Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/DojoGenesis/plugins --skill diagramming-code명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Run a bounded kata roll over the project's bring queue — start a session with an explicit target (reps or minutes), surface and stage exactly one bring per tick, log the outcome only on the human's word, then advance. Use when the user says "start a roll", "roll for N reps", "do a 25-minute roll", "tick", "next rep", "resolve this tick", "skip this tick", or "pause/resume/end the roll".
Set up the kata roll in a project that already runs (or is starting) the bring loop — confirm bring/ exists, explain where the roll's own ledger lives, walk through the opt-in timer, and make the two-plugin coexistence with bring-loop explicit. Use when the user says "set up kata-harness", "install the roll plugin", "add rolls on top of bring-loop", or asks how the timed roll relates to their daily bring.
Produces markdown memory artifacts (conversation summaries, seed files, philosophical reflections, doc updates) and a dated compression log by distilling a long conversation into its essential decisions and learnings. For the lighter, routine end-of-session wrap-up, use `session-compression` instead — this skill is for a long conversation that needs the fuller multi-artifact treatment. Use when: "compress this context", "distill this conversation", "create a memory artifact", "condense this history", "extract key wisdom before handoff".
SOC 직업 분류 기준
SKILL.md 표시 중
| name | diagramming-code |
| description | >. Trigger phrases: "invoke this skill". |
Generates Mermaid diagrams from Trailmark's code graph. A pre-made script handles Mermaid syntax generation; Claude selects the diagram type and parameters.
trailmark skill)genotoxic skill)trailmark must be installed. If uv run trailmark fails, run:
uv pip install trailmark
DO NOT fall back to hand-writing Mermaid from source code reading. The script uses Trailmark's parsed graph for accuracy. If installation fails, report the error to the user.
uv run {baseDir}/scripts/diagram.py \
--target {targetDir} --type call-graph \
--focus main --depth 2
Output is raw Mermaid text. Wrap in a fenced code block:
```mermaid
flowchart TB
...
```
├─ "Who calls what?" → --type call-graph
├─ "Class inheritance?" → --type class-hierarchy
├─ "Module dependencies?" → --type module-deps
├─ "Class members and structure?" → --type containment
├─ "Where is complexity highest?" → --type complexity
└─ "Path from input to function?" → --type data-flow
For detailed examples of each type, see references/diagram-types.md.
Diagram Progress:
- [ ] Step 1: Verify trailmark is installed
- [ ] Step 2: Identify diagram type from user request
- [ ] Step 3: Determine focus node and parameters
- [ ] Step 4: Run diagram.py script
- [ ] Step 5: Verify output is non-empty and well-formed
- [ ] Step 6: Embed diagram in response
Step 1: Run uv run trailmark analyze --summary {targetDir}. Install
if it fails. Then run pre-analysis via the programmatic API:
from trailmark.query.api import QueryEngine
engine = QueryEngine.from_directory("{targetDir}", language="{lang}")
engine.preanalysis()
Pre-analysis enriches the graph with blast radius, taint propagation,
and privilege boundary data used by data-flow diagrams.
Step 2: Match the user's request to a --type using the decision tree
above.
Step 3: For call-graph and data-flow, identify the focus function.
Default --depth 2. Use --direction LR for dependency flows.
Step 4: Run the script and capture stdout.
Step 5: Check: output starts with flowchart or classDiagram,
contains at least one node. If empty or malformed, consult
references/mermaid-syntax.md.
Step 6: Wrap output in ```mermaid ``` code fence.
uv run {baseDir}/scripts/diagram.py [OPTIONS]
| Argument | Short | Default | Description |
|---|---|---|---|
--target | -t | required | Directory to analyze |
--language | -l | python | Source language |
--type | -T | required | Diagram type (see above) |
--focus | -f | none | Center diagram on this node |
--depth | -d | 2 | BFS traversal depth |
--direction | TB | Layout: TB (top-bottom) or LR (left-right) | |
--threshold | 10 | Min complexity for complexity type |
# Call graph centered on a function
uv run {baseDir}/scripts/diagram.py -t src/ -T call-graph -f parse_file
# Class hierarchy for a Rust project
uv run {baseDir}/scripts/diagram.py -t src/ -l rust -T class-hierarchy
# Module dependency map, left-to-right
uv run {baseDir}/scripts/diagram.py -t src/ -T module-deps --direction LR
# Class members
uv run {baseDir}/scripts/diagram.py -t src/ -T containment
# Complexity heatmap (threshold 5)
uv run {baseDir}/scripts/diagram.py -t src/ -T complexity --threshold 5
# Data flow from entrypoints to a specific function
uv run {baseDir}/scripts/diagram.py -t src/ -T data-flow -f execute_query
Direction: Use TB (default) for hierarchical views, LR for
left-to-right flows like dependency chains.
Depth: Increase --depth to see more of the call graph. Decrease to
reduce clutter. The script warns if the diagram exceeds 100 nodes.
Focus: Always use --focus for call-graph on non-trivial codebases.
For data-flow, omitting focus auto-targets the top 10 complexity hotspots.