Archify Diagrams
tt-a1i/archify
Creates interactive architecture, workflow, sequence, data-flow and lifecycle diagrams as standalone HTML with inline SVG, themes and image or video export.
Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief.
$ npx skills add shareAI-lab/lab-skills --skill understanding-first-report -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install shareAI-lab/lab-skills understanding-first-report --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/shareAI-lab/lab-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/research-analysis/understanding-first-report .claude/skills/understanding-first-report && rm -rf skills-srcUse ~/.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/
Install the "understanding-first-report" agent skill from https://github.com/shareAI-lab/lab-skills/tree/main/research-analysis/understanding-first-report into .claude/skills/understanding-first-report/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "understanding-first-report", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/shareAI-lab/lab-skills/tree/main/research-analysis/understanding-first-reportType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add shareAI-lab/lab-skills --skill understanding-first-report -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install shareAI-lab/lab-skills understanding-first-report --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shareAI-lab/lab-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/research-analysis/understanding-first-report .agents/skills/understanding-first-report && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "understanding-first-report" agent skill from https://github.com/shareAI-lab/lab-skills/tree/main/research-analysis/understanding-first-report into .agents/skills/understanding-first-report/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "understanding-first-report", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add shareAI-lab/lab-skills --skill understanding-first-report -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install shareAI-lab/lab-skills understanding-first-report --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shareAI-lab/lab-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/research-analysis/understanding-first-report .cursor/skills/understanding-first-report && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "understanding-first-report" agent skill from https://github.com/shareAI-lab/lab-skills/tree/main/research-analysis/understanding-first-report into .cursor/skills/understanding-first-report/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "understanding-first-report", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/shareAI-lab/lab-skills.git --path research-analysis/understanding-first-report--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add shareAI-lab/lab-skills --skill understanding-first-report -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install shareAI-lab/lab-skills understanding-first-report --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shareAI-lab/lab-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/research-analysis/understanding-first-report .gemini/skills/understanding-first-report && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "understanding-first-report" agent skill from https://github.com/shareAI-lab/lab-skills/tree/main/research-analysis/understanding-first-report into .gemini/skills/understanding-first-report/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "understanding-first-report", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install shareAI-lab/lab-skills understanding-first-reportInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add shareAI-lab/lab-skills --skill understanding-first-report -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/shareAI-lab/lab-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/research-analysis/understanding-first-report .github/skills/understanding-first-report && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "understanding-first-report" agent skill from https://github.com/shareAI-lab/lab-skills/tree/main/research-analysis/understanding-first-report into .github/skills/understanding-first-report/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "understanding-first-report", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add shareAI-lab/lab-skills --skill understanding-first-report -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install shareAI-lab/lab-skills understanding-first-report --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shareAI-lab/lab-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/research-analysis/understanding-first-report .opencode/skills/understanding-first-report && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "understanding-first-report" agent skill from https://github.com/shareAI-lab/lab-skills/tree/main/research-analysis/understanding-first-report into .opencode/skills/understanding-first-report/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "understanding-first-report", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
understanding-first-reportReconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief.
Understanding First Report is an agent skill from shareAI-lab/lab-skills. Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when…
Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).
It sits in Development, covering Diagrams. The repository describes itself as: Skills distilled from the Lab's real work and collaboration practices. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit becee99. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown and diff).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Understanding First Report loads about 4.3k tokens when it runs. Until then it costs about 158 tokens; SKILL.md has 2,041 words of instructions outside code blocks.
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.
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.
The full file from shareAI-lab/lab-skills at commit becee99, republished under its Apache-2.0 licence (© shareAI-lab). 2,041 words, ~4,309 tokens.
.claude/skills/understanding-first-report/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Turn completed or partially completed work into a low-friction cognitive handoff.
This skill governs reporting only. It does not govern planning, progress updates, approvals, tool execution, scheduling, or coordination while work is in progress.
The report should let the reader recover:
Treat these as observable acceptance criteria:
Understand implied intent -> The reader rarely repairs the frame.
Read the moment -> Depth and format fit the current need.
Be clear and complete -> The judgment is easy to find and use.
Be warm, not flattering -> The report has judgment and preserves choice.
Write one coherent line -> Structure serves meaning, not a template.When these qualities conflict with richer formatting, protect the qualities. A polished report still fails if the reader must correct its interpretation, hunt for the conclusion, or rewrite the answer.
