H-5721: Figma-to-tokens transform / panda "preset + styled-system + ui" packages

[lunelson]

🌟 What is the purpose of this PR?

This PR implements a new variable import and transform logic and workflow, for synchronizing design tokens from Figma to the HASH design system, and also restructures the packages of the design system into a 3-package architecture with optimized compilation setups for each.

Background

Besides the Figma variable importation logic, the original plan with these changes was to emulate Park UI, as a reference integration of Ark UI / Panda CSS, since they are all the same maintainers. However, from a packaging, dependency and consumption point of view, Park UI does not correspond to our current use-case, since it implements a shadcn-style registry approach —where consumers copy component code into their projects rather than importing from a dependency.

Therefore Park UI's component/style integration patterns will remain a reference, but for compilation and publishing we're following the "panda css as external package" pattern recommendation:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   ds-theme      │────▶│   ds-helpers    │────▶│  ds-components  │
│  (Panda Preset) │     │ (Styled System) │     │ (React + Ark UI)│
└─────────────────┘     └─────────────────┘     └─────────────────┘
        │                       │                       │
   Design tokens          css(), cva(),           Button, Checkbox,
   from Figma             tokens, jsx             Avatar, etc.

Deployments

In addition to the existing deployment of Storybook for the ds-components, this PR adds a Ladle deployment (similar principle to Storybook but simpler and faster) which demonstrates the tokens in ds-theme and which is built from within ds-helpers (see Demo section below)

🔍 What does this change?

Package Architecture

@hashintel/ds-theme — The Panda Preset

A pure PandaCSS preset that defines design tokens sourced from Figma variable exports:

• New codegen scripts: generate-colors.ts and generate-tokens.ts transform Figma JSON into Panda token definitions
• Dual exports:
  • Main export (.) — The preset for use in panda.config.ts
  • Theme export (./theme) — Raw color/token definitions for stories and helpers
• Token structure following Panda/Tailwind conventions:
  • Colors: gray.10, blue.50, accent.60 (core palette) + semantic tokens like bg.accent.bold, text.primary, border.neutral
  • Spacing: "3" = 4px, "5" = 8px, etc. (DEFAULT scale is implicit)
  • Radii: md.4 = 8px, md.full = 9999px, plus sm/lg/full scales
  • Typography: text-sm, text-base, font weights as strings ("medium", "semibold")

@hashintel/ds-helpers — The Styled System Package

The single source of truth for Panda CSS utilities across all consuming packages:

• Runs panda codegen using ds-theme preset
• Exports styled-system/* modules: css, cva, tokens, jsx, patterns, types
• Includes Ladle stories for visual documentation
• New: Playwright snapshot testing for visual regression

@hashintel/ds-components — The Component Library

React components built with Ark UI (headless) + Panda CSS (styling):

• Token references updated in existing components: Avatar, Badge, Button, Checkbox, RadioGroup, SegmentedControl, Slider, Switch
• Storybook for documentation and development

Token Name Migrations

All components updated to use new token naming conventions:

Old Token	New Token
"default.3"	"3"
"spacing.1"	"2"
"border.neutral.default"	"border.neutral"
"bg.accent.bold.default"	"bg.accent.bold"
"surface.default"	"surface"
"radius.4"	"md.4"
"core.gray.20"	"gray.20"
fontWeight: 500	fontWeight: "medium"

Build System Changes

• Migrated ds-theme and ds-components to tsdown for faster builds
• Updated ESLint config for styled-system import resolution
• Added .gitignore / biome.jsonc entries for generated styled-system directories

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

• modifies an npm-publishable library and I have added a changeset file(s)

📜 Does this require a change to the docs?

The changes in this PR:

• are internal and do not require a docs change

🕸️ Does this require a change to the Turbo Graph?

The changes in this PR:

• affected the execution graph, and the turbo.json's have been updated to reflect this

🛡 What tests cover this?

• unit tests for the token transformations, in ds-theme
• playwright snapshot tests on Ladle routes, in ds-helpers — Visual regression testing for color/token stories
• type check via yarn lint:tsc in each package validates token name correctness
• storybook build success (as before) in ds-components

❓ How to test this?

