skills/atomise/SKILL.md
Atom of Thoughts (AoT) reasoning - decompose complex problems into atomic units with confidence tracking and backtracking. For genuinely complex reasoning, not everyday questions. Triggers on: atomise, complex reasoning, decompose problem, structured thinking, verify hypothesis.
npx skillsauth add 0xDarkMatter/claude-mods atomiseInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Decompose complex problems into minimal, verifiable "atoms" of thought. Unlike chain-of-thought (linear, error-accumulating), AoT treats each step as independently verifiable and backtracks when confidence drops.
Use for: Security analysis, architectural decisions, complex debugging, multi-step proofs. Don't use for: Simple questions, trivial calculations, information lookup.
/atomise "<problem>" [--light | --deep] [--math | --code | --security | --design]
1. DECOMPOSE -> Break into atomic subquestions (1-2 sentences each)
2. SOLVE -> Answer leaf nodes first, propagate up
3. VERIFY -> Test each hypothesis (counterexample, consistency, domain check)
4. CONTRACT -> Summarize verified state in 2 sentences (drop history)
5. EVALUATE -> Confident enough? Done. Too uncertain? Backtrack and try another path.
Repeat until confident or all paths exhausted.
Each atom is a minimal unit:
{id, type, content, depends_on[], confidence, verified}
| Type | Purpose | Starting Confidence | |------|---------|---------------------| | premise | Given facts | 1.0 | | reasoning | Logical inference | Inherited from parents | | hypothesis | Claim to test | Max 0.7 until verified | | verification | Test result | Based on test outcome | | conclusion | Final answer | Propagated from chain |
Confidence propagates: A child can't be more confident than its least-confident parent.
These numbers are heuristic, not calibrated probabilities. They're useful for tracking relative certainty, not for actual risk assessment.
| Threshold | Meaning | |-----------|---------| | > 0.85 | Confident enough to conclude | | 0.6 - 0.85 | Needs more verification | | < 0.6 | Decompose further or backtrack | | < 0.5 | Backtrack - this path isn't working |
Verification adjusts confidence:
Depth:
--light - Fast: max 3 levels, 0.70 confidence threshold--deep - Exhaustive: max 7 levels, 0.90 thresholdDomain (adjusts verification style):
--math - Arithmetic checks, proof validation, boundary tests--code - Type checking, invariant verification, test generation--security - Threat modeling, attack surface, adversarial thinking--design - Tradeoff analysis, constraint satisfaction, feasibilityANSWER: {result}
CONFIDENCE: {0.0-1.0} - {why}
KEY CHAIN: P1 -> R1 -> H1 -> V1 -> C1
ATOMS:
| id | type | content | conf | verified |
|----|------|---------|------|----------|
| P1 | premise | Given: ... | 1.0 | Y |
| R1 | reasoning | Therefore: ... | 0.95 | Y |
| ... | ... | ... | ... | ... |
RISKS: {what could change this}
Add --verbose for full trace, --quiet for just the answer.
When a path yields confidence < 0.5 after verification:
# Complex debugging
/atomise "Why does this function return null on the second call?" --code
# Security review
/atomise "Is this authentication flow vulnerable to session fixation?" --security
# Architecture decision
/atomise "Should we use event sourcing for this domain?" --deep --design
# Quick decision (light mode)
/atomise "Redis vs Memcached for this cache layer?" --light
BAD: /atomise "What's 2+2?" -> Just answer it
BAD: /atomise "Rewrite this function" -> That's implementation, not reasoning
BAD: Forcing conclusion despite low confidence -> Let it backtrack
GOOD: /atomise for genuine uncertainty requiring structured decomposition
testing
Audit any repo against the agentic-quality doctrine — score entry docs, structure, and enforcement gates, then map each finding to its fix. Triggers on: repo doctor, repo audit, agentic quality, is this repo agent-friendly, doc drift, stale AGENTS.md, monorepo structure, nested CLAUDE.md.
data-ai
Router for parallel or recurring agent work across six skills. Covers: parallel agents, fan out work, delegate to workers, run overnight, scheduled loop, land branches, mixed-model fleet, orchestrate workers, background agents at scale. Triggers on: which skill for parallel work, fan out agents, spawn workers, run this overnight, schedule a loop, land my branches, heterogeneous fleet, delegate to cheaper model, autonomous loop.
tools
Heterogeneous cross-provider fleet - GLM (z.ai), Codex (OpenAI), Grok (xAI), Anthropic Sonnet/Opus/Haiku - from one session, porting the native Workflow tool's patterns (adversarial verify, judge panels, journal resume) to OS-process workers. Triggers: fleetflow, heterogeneous/mixed-model fleet, codex worker, grok worker, cross-provider fan-out, cross-model verify.
development
Application/game-scale three.js: ES modules, GLTF pipeline (DRACO/KTX2/meshopt), AnimationMixer, physics (rapier/cannon-es), react-three-fiber, and performance at scale (InstancedMesh, LOD, draw calls). Triggers on: three.js, GLTFLoader, r3f, game loop, WebGL memory leak, boids.