decompose
Break down a goal, process, or workflow into MECE components with Agent SDK mapping.
Skill by fblissjr
Chalk Labs
Need help deploying decompose?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/fblissjr-decompose
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: spreadsheets, productivity, agent-building
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/decompose
What's inside
- Skill name
decompose- Description
- Break down a goal, process, or workflow into MECE components with Agent SDK mapping. Use when user says "decompose", "break down this process", "MECE analysis", "create a decomposition tree", or pastes a JSON/YAML/CSV workflow export to analyze.
- License
- Apache-2.0
- Instructions
- 40 lines in SKILL.md · view on GitHub
Agent Skills spec issues
- files: referenced but missing: references/output_schema.md
Files (1)
- SKILL.md
SKILL.md (first lines)
# /decompose
Decompose a process, goal or workflow into MECE components, producing a human-readable tree and JSON that maps to agent primitives. The method, limits and scoring are in the **mece-decomposer** skill and its references; this skill adds the input rules and the output shape.
Usage: `/decompose <what to decompose>`, or `/decompose` followed by a pasted JSON, YAML or CSV export from a workflow tool.
<input_rules>
- **Structured input gets its schema confirmed before any cut.** For JSON, XML, YAML, CSV or any tool export, ask what the data represents, what the key fields mean and how the entities relate; state your interpretation and let the user correct it until they agree. Field names in exports are routinely misleading, and a tree built on a misread schema is wrong at every level. Free text skips this.
- **Scope is asked, not guessed.** When the trigger, completion criteria or exclusions are ambiguous, ask; an assumed boundary makes the exhaustiveness score meaningless. If scope cannot be established from the conversation, switch to `/interview`.
</input_rules>
<done>
Done is a tree that passes the structural validator and carries ME/CE scores for every level the depth-adaptive schedule tests, with dimension rationale, atom classifications, model tiers and cross-branch dependencies filled in.
</done>
<output>
1. The human-readable tree. One line per node: label, execution type in brackets, estimated duration, and model tier or integration method:
```
Process Name (orchestration type)
+-- Phase 1 (parallel)
| +-- [agent] Step A (~5m, sonnet)
| +-- [human] Step B (~2h, webhook)
+-- Phase 2 (sequential)
+-- [tool] Step C (~10s, tool_name)
+-- [agent] Step D (~1m, haiku)
```
2. The JSON, conforming to `references/output_schema.md` in the mece-decomposer skill, checked with:
```bash
uv run ${CLAUDE_PLUGIN_ROOT}/skills/mece-decomposer/scripts/validate_mece.py <output.json>
```
3. When the `mece-decompose` MCP tool is available, call it with the full JSON string to render the interactive tree.
</output>
Adjacent skills: `/validate` scores an existing decomposition, `/interview` extracts one from an SME, `/export` turns a validated one into code.From fblissjr/fb-claude-skills, Apache-2.0.
More from fblissjr/fb-claude-skills
- 0.603score
heylook-provider
fblissjr
Wire an application to heylook (heylookitsanllm), a local multimodal LLM server on Apple Silicon serving MLX and gguf models over one Anthropic…
SkillAFreshness: A★ 2Claude Code +9 - 0.593score
skill-dashboard
fblissjr
Show an interactive dashboard of all tracked skills, plugins, and repo hygiene in this project.
SkillAFreshness: A★ 2Claude Code +9 - 0.563score
design-scoreboard
fblissjr
Designs what an improvement or optimizer loop should climb for a given project and goal, and what must not get worse while it does: goal metrics measured on…
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
mece-decomposer
fblissjr
MECE decomposition methodology, scoring rubrics, and Agent SDK mapping for process analysis.
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
python-tooling
fblissjr
House Python conventions plus the type-checking failures that get misdiagnosed: the dependency pinning policy (applications exact, libraries floors), the rule…
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
dimensional-modeling
fblissjr
Design and implement Kimball-style star schemas in DuckDB for LLM agent state persistence.
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
improve
fblissjr
Runs one improvement pass over a project in the direction its north star points, without breaking what it does today: faster in real use, less code, a truer…
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
optimize
fblissjr
Speeds up a codebase as far as it will go in one session, against a hard target (every primary benchmark at least 1.2x faster than the base commit by default)…
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
trim-agents-md
fblissjr
Tightens a repository's always-loaded agent instructions (AGENTS.md, CLAUDE.md, unconditional rules): every line must prevent a mistake, and each one that…
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
json-query
fblissjr
Guide JSON query tool selection and syntax.
SkillBFreshness: B★ 2Claude Code +9 - 0.558score
gemini-multimodal
fblissjr
Send a task to a Gemini model and get a structured answer back - perceptual work Claude cannot do directly, or any ad-hoc question worth a second model's take.
SkillBFreshness: B★ 2Claude Code +9 - 0.557score
dep-audit
fblissjr
Both uv and bun ship a native CVE audit subcommand, which is the part worth knowing — without it the reflex is to reach for pip-audit or safety and never find…
SkillBFreshness: B★ 2Claude Code +9
Signals
- Stars
- 2
- Forks
- 1
- Open issues
- 0
- Last commit (this folder)
- Sep 24, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/fblissjr/fb-claude-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from fblissjr
- 0.603score
heylook-provider
fblissjr
Wire an application to heylook (heylookitsanllm), a local multimodal LLM server on Apple Silicon serving MLX and gguf models over one Anthropic…
SkillAFreshness: A★ 2Claude Code +9 - 0.593score
skill-dashboard
fblissjr
Show an interactive dashboard of all tracked skills, plugins, and repo hygiene in this project.
SkillAFreshness: A★ 2Claude Code +9 - 0.563score
dimensional-modeling
fblissjr
Design and implement Kimball-style star schemas in DuckDB for LLM agent state persistence.
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
mece-decomposer
fblissjr
MECE decomposition methodology, scoring rubrics, and Agent SDK mapping for process analysis.
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
python-tooling
fblissjr
House Python conventions plus the type-checking failures that get misdiagnosed: the dependency pinning policy (applications exact, libraries floors), the rule…
SkillBFreshness: B★ 2Claude Code +9 - 0.563score
improve
fblissjr
Runs one improvement pass over a project in the direction its north star points, without breaking what it does today: faster in real use, less code, a truer…
SkillBFreshness: B★ 2Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get decompose running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
decompose skill. Agent Mart. https://agentmart.dev/skills/fblissjr-decompose (last updated 2026-10-24)