decompose — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited decompose (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Break a problem into sub-problems, then analyze each one in depth using sequential reasoning chains. Unlike /breakdown (strategic overview), /decompose gives you deep understanding of every piece.
/decompose [problem]
/decompose [depth] [problem]Depth: 1-5 (default 3). Higher = more granular sub-problems.
/decompose vs /breakdown/breakdown | /decompose | |
|---|---|---|
| Strategy | Breadth-first: overview pipeline | Depth-first: split then drill each |
| Steps | first_principles -> decompose -> patterns -> feasibility | decompose -> deep-dive each -> synthesize |
| Output | Go/no-go assessment | Deep understanding per piece |
| Cost | 4 API calls | 1 + (N sub-problems x 2) calls |
| When | "Should we do this?" | "How does each part actually work?" |
When user invokes /decompose [problem]:
Parse optional depth (default 3). Call:
mcp__tachibot-mcp__kimi_decompose({
task: "[problem]",
depth: [depth],
outputFormat: "dependencies",
context: "Break into distinct sub-problems. Each should be independently analyzable."
})If kimi_decompose is unavailable, fall back to:
mcp__tachibot-mcp__nextThought({
thought: "Decompose this problem into 3-7 distinct sub-problems with dependencies:\n\n[problem]\n\nFor each sub-problem provide: ID, name, description, depends_on, estimated complexity (low/medium/high)",
model: "gemini",
executeModel: true,
contextWindow: "none",
nextThoughtNeeded: true
})Present the sub-problem tree to the user before diving in:
Found [N] sub-problems:
1. [Sub-problem A] (complexity: medium)
2. [Sub-problem B] -> depends on A (complexity: high)
3. [Sub-problem C] (complexity: low)
...
Diving into each one...For EACH sub-problem (in dependency order), run a 2-step nextThought chain:
Analysis step (fresh context per sub-problem):
mcp__tachibot-mcp__nextThought({
thought: "Deep-dive analysis of sub-problem: [sub-problem name]\n\nDescription: [sub-problem description]\n\nFull problem context: [original problem]\n\nAnalyze:\n1. CONTRACT: What are the preconditions (what must be true before)? What are the postconditions (what's guaranteed after)?\n2. What exactly needs to happen here?\n3. What are the edge cases?\n4. What could go wrong?\n5. What are the key decisions to make?\n6. What patterns or prior art exist?",
model: "grok",
executeModel: true,
contextWindow: "none",
nextThoughtNeeded: true
})Model selection for analysis - use FIRST available:
grok - strong first-principles reasoninggemini - broad analytical capabilityopenai - structured analysiskimi - step-by-step depthSynthesis step (sees the analysis):
mcp__tachibot-mcp__nextThought({
thought: "Synthesize the deep-dive on: [sub-problem name]\n\nDistill to:\n- Core insight (1 sentence)\n- Key decisions needed\n- Risks identified\n- Recommended approach\n- Estimated effort: trivial / small / medium / large",
model: "gemini",
executeModel: true,
contextWindow: "recent",
nextThoughtNeeded: true
})Model selection for synthesis - use a DIFFERENT model than analysis. Priority:
gemini - great at synthesisopenai - structured outputkimi - thorough summarizationAfter all sub-problems are analyzed, one final thought connecting everything:
mcp__tachibot-mcp__nextThought({
thought: "Final synthesis of all [N] deep-dives:\n\n[List each sub-problem + its core insight]\n\nSynthesize:\n1. Where does the REAL complexity live? (which sub-problems are hardest)\n2. What connections exist between sub-problems that weren't obvious?\n3. What's the critical path?\n4. What should be tackled first and why?\n5. Are there sub-problems that could be eliminated or simplified?",
model: "gemini",
executeModel: true,
contextWindow: "all",
nextThoughtNeeded: false
})Format:
## Decomposition: [problem]
### Sub-Problems ([N] found)
#### 1. [Sub-problem A] - [complexity]
**Contract:** Pre: [what must be true] → Post: [what's guaranteed]
**Insight:** [1 sentence core finding]
**Key decisions:** [what needs deciding]
**Risks:** [what could go wrong]
**Approach:** [recommended path]
**Effort:** [trivial/small/medium/large]
#### 2. [Sub-problem B] - [complexity]
...
### Where the Real Complexity Lives
[Which sub-problems are hardest and why]
### Hidden Connections
[Dependencies and interactions not obvious from the surface]
### Critical Path
1. [First] - [why first]
2. [Second] - [why second]
...
### Simplification Opportunities
[Sub-problems that could be eliminated or combined]/decompose implement a real-time collaborative editor/decompose 4 migrate monolith to microservices/decompose why is our CI pipeline taking 45 minutes/decompose design an API rate limiter that handles burst traffic~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.