These qualities reduce six kinds of reader friction:
re-entry · meaning · navigation
verification · cleanup · decisionUse the smallest shape that completes the handoff.
These are lenses, not required headings. Combine modes only when the reader genuinely needs both.
Before substantive analysis, quote the relevant user wording. This restores continuity before summary, interpretation, or judgment.
A compact shape is enough:
> **Earlier request**
> Relevant original wording.
>
> **Scope change**
> Wording that changed the task.
>
> **Current request**
> The question this report answers now.After the quote, provide a short synthesis. Do not replay the transcript.
For repeated or long-running work, emphasize why the new report matters now:
STILL TRUE CHANGED INVALIDATED
accepted facts moved scope reversed assumption
NEW STILL OPEN
new evidence remaining blockerInclude only deltas that change context, confidence, choice, risk, or action. Do not reopen an accepted decision unless new evidence invalidates it.
First inventory the explicit questions. Then identify:
A list prevents omissions. A spatial map exposes structure.
Earlier context Current questions
┌──────────────────┐ ┌──────────────────┐
│ accepted choices │ │ explicit asks │
│ relevant changes │ │ current limits │
└─────────┬────────┘ └─────────┬────────┘
└────────────┬────────────────┘
▼
┌──────────────────┐
│ Core conflict │
└────────┬─────────┘
│
┌────────────┴────────────┐
▼ ▼
deeper question unmet needTreat a deeper need as inference until the conversation or evidence supports it. If an uncertain inference would change the answer, show the fork.
Useful insight changes the reader's mental model. It may reveal:
Do not force novelty. A simple, well-supported answer is better than artificial depth.
Organize the material into three lanes:
The main line should remain understandable without the detail line. Remove material that does not change the mental model, decision, risk, or next action.
For a substantial decision report, the first screen should usually reveal:
original ask · current frame · verdict or readiness stateUse only the evidence labels that help the decision:
If the main question is not ready:
Do not let visual confidence or smooth prose make weak evidence look settled.
When evidence is ready, give one clear judgment with its confidence, central contradiction, and boundary. Include alternatives only when they materially change cost, ownership, risk, or the decision.
Separate basis, judgment, and choice:
┌──────────────────┐ ┌──────────────────┐
│ Verified basis │─────>│ Report judgment │
│ fact · test · gap│ │ reason + limit │
└──────────────────┘ └────────┬─────────┘
│
┌─────────────┴─────────────┐
▼ ▼
human choice reversal condition
when one remains what would change itUse a visual only when it replaces explanation or makes a relationship materially easier to see.
Good uses include:
One diagram should answer one question. Prefer two small diagrams over one overloaded picture.
Use spatial composition deliberately:
Avoid long vertical chains. Keep roughly three sequential nodes on one axis, then group, branch, or split the visual.
Do not place standalone arrow blocks between ordinary sections. Document order already provides vertical flow.
Natural prose is the default. Add structure when it lowers navigation or verification cost.
Label provenance immediately above code:
Never combine verified source code and recommended design into a fictional official API.
Bold only short plain-text anchors. Keep edge punctuation and emoji outside the bold markers.
- **Key judgment:** Keep one state owner.
+ 🎯 **Key judgment**: Keep one state owner.A small, stable emoji vocabulary may improve navigation:
Emoji is optional. It never replaces a label, severity, owner, or status. Avoid emoji inside fixed-width diagrams unless alignment is checked.
Design the main path for about two to three minutes of reading when the material allows it. Treat this as an attention target, not a quota.
Useful editing heuristics:
When over budget, cut in this order:
vendor inventory
-> secondary history
-> repeated examples
-> edge cases that do not change the choice
-> implementation detail that can waitProtect the question, real decision, mechanism, decisive evidence, main risk, and next action.
Write like a thoughtful colleague: calm, attentive, candid, and kind.
Warmth comes from accurate listening, fair criticism, honest uncertainty, and useful action. It is not praise.
Avoid automatic compliments, therapy language, repeated apologies, canned empathy, performative certainty, and long emotional prefaces.
The chat answer should carry the main question, verdict or readiness state, mechanism, decisive evidence, risk, and next action.
Create a separate artifact only when the user requests it, the workflow requires it, or deep evidence cannot be preserved responsibly in chat. A linked file may be an appendix; it must not replace the report.
Do not publish or attach raw research notes, source catalogs, private discussion, internal reasoning, or conversation history unless the user explicitly requests that exact artifact. Quote only the user wording needed to understand the current report.