1. Checkout the branch
2. Run yarn install at repo root
3. Build the design system packages:

   cd libs/@hashintel/ds-theme && yarn build
   cd libs/@hashintel/ds-helpers && yarn build
   cd libs/@hashintel/ds-components && yarn codegen && yarn build

4. Run type checking:

   cd libs/@hashintel/ds-components && yarn lint:tsc

5. Launch Storybook to verify components:

   cd libs/@hashintel/ds-components && yarn storybook

6. Run snapshot tests:

   cd libs/@hashintel/ds-helpers && yarn test

📹 Demo

light	dark

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 60.96%. Comparing base (94ee310) to head (fb8fe2b).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #8191      +/-   ##
==========================================
+ Coverage   59.71%   60.96%   +1.25%     
==========================================
  Files        1214     1090     -124     
  Lines      115203   110581    -4622     
  Branches     5062     4570     -492     
==========================================
- Hits        68793    67416    -1377     
+ Misses      45608    42395    -3213     
+ Partials      802      770      -32     

Flag	Coverage Δ
apps.hash-ai-worker-py	?
apps.hash-ai-worker-ts	1.41% <ø> (ø)
apps.hash-api	0.00% <ø> (ø)
backend-integration-tests	?
blockprotocol.type-system	40.84% <ø> (ø)
deer	?
error-stack	?
local.claude-hooks	0.00% <ø> (ø)
local.harpc-client	51.24% <ø> (ø)
local.hash-backend-utils	?
local.hash-graph-sdk	10.88% <ø> (ø)
local.hash-isomorphic-utils	0.00% <ø> (ø)
local.hash-subgraph	?
rust.antsi	0.00% <ø> (ø)
rust.deer	?
rust.error-stack	90.88% <ø> (ø)
rust.harpc-codec	84.70% <ø> (ø)
rust.harpc-net	96.14% <ø> (ø)
rust.harpc-tower	66.80% <ø> (ø)
rust.harpc-types	0.00% <ø> (ø)
rust.harpc-wire-protocol	92.23% <ø> (ø)
rust.hash-codec	72.76% <ø> (ø)
rust.hash-graph-api	2.89% <ø> (ø)
rust.hash-graph-authorization	62.47% <ø> (ø)
rust.hash-graph-store	30.54% <ø> (ø)
rust.hash-graph-temporal-versioning	47.95% <ø> (ø)
rust.hash-graph-types	0.00% <ø> (ø)
rust.hash-graph-validation	83.45% <ø> (ø)
rust.hashql-ast	87.25% <ø> (ø)
rust.hashql-compiletest	46.65% <ø> (ø)
rust.hashql-core	81.77% <ø> (+<0.01%)	⬆️
rust.hashql-diagnostics	72.43% <ø> (ø)
rust.hashql-eval	68.54% <ø> (ø)
rust.hashql-hir	89.10% <ø> (ø)
rust.hashql-mir	88.00% <ø> (-1.66%)	⬇️
rust.sarif	?
sarif	?
tests.hash-backend-integration	?
unit-tests	?

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[cursor]

Cursor Bugbot has reviewed your changes and found 1 potential issue.

^{Bugbot Autofix is OFF. To automatically fix reported issues with Cloud Agents, enable Autofix in the Cursor dashboard.}

[cursor]

Hardcoded local machine paths in documentation file

Medium Severity

The AGENTS.md documentation contains hardcoded absolute paths to a developer's local machine: /Users/lunelson/Code/chakra-ui/park-ui/packages/preset and /Users/lunelson/Code/chakra-ui/park-ui/components/react. These paths reference the Park UI project but use developer-specific local filesystem locations that are meaningless to other developers or AI agents reading this documentation. The "Reference Implementation" section likely intended to link to the Park UI GitHub repository or provide relative/generic path descriptions instead.

