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Add solana-tokenomics skill (design & pressure-test token economies)#3

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Add solana-tokenomics skill (design & pressure-test token economies)#3
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solana-tokenomics — a novel tokenomics design skill

Submitted as a git submodule at the repo root (mirroring the existing submodule-style submission).

Repo: https://github.com/zupy-fmartinelli/solana-tokenomics-skill (MIT)
Also submitted to: solanabr/solana-ai-kitsolanabr/solana-ai-kit#35

Problem

Loyalty programs, rewards apps, DePIN, game economies, and fintech credits all reinvent token design and hit the same failures: dump-to-zero tokens, inflationary-yield treadmills, mercenary liquidity, broken pegs, low-float/high-FDV unlock dumps, and accidental securities/e-money exposure. Plenty of tooling helps launch a token; little helps decide what the token should be.

What it is

A working tokenomist in a skill — option-rich, not prescriptive. It gives the agent every lever and its tradeoffs:

  • supply & emission options (fixed, capped, work-based, fiat-backed, algorithmic, fair-launch), allocation, vesting
  • the faucet → sink → hold flow balance + coverage
  • demand/utility & the velocity problem
  • staking & incentives (real vs inflationary yield decomposition, ve-models, liquidity mining)
  • liquidity & markets, peg mechanisms, oracles
  • governance (token-vote/ve/quadratic, DAO treasury, timelocks, progressive decentralization)
  • compliance — securities (Howey) / e-money / commodity lines, incl. token-sale & staking-yield risk
  • Token-2022 selection + money-safe minting/settlement
  • four archetypes: closed-loop loyalty, utility/work + staking, governance/DAO, pegged/stablecoin + LST

Quality

Current to the 2026 stack (Token-2022, SPL Governance/Realms, LSTs/oracles); validated end-to-end on a real DePIN design case. Progressive disclosure via SKILL.md; pure Markdown (no executables, no network, no deps); install.sh + README + comprehensive SKILL.md. MIT.

Design & pressure-test token economies on Solana — supply/emission options,
faucet->sink->hold balance, demand/utility, staking (real vs inflationary yield),
liquidity & pegs, governance, Token-2022, money-safe settlement, and the
securities/e-money compliance lines. Four archetypes. Option-rich, MIT.

Repo: https://github.com/zupy-fmartinelli/solana-tokenomics-skill
Also submitted to solana-ai-kit (PR solanabr#35).
bhuvarloka added a commit to bhuvarloka/solana-skill-bounty that referenced this pull request Jun 28, 2026
Engine fixes:
- layout: backtrack the sizeOf `seen` set so sibling fields of the same
  defined type both resolve (was corrupting all later offsets to NaN) (solanabr#1)
- layout: resolve `kind: "type"` aliases to the aliased size (solanabr#9)
- diff: stop flagging newly-added accounts as UNSAFE; report removed
  accounts as UNSAFE instead (solanabr#3)
- diff: typeKey normalizes via typeLabel so Anchor spec spellings of the
  same defined type compare equal — no false retype warnings (solanabr#6)
- diff: detect removed instructions (was silently skipped) (solanabr#8)
- diff: detect error variants inserted before existing ones; appends stay
  safe (added variants were never inspected) (solanabr#7)
- codegen: migration V1/V2 use bare Borsh derives, not #[account], and the
  scaffold preserves the original 8-byte discriminator on read+write (solanabr#2)
- codegen: size realloc from the actual Borsh length + top up rent, instead
  of std::mem::size_of (which mishandles String/Vec and padding) (solanabr#5)
- codegen: minimalIdl keeps the full type table so nested defined types
  resolve in the generated regression test (solanabr#4)

Tests (built to catch, not to pass): the old suite ran every one of these
bugs green because it only tested the first case of each feature. Added the
adversarial second case:
- 5 fixture pairs (added/removed account, removed instruction, mid-inserted
  error, two spec spellings of one defined type) — these feed both the diff
  suite and the offline benchmark
- 2 layout tests asserting exact offsets for sibling same-type fields and
  type aliases

Suite 38/38; benchmark with-skill 100% (16/16), baseline drops to 25% as the
new cases expose failure modes the old corpus couldn't.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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