigned pending item, open
  6. Build the source semantics from the richest published evidence available instead of defaulting to flattened text
  7. Write knowledge.json with the required bilingual fields and only cite real evidence-unit IDs from the matching source.
  8. Write summary.md with
  9. When the staged source includes high-value hybrid candidates and the environment can inspect renders, write additive…
  10. Avoid placeholders, speculative citations, and unsupported related-source links.
  11. Treat derived_affordances.json as a published sidecar rather than scratch output.
  12. When evidence is weak, say so explicitly in known_gaps, ambiguities, confidence notes, or overlay uncertainty notes instead of inventing…

What it can do on your machine

Read from SKILL.md and the folder at commit 362417b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Knowledge Construction loads about 1.8k tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 906 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~35
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from JetXu-LLM/DocMason at commit 362417b, republished under its Apache-2.0 licence (© JetXu-LLM). 906 words, ~1,813 tokens.

Download SKILL.mdSave it as .claude/skills/knowledge-construction/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
knowledge-construction
description
Write bilingual Phase 3 knowledge objects for staged DocMason sources from rendered evidence and extracted structure.

Knowledge Construction

Use this skill when docmason sync has prepared staged evidence and the agent must write knowledge.json, summary.md, or additive semantic_overlay/<unit-id>.json sidecars.

This is an internal follow-on workflow behind knowledge-base-sync. In the normal path, the main agent hands this workflow a bounded governed follow-up packet from the latest sync result before any source-level authoring starts. Ordinary users should not need to invoke it by name.

Required Capabilities

  • local file access
  • shell or command execution
  • ability to inspect rendered images and extracted artifacts

If the agent cannot inspect rendered images, stop and explain that the environment is not capable enough for multimodal knowledge construction.

Procedure

  1. If the current sync result or workflow handoff provides a governed follow-up packet path such as lane_b_follow_up.work_path, open that packet first.
    • prefer the bounded packet path handed off by knowledge-base-sync
    • treat that packet as the authoritative source and unit selection scope for this pass
    • do not scan shared-job directories to invent a job id when the bounded packet path is already available
  2. Read knowledge_base/staging/pending_work.json.
  3. If knowledge_base/staging/hybrid_work.json exists, treat that file as the authoritative hard-artifact overlay queue inside the current bounded scope.
  4. Work only on the staged items selected by the governed packet when one exists; otherwise work only on the staged items listed in pending_work.json. Treat each pending source or interaction memory as an independent bounded write scope.
    • when both a governed packet and hybrid_work.json are present, use the packet's selected sources and units as the outer boundary and the queued hybrid targets as the inner overlay queue
  5. For each assigned pending item, open:
    • work_item.json
    • source_manifest.json
    • evidence_manifest.json
    • knowledge_base/staging/hybrid_work.json when the current sync result reported candidate-prepared
    • artifact_index.json when present
    • pdf_document.json when present
    • spreadsheet_workbook.json when present
    • spreadsheet_sheet/*.json when present
    • visual_layout/*.json when present
    • derived_affordances.json when it already exists
    • extracted text and structure files
    • rendered assets referenced by the evidence manifest
    • when present, the staged interaction-specific context file such as interaction_context.json
  6. Build the source semantics from the richest published evidence available instead of defaulting to flattened text:
    • for spreadsheets, prefer workbook, sheet, table, chart, metric, dimension, time-axis, hidden-sheet, and formula summaries over raw cell dumps
    • for PDF and PPTX, prefer section paths, captions, continuation links, procedure spans, region roles, charts, tables, pictures, connectors, groups, and major regions over page-level text alone
    • when the real support is artifact-level, include artifact_id in the citation instead of citing only the parent unit_id
  7. Write knowledge.json with the required bilingual fields and only cite real evidence-unit IDs from the matching source.
  8. Write summary.md with:
    • # <title>
    • a line that mentions the source ID
    • ## English Summary
    • ## Source-Language Summary
  9. When the staged source includes high-value hybrid candidates and the environment can inspect renders, write additive semantic_overlay/<unit-id>.json sidecars only for units that are inside the current bounded scope and queued in hybrid_work.json.
    • prefer overlay work where deterministic structure is already rich but cross-region or multimodal semantics are still missing
    • keep the hard-artifact boundary intact:
      • use the queued target_artifact_ids
      • use the queued target_focus_render_assets first
      • use the queued target_render_assets and target_render_page_span
      • for image-only or scanned PDF pages, treat the published page-image artifact as the first-class target instead of pretending the text layer is enough
      • if the baseline focus render is still not legible enough, use the targeted hi-res focus-render helper for that artifact instead of rerendering the whole source
    • overlays must remain additive
    • do not rewrite deterministic sidecars such as artifact_index.json, visual_layout/*.json, spreadsheet_*, or pdf_document.json
    • bind overlay claims to consumed inputs, artifact ids when available, explicit uncertainty notes, and the current freshness contract:
      • origin
      • source_fingerprint
      • unit_evidence_fingerprint
      • covered_slots
      • blocked_slots
  10. Avoid placeholders, speculative citations, and unsupported related-source links.
  11. Treat derived_affordances.json as a published sidecar rather than scratch output.
Show full SKILL.md (299 more words)Show less
  • the baseline affordance sidecar is generated deterministically by the repo
  • if you enrich it, keep descriptors compact, evidence-backed, grouped by channel, and explicitly derived rather than source-authored fact
  1. When evidence is weak, say so explicitly in known_gaps, ambiguities, confidence notes, or overlay uncertainty notes instead of inventing certainty.
  2. After all assigned staged sources are complete, return control to the main agent so it can rerun docmason sync --json or docmason validate-kb --json.