 

[lunelson]

will remove in a follow-up

[github-actions]

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2002	$$25.4 \mathrm{ms} \pm 188 \mathrm{μs}\left({\color{gray}-1.921 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.21 \mathrm{ms} \pm 14.9 \mathrm{μs}\left({\color{gray}-0.736 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$11.7 \mathrm{ms} \pm 64.0 \mathrm{μs}\left({\color{gray}0.131 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$41.5 \mathrm{ms} \pm 276 \mathrm{μs}\left({\color{gray}-0.954 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$13.6 \mathrm{ms} \pm 75.9 \mathrm{μs}\left({\color{gray}-0.599 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$22.9 \mathrm{ms} \pm 132 \mathrm{μs}\left({\color{gray}0.696 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$25.7 \mathrm{ms} \pm 169 \mathrm{μs}\left({\color{lightgreen}-38.762 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.58 \mathrm{ms} \pm 19.1 \mathrm{μs}\left({\color{lightgreen}-81.715 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$11.0 \mathrm{ms} \pm 93.2 \mathrm{μs}\left({\color{lightgreen}-59.659 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.61 \mathrm{ms} \pm 19.3 \mathrm{μs}\left({\color{gray}0.272 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.82 \mathrm{ms} \pm 12.4 \mathrm{μs}\left({\color{gray}-2.401 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.22 \mathrm{ms} \pm 17.9 \mathrm{μs}\left({\color{gray}1.12 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$4.96 \mathrm{ms} \pm 25.5 \mathrm{μs}\left({\color{gray}-0.368 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.36 \mathrm{ms} \pm 15.8 \mathrm{μs}\left({\color{gray}-1.731 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.92 \mathrm{ms} \pm 16.9 \mathrm{μs}\left({\color{gray}-0.635 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.18 \mathrm{ms} \pm 18.1 \mathrm{μs}\left({\color{gray}0.881 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.25 \mathrm{ms} \pm 12.8 \mathrm{μs}\left({\color{gray}-1.322 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.83 \mathrm{ms} \pm 20.4 \mathrm{μs}\left({\color{gray}-0.432 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.52 \mathrm{ms} \pm 9.54 \mathrm{μs}\left({\color{red}7.27 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.45 \mathrm{ms} \pm 11.5 \mathrm{μs}\left({\color{red}6.35 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.50 \mathrm{ms} \pm 9.61 \mathrm{μs}\left({\color{gray}4.88 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.81 \mathrm{ms} \pm 11.6 \mathrm{μs}\left({\color{red}9.06 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.63 \mathrm{ms} \pm 12.3 \mathrm{μs}\left({\color{red}6.63 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.84 \mathrm{ms} \pm 17.9 \mathrm{μs}\left({\color{red}7.49 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.94 \mathrm{ms} \pm 13.3 \mathrm{μs}\left({\color{red}7.84 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.60 \mathrm{ms} \pm 9.94 \mathrm{μs}\left({\color{red}9.12 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.78 \mathrm{ms} \pm 13.9 \mathrm{μs}\left({\color{red}7.90 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.28 \mathrm{ms} \pm 14.1 \mathrm{μs}\left({\color{red}6.82 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.84 \mathrm{ms} \pm 12.9 \mathrm{μs}\left({\color{red}7.99 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.04 \mathrm{ms} \pm 9.85 \mathrm{μs}\left({\color{red}7.41 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.22 \mathrm{ms} \pm 17.1 \mathrm{μs}\left({\color{red}7.79 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.80 \mathrm{ms} \pm 10.9 \mathrm{μs}\left({\color{red}7.72 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.09 \mathrm{ms} \pm 15.6 \mathrm{μs}\left({\color{red}9.70 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$38.0 \mathrm{ms} \pm 173 \mathrm{μs}\left({\color{gray}0.816 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$77.0 \mathrm{ms} \pm 391 \mathrm{μs}\left({\color{gray}2.97 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$42.9 \mathrm{ms} \pm 183 \mathrm{μs}\left({\color{gray}2.97 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$44.6 \mathrm{ms} \pm 160 \mathrm{μs}\left({\color{gray}0.352 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$53.0 \mathrm{ms} \pm 335 \mathrm{μs}\left({\color{gray}2.27 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$39.1 \mathrm{ms} \pm 140 \mathrm{μs}\left({\color{gray}-0.726 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$426 \mathrm{ms} \pm 774 \mathrm{μs}\left({\color{gray}1.94 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$94.7 \mathrm{ms} \pm 389 \mathrm{μs}\left({\color{gray}1.76 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$84.0 \mathrm{ms} \pm 317 \mathrm{μs}\left({\color{gray}0.540 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$264 \mathrm{ms} \pm 778 \mathrm{μs}\left({\color{lightgreen}-12.565 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.7 \mathrm{ms} \pm 70.8 \mathrm{μs}\left({\color{gray}2.37 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$15.4 \mathrm{ms} \pm 82.9 \mathrm{μs}\left({\color{red}6.43 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.4 \mathrm{ms} \pm 83.0 \mathrm{μs}\left({\color{gray}4.75 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$15.2 \mathrm{ms} \pm 57.4 \mathrm{μs}\left({\color{red}5.74 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$18.5 \mathrm{ms} \pm 108 \mathrm{μs}\left({\color{red}6.52 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.9 \mathrm{ms} \pm 77.0 \mathrm{μs}\left({\color{red}5.51 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$15.0 \mathrm{ms} \pm 84.8 \mathrm{μs}\left({\color{red}6.02 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$15.0 \mathrm{ms} \pm 74.4 \mathrm{μs}\left({\color{red}5.17 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.6 \mathrm{ms} \pm 69.3 \mathrm{μs}\left({\color{red}5.12 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$22.9 \mathrm{ms} \pm 158 \mathrm{μs}\left({\color{gray}4.65 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity

