| name | dnd-alignment |
| description | Rank cobrabox features and pipelines on the D&D 9-alignment grid (Law/Chaos × Good/Evil). Use when the user invokes "/dnd-alignment", asks "what is the alignment of X", asks to "align my pipeline", or wants to know the moral character of a feature or analysis chain. |
DnD Alignment
Determine the D&D 9-alignment of a cobrabox feature and append it to the canonical table.
Pipeline and roster rendering are handled by the Python script — do not compute them here.
Invocation
/dnd-alignment <FeatureName>
Responsibility split
| Task | Owner |
|---|
| Rank an individual feature (assign Law/Good scores + lore) | This skill |
| Append ranking to the canonical table | This skill |
| Print the full roster | uv run python -m cobrabox.egg.dnd_alignment --roster |
| Compute pipeline aggregate | uv run python -m cobrabox.egg.dnd_alignment F1 F2 F3 |
| Compute Chord pipeline aggregate | uv run python -m cobrabox.egg.dnd_alignment --chord F1 F2 F3 |
Canonical alignment table
The source of truth is src/cobrabox/egg/alignments.py.
Read that file before ranking a new feature — the feature may already be present.
Axis encoding
- Law axis: Lawful = +1, Neutral = 0, Chaotic = −1
- Good axis: Good = +1, Neutral = 0, Evil = −1
Label grid
LAW (+1) NEUTRAL (0) CHAOS (-1)
GOOD (+1) Lawful Good Neutral Good Chaotic Good
NEUT ( 0) Lawful Neutral True Neutral Chaotic Neutral
EVIL (-1) Lawful Evil Neutral Evil Chaotic Evil
Procedure
1. Read the alignment table
Read src/cobrabox/egg/alignments.py. Check whether the feature is already present
in ALIGNMENTS. If it is, print its existing entry and stop — do not re-rank.
2. Read the feature file
Read src/cobrabox/features/<feature_snake_case>.py to understand what the feature
actually does before assigning an alignment.
3. Assign scores
Choose Law and Good scores using the rubrics below. Write 1–2 sentences of reasoning
for each axis before committing to a score.
Law axis rubric
| Score | Meaning | Indicators |
|---|
| +1 Lawful | Actively imposes structure | Creates segments (windowing), hard threshold classification (IQR), named category ontology (frequency bands), strict published-protocol adherence |
| 0 Neutral | Passively describes existing patterns | Correlation/synchrony measures, spectral descriptions, statistical summaries — even with fixed formulas |
| -1 Chaotic | Disrupts or ignores conventions | print statements, missing validation, unpredictable output shape |
Common trap: A fixed, deterministic formula does not make a feature Lawful — almost all
signal processing is deterministic. Ask instead: does this feature impose structure onto the
data, or describe structure already present in it?
Lawful examples: SlidingWindow (creates window segments), Bandpower (names frequency
categories), SpikesCalc (classifies by IQR rule).
Neutral examples: Coherence, PLV, Autocorr, Spectrogram, EnvelopeCorrelation,
PartialCorrelation — all use precise formulas but measure existing patterns without imposing.
Good axis rubric
| Score | Meaning | Indicators |
|---|
| +1 Good | Preserves or enhances signal meaning | Increases interpretability, faithful to data, good metadata practice |
| 0 Neutral | Indifferent to meaning | Mechanical reduction with no semantic intent (pure aggregation) |
| -1 Evil | Discards or distorts signal meaning | Selects extremes ruthlessly, drops metadata, lossy without documentation |
4. Write one lore sentence
One punchy sentence (≤ 15 words) in the style of the existing entries. It should
capture the moral character of what the feature does to data, not just describe it
technically.
5. Choose a 2-char abbreviation
Use the first two lowercase letters of the class name, unless that conflicts with an
existing abbreviation in the table — in that case pick the most recognisable 2-char
substring.
6. Append to the table
Edit src/cobrabox/egg/alignments.py — add a new entry to the ALIGNMENTS dict
following the existing format exactly:
"FeatureName": {
"law": <+1|0|-1>,
"good": <+1|0|-1>,
"label": "<Alignment Label>",
"abbrev": "<2-char>",
"lore": "<lore sentence>",
},
Place it alphabetically by key, or at the end if alphabetical order is not obvious.
7. Run the roster to confirm
uv run python -m cobrabox.egg.dnd_alignment --roster
Confirm the new feature appears correctly in the output.
8. Report to conversation
Print:
<FeatureName> — <Alignment Label>
Law: <score> (<reasoning>)
Good: <score> (<reasoning>)
"<lore sentence>"
Table updated: src/cobrabox/egg/alignments.py
Run `uv run python -m cobrabox.egg.dnd_alignment --roster` to see the full grid.
Pipeline alignment — delegate to script
When the user asks about a pipeline or sequence of features, do not compute it
yourself. Instead, tell them to run:
uv run python -m cobrabox.egg.dnd_alignment SlidingWindow LineLength MeanAggregate
uv run python -m cobrabox.egg.dnd_alignment --chord SlidingWindow LineLength MeanAggregate
Roster — delegate to script
When the user asks for the full roster or grid:
uv run python -m cobrabox.egg.dnd_alignment --roster