Escalation Rules

  • If a staged source or interaction memory requires render inspection and the environment cannot inspect renders, stop that item and report the blocker directly.
  • If a cross-source relation is uncertain, omit it rather than guessing.
  • If a chart, table, diagram, or region claim cannot be supported by the published artifacts, write the uncertainty explicitly rather than laundering it through a source-level summary.
  • If hybrid_work.json says a source is candidate-prepared, do not declare the governed follow-up complete merely because deterministic publication already succeeded.
  • Do not publish, validate, or sign off the final sync result from inside this workflow. That judgment belongs to the main agent.

Completion Signal

  • The workflow is complete when every assigned staged source in the current bounded scope has updated knowledge.json and summary.md, or when a concrete capability blocker has been surfaced to the main agent.

Notes

  • summary_source may equal summary_en when the source itself is English.
  • related_sources should stay light in Phase 3. Only add a relation when the source evidence clearly supports it.
  • semantic_overlay is for cross-region or multimodal semantics that add value beyond the deterministic substrate. It is not a second primary truth surface.
  • If extraction is weak, record that weakness explicitly in known_gaps or ambiguities. Do not invent certainty.
  • Interaction memories should remain explicit about lower trust tier, interaction-derived provenance, and the distinction from authored source documents.

© JetXu-LLM, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/canonical/knowledge-construction of JetXu-LLM/DocMason.

  • SKILL.md
  • workflow.json

Open the folder on GitHubat commit 362417b

Compare with similar skills

Knowledge Construction next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Knowledge Construction compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Knowledge Construction this skillJetXu-LLM/DocMason147—~1.8kAutomated safety check: PassApache-2.0
Ncats AraxK-Dense-AI/scientific-agent-skills48k2 repos~2.3kAutomated safety check: NotesMIT
Cet Skillmingchen666/Reviva237—~6.4kAutomated safety check: PassMIT
Soulaeonfun/soul.md6872 repos~1.1kAutomated safety check: PassMIT
Humanities Writing Companiontizzy916/humanities-writing-companion434—~3.4kAutomated safety check: PassCC-BY-NC-4.0
Academic Prose De-AI Editorheise3/academic-deai225—~1.4kAutomated safety check: PassMIT

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More from JetXu-LLM/DocMason

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  • Grounded Answer

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  • Grounded Composition

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  • Knowledge Base Sync

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Questions about Knowledge Construction

What does Knowledge Construction do?

Write bilingual Phase 3 knowledge objects for staged DocMason sources from rendered evidence and extracted structure. Knowledge Construction is an agent skill from JetXu-LLM/DocMason. Write bilingual Phase 3 knowledge objects for staged DocMason sources from rendered evidence and extracted structure.

When should I use Knowledge Construction?

Knowledge Construction fits situations like: tasks that involve Translation.

How do I install Knowledge Construction in Claude Code?

Run `npx skills add JetXu-LLM/DocMason --skill knowledge-construction -a claude-code`. Or copy the skill folder (skills/canonical/knowledge-construction in JetXu-LLM/DocMason) into .claude/skills/knowledge-construction in your project. Claude Code loads it when a task matches its description.

How do I install Knowledge Construction in Codex?

Run `npx skills add JetXu-LLM/DocMason --skill knowledge-construction -a codex`. Or copy the skill folder (skills/canonical/knowledge-construction in JetXu-LLM/DocMason) into .agents/skills/knowledge-construction in your project. Codex loads it when a task matches its description.

Can I use Knowledge Construction in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JetXu-LLM/DocMason --skill knowledge-construction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/knowledge-construction, .gemini/skills/knowledge-construction, .github/skills/knowledge-construction and .opencode/skills/knowledge-construction in your project.

What does Knowledge Construction need to run?

SKILL.md names no scripts, command-line tools or credentials: Knowledge Construction is instructions for the agent only.

Does Knowledge Construction access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Knowledge Construction safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Knowledge Construction use?

Knowledge Construction is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Knowledge Construction use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Knowledge Construction?

Skills that share tags, products or a category with Knowledge Construction: Ncats Arax (K-Dense-AI/scientific-agent-skills, 48k stars), Cet Skill (mingchen666/Reviva, 237 stars), Soul (aeonfun/soul.md, 687 stars) and Humanities Writing Companion (tizzy916/humanities-writing-companion, 434 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Knowledge Construction?

JetXu-LLM (a GitHub user) maintains it in JetXu-LLM/DocMason, which has 147 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on September 30, 2026.

Source: JetXu-LLM/DocMason on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.