Function	Value	Mean	Flame graphs
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/block/v/1	$$29.3 \mathrm{ms} \pm 275 \mathrm{μs}\left({\color{gray}1.26 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$29.4 \mathrm{ms} \pm 237 \mathrm{μs}\left({\color{gray}2.39 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$31.6 \mathrm{ms} \pm 355 \mathrm{μs}\left({\color{red}9.49 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$29.1 \mathrm{ms} \pm 281 \mathrm{μs}\left({\color{gray}-1.097 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$28.9 \mathrm{ms} \pm 269 \mathrm{μs}\left({\color{gray}-0.905 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$29.0 \mathrm{ms} \pm 250 \mathrm{μs}\left({\color{gray}-0.953 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$29.7 \mathrm{ms} \pm 296 \mathrm{μs}\left({\color{gray}1.65 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$30.7 \mathrm{ms} \pm 316 \mathrm{μs}\left({\color{red}7.98 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$30.2 \mathrm{ms} \pm 269 \mathrm{μs}\left({\color{gray}1.23 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$7.90 \mathrm{ms} \pm 30.9 \mathrm{μs}\left({\color{gray}0.444 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$44.7 \mathrm{ms} \pm 214 \mathrm{μs}\left({\color{gray}-0.765 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$91.8 \mathrm{ms} \pm 374 \mathrm{μs}\left({\color{gray}-0.020 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$50.5 \mathrm{ms} \pm 343 \mathrm{μs}\left({\color{gray}-1.094 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$58.5 \mathrm{ms} \pm 328 \mathrm{μs}\left({\color{gray}0.111 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$66.5 \mathrm{ms} \pm 315 \mathrm{μs}\left({\color{gray}0.423 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$72.6 \mathrm{ms} \pm 326 \mathrm{μs}\left({\color{gray}-0.407 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$48.0 \mathrm{ms} \pm 215 \mathrm{μs}\left({\color{gray}-2.274 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$75.2 \mathrm{ms} \pm 339 \mathrm{μs}\left({\color{gray}0.025 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$55.9 \mathrm{ms} \pm 308 \mathrm{μs}\left({\color{gray}1.11 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$62.8 \mathrm{ms} \pm 338 \mathrm{μs}\left({\color{gray}-0.296 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$65.2 \mathrm{ms} \pm 380 \mathrm{μs}\left({\color{gray}0.925 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$64.6 \mathrm{ms} \pm 295 \mathrm{μs}\left({\color{gray}-0.365 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$136 \mathrm{ms} \pm 423 \mathrm{μs}\left({\color{gray}-2.382 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$134 \mathrm{ms} \pm 400 \mathrm{μs}\left({\color{gray}-1.621 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$39.5 \mathrm{ms} \pm 178 \mathrm{μs}\left({\color{lightgreen}-47.037 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$577 \mathrm{ms} \pm 992 \mathrm{μs}\left({\color{red}6.79 \mathrm{\%}}\right) $$	Flame Graph

[CiaranMn]

We can deal with this in follow-up. I'm maybe 70% confident I'm right above but it can easily be verified by building the library and looking at the generated CSS file