End with:
If the user requested a report rather than authorization to act, keep the next step as a recommendation. Reporting does not authorize an external change.
When a failure signal appears, simplify the report and return to the reader's real decision.
These are firm because crossing them creates a false or unsafe report:
Before sending, ask:
Use these prompts as aids to judgment, not as a visible checklist.
© shareAI-lab, 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
SKILL.md and 1 other file in research-analysis/understanding-first-report of shareAI-lab/lab-skills.
Open the folder on GitHubat commit becee99
Understanding First Report 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Understanding First Report this skillshareAI-lab/lab-skills | 314 | — | ~4.3k | Automated safety check: Pass | Apache-2.0 | |
| Archify Diagramstt-a1i/archify | 81k | — | ~2.9k | Automated safety check: Pass | MIT | |
| JSON Canvasheyitsnoah/claudesidian | 2.6k | 18 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Diagram Designcathrynlavery/diagram-design | 47k | 1 repos | ~7.5k | Automated safety check: Pass | MIT | |
| Fireworks Tech Graphtisfeng/Easydict | 15k | 1 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Excalidraw Diagramcoleam00/excalidraw-diagram-skill | 5k | 2 repos | ~6.1k | Automated safety check: Pass | None |
tt-a1i/archify
Creates interactive architecture, workflow, sequence, data-flow and lifecycle diagrams as standalone HTML with inline SVG, themes and image or video export.
heyitsnoah/claudesidian
Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections.
cathrynlavery/diagram-design
Creates branded diagrams, from architecture, flowchart and sequence to charts and maps, as self-contained HTML with inline SVG, with import from draw.io, Mermaid and Excalidraw.
tisfeng/Easydict
Create precise SVG technical diagrams, export PNG or offline HTML, and animate supported semantic SVGs to GIF.
coleam00/excalidraw-diagram-skill
Create Excalidraw diagram JSON files that make visual arguments.
Agents365-ai/drawio-skill
Creates and edits editable draw.io diagrams from descriptions, code, infrastructure files, SQL and API schemas, with sync, review, test and export tools.
shareAI-lab/lab-skills
Helps design and build AI agents for any domain around a minimal loop of capabilities, knowledge and context, adding planning or subagents only when needed.
shareAI-lab/lab-skills
Deeply research technical architecture, source code, mechanisms, SDKs, frameworks, project comparisons, and system-design options across repositories, history, official docs, issues, discussions…
shareAI-lab/lab-skills
Research why neural architectures and training methods work through forward computation, geometry, gradients, optimization dynamics, historical experiments, and competing explanations.
shareAI-lab/lab-skills
Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor.
shareAI-lab/lab-skills
Evaluate Agent Skill design quality with an opinionated, practice-derived rubric informed by public specifications and examples.
shareAI-lab/lab-skills
Transform an AI agent into a disciplined software development partner with strong judgment, transparent decisions, proportionate verification, and craftsmanship.
Categories
Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Understanding First Report is an agent skill from shareAI-lab/lab-skills. Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief.
Understanding First Report fits situations like: A reader must re-enter earlier context; understand the real question and its relationships; distinguish verified facts from judgment and open evidence; make a decision without reconstructing the work log.
Run `npx skills add shareAI-lab/lab-skills --skill understanding-first-report -a claude-code`. Or copy the skill folder (research-analysis/understanding-first-report in shareAI-lab/lab-skills) into .claude/skills/understanding-first-report in your project. Claude Code loads it when a task matches its description.
Run `npx skills add shareAI-lab/lab-skills --skill understanding-first-report -a codex`. Or copy the skill folder (research-analysis/understanding-first-report in shareAI-lab/lab-skills) into .agents/skills/understanding-first-report in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add shareAI-lab/lab-skills --skill understanding-first-report -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/understanding-first-report, .gemini/skills/understanding-first-report, .github/skills/understanding-first-report and .opencode/skills/understanding-first-report in your project.
SKILL.md names no scripts, command-line tools or credentials: Understanding First Report is instructions for the agent only.
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.
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.
Understanding First Report 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.
About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Understanding First Report: Archify Diagrams (tt-a1i/archify, 81k stars), JSON Canvas (heyitsnoah/claudesidian, 2.6k stars), Diagram Design (cathrynlavery/diagram-design, 47k stars) and Fireworks Tech Graph (tisfeng/Easydict, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
shareAI-lab (a GitHub organization) maintains it in shareAI-lab/lab-skills, which has 314 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on September 16, 2026.
Source: shareAI-lab/lab-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.