Automated summary
Spindle performs rapid classification and sorting of fragments from a prior drift stage, categorizing them by relevance and signal strength to filter noise and extract themes for further processing.
Strengths
- Fast classification with clear category system
- Customizable selectivity per cycle
- Special handling of gaps and friction
- Theme extraction from related fragments
Limitations
- Cannot generate new fragments
- Classification based on limited context
- Selectivity thresholds may discard useful fragments if too strict
Use when you have a large set of mixed user feedback or fragments that need to be sorted by relevance to a specific skill or domain.
Avoid when you need to generate new ideas or when the fragments are already well-organized and require no filtering.
Security analysis
SafeThe skill only uses the Read tool, has no network or shell access, and performs purely classificatory analysis on textual fragments. It does not instruct any destructive or exfiltrating actions.
No concerns found
How this analysis is madeExamples
I have a bag of fragments from the drift stage containing user feedback about my AI agent. Run spindle with selectivity=0.6 to classify each fragment as HIT, EDGE, GAP, FRIC, ADJ, or NOISE, assign signal strength, and produce a sorted manifest with themes.Take these 20 support ticket summaries and apply spindle classification. Group fragments that touch the same area into themes with priority levels. Flag any contradictions where the skill documentation says one thing but users expect another.name: spindle description: "Stage N2. Rapid classification bursts — sorting, tagging, filtering fragments. The workhorse stage at ~45% of cycle time. Most fragments die here." context: fork user-invocable: false allowed-tools:
- Read
Spindle — Stage N2
The sorting room. Takes the noisy fragment bag from drift, classifies each fragment, kills irrelevant noise, extracts themes.
Inputs
From orchestrator: fragment_bag (from N1), target_skill (path),
selectivity (0-1, default 0.6), cycle_number.
Procedure
1. Load Target Skill
Read SKILL.md. Build mental model of: what it does, what it handles, what it assumes, what it declines.
2. Classify Each Fragment
One category + one signal strength (0-1) per fragment:
| Category | Code | Description |
|----------|------|-------------|
| Direct hit | HIT | Skill explicitly covers this |
| Edge case | EDGE | Within domain but near boundaries |
| Gap | GAP | Within domain but NOT covered |
| Friction | FRIC | User difficulty with documented workflow |
| Adjacent | ADJ | Related domain, different skill |
| Noise | NOISE | Irrelevant or unrealistically synthetic |
3. Filter
Survival threshold = selectivity × 0.8. Exceptions:
FRICandGAPsurvive at signal > 0.3 (always interesting)- Synthetic fragments get +0.1 bonus (give creativity a chance)
| Cycle | Selectivity | Expected Kill Rate | |-------|------------|-------------------| | 1 | 0.7 | 60-70% | | 2 | 0.5 | 40-50% | | 3 | 0.3 | 20-30% | | 4+ | 0.2 | 10-20% |
4. Extract Themes
If 2+ fragments touch the same area, elevate to a theme with priority (high/medium/low). Themes are primary input for N3.
Flag contradictions (skill declines something users expect) and escalations (fundamental structural issues).
5. Output Sorted Manifest
sorted_manifest:
target_skill: /path/to/SKILL.md
input_fragments: 14
surviving_fragments: 6
kill_rate: 0.57
themes: [{name, fragment_ids, priority, reason}]
survivors: [{id, category, signal, content, tags, theme, notes}]
discarded: {count, categories: {NOISE: 4, ADJ: 2, HIT: 2}}
escalations: []
Behavioral Rules
- Be decisive. If you can't classify quickly, it's NOISE.
- Favor false positives. Better to pass mediocre fragments to N3 than kill one that would have produced insight in REM.
- Don't generate. Spindle classifies, it doesn't create new fragments.
From the file to a first result
The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.
View the source and supporting filesThis download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.
1. Download the file
Download SKILL.mdFrom the root of a test project, save the file as .skill-downloads/spindle-rapid-classification-mryiqgsg/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.
Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.
mkdir -p '.skill-downloads/spindle-rapid-classification-mryiqgsg' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/spindle-rapid-classification-mryiqgsg/download' --output '.skill-downloads/spindle-rapid-classification-mryiqgsg/SKILL.md.part' && mv '.skill-downloads/spindle-rapid-classification-mryiqgsg/SKILL.md.part' '.skill-downloads/spindle-rapid-classification-mryiqgsg/SKILL.md'2. Check prerequisites
Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.
No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.
The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.
3. Load the skill
After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.
Claude Code
test -f '.skill-downloads/spindle-rapid-classification-mryiqgsg/SKILL.md' && test -s '.skill-downloads/spindle-rapid-classification-mryiqgsg/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/spindle' && cp -R '.skill-downloads/spindle-rapid-classification-mryiqgsg' '.claude/skills/spindle'OpenSkills
OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.
Official OpenSkills installationopenskills install './.skill-downloads/spindle-rapid-classification-mryiqgsg'openskills read 'spindle-rapid-classification-mryiqgsg'Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.
For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.
openskills syncManual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.
4. Try a first task
Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:
Read .skill-downloads/spindle-rapid-classification-mryiqgsg/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.
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