lispsyntax-acset

Bidirectional S-expression ↔ ACSet conversion with Specter-style inline caching

Version: 1.2.0 Trit: 0 (Ergodic - coordinates data serialization) Dynamic Sufficiency: ✅ VERIFIED (2025-12-22)

Overview

This skill bridges LispSyntax.jl with ACSets.jl using patterns from:

1. OCaml's

   ppx_sexp_conv

   library (bidirectional deriving)
2. Clojure Specter's inline caching and CPS (Nathan Marz)

Core Capabilities

1. S-expression Parsing & Serialization

# String → Sexp (like OCaml's Sexp.of_string)
sexp = parse_sexp("(define (square x) (* x x))")

# Sexp → String (like OCaml's Sexp.to_string)
str = to_string(sexp)

2. ACSet Conversion (ppx_sexp_conv pattern)

# ACSet → Sexp
sexp = sexp_of_acset(my_graph)

# Sexp → ACSet
graph = acset_of_sexp(GraphType, sexp)

3. Specter-Style Bidirectional Navigation

Key insight from Specter: Same path expression works for both

select

transform

# Same path for selection and transformation
path = [ALL, pred(iseven)]

# Select: collect matching values
select(path, [1,2,3,4,5])  # → [2, 4]

# Transform: modify matching values in-place
transform(path, x -> x*10, [1,2,3,4,5])  # → [1, 20, 3, 40, 5]

Specter Navigator Protocol

From Marz's talk "Rama on Clojure's Terms":

RichNavigator

Navigator

comp-navs

comp_navs(navs...)

late-bound-nav

@late_nav

coerce-nav

coerce_nav(x)

Primitive Navigators

ALL       # Navigate to every element
FIRST     # Navigate to first element
LAST      # Navigate to last element
keypath(k) # Navigate to key in map/dict
pred(f)   # Filter by predicate

S-expression Navigators (Unique to Julia)

SEXP_HEAD      # Navigate to first child (head of list)
SEXP_TAIL      # Navigate to rest of children
SEXP_CHILDREN  # Navigate to children as vector
SEXP_WALK      # Recursive descent (prewalk)
sexp_nth(n)    # Navigate to nth child
ATOM_VALUE     # Navigate to atom's string value

ACSet Navigators (Unique to Julia)

acset_field(:E, :src)        # Navigate morphism values
acset_where(:E, :src, ==(1)) # Filter parts by predicate
acset_parts(:V)              # Navigate all parts of object

Bidirectional Examples

S-expression Transform

sexp = parse_sexp("(define (square x) (* x x))")

# Uppercase all atoms
transformed = nav_transform(SEXP_WALK, sexp, 
    s -> s isa Atom ? Atom(uppercase(s.value)) : s)
# → (DEFINE (SQUARE X) (* X X))

# Rename function: change 'square' to 'cube'
renamed = transform([sexp_nth(2), sexp_nth(1), ATOM_VALUE], _ -> "cube", sexp)
# → (define (cube x) (* x x))

ACSet Navigation

g = @acset Graph begin V=4; E=3; src=[1,2,3]; tgt=[2,3,4] end

# Select all source vertices
select([acset_field(:E, :src)], g)  # → [1, 2, 3]

# Transform: shift all targets (mod 4)
g2 = transform([acset_field(:E, :tgt)], t -> mod1(t+1, 4), g)

Cross-Domain Bridge

# ACSet → Sexp → Navigate → Transform → Sexp → ACSet
graph = @acset Graph begin V=4; E=3; src=[1,2,3]; tgt=[2,3,4] end
sexp = sexp_of_acset(graph)

# Navigate sexp to find all morphism names
morphism_names = select([SEXP_CHILDREN, sexp_nth(1), ATOM_VALUE], sexp)

# Roundtrip back to ACSet
graph2 = acset_of_sexp(Graph, sexp)

Performance: Inline Caching (comp-navs = alloc + field sets)

From Marz's Specter talk: "comp-navs is fast because it's just object allocation + field sets"

What This Means

# Traditional approach: work at composition time
compose(a, b, c) → [compile] → [optimize] → CompiledPath  # SLOW

# Specter approach: zero work at composition
comp_navs(a, b, c) → ComposedNav{navs: [a, b, c]}  # Just allocate!

The

comp_navs

1. Allocate a

   ComposedNav

   struct
2. Set its

   navs

   field to the array of navigators

No compilation. No interpretation. No tree walking. Just allocation.

Why This Works: CPS (Continuation-Passing Style)

All actual work happens at traversal time via chained continuations:

# When you call select(), it builds a chain:
nav_select(first_nav, data, 
    result1 -> nav_select(second_nav, result1,
        result2 -> nav_select(third_nav, result2,
            final_result -> collect(final_result))))

Inline Caching at Callsite

# At each callsite, path compiled ONCE:
@compiled_select([ALL, pred(iseven)], data)

# Internally:
let cached = @__MODULE__.CACHE[callsite_id]
    if cached === nothing
        cached = comp_navs(ALL, pred(iseven))  # Once!
    end
    nav_select(cached, data, identity)
end

Result: Near-hand-written performance with full abstraction.

GF(3) Triads

Catlab API Notes

The

homs(schema)

(name, dom, codom)

# Correct usage (post-Catlab update)
for hom_tuple in homs(schema)
    hom_name = hom_tuple[1]  # First element is name
    dom_ob = hom_tuple[2]    # Second is domain
    codom_ob = hom_tuple[3]  # Third is codomain
end

Files

• Bridge:

  lib/lispsyntax_acset_bridge.jl

• Specter navigators:

  lib/specter_acset.jl

• Comparison:

  lib/specter_comparison.bb

References

• Specter - Nathan Marz
• ppx_sexp_conv - Jane Street
• LispSyntax.jl
• ACSets.jl

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Annotated Data

• anndata [○] via bicomodule
  • Hub for annotated matrices

Bibliography References

• general

  : 734 citations in bib.duckdb

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.