| name | nip-to-toon-skill |
| description | Convert any Nostr NIP specification into a TOON-aware Claude Agent Skill following the 13-step pipeline. Use when asked to create a TOON skill from a NIP ("create a TOON skill for NIP-25", "convert NIP-23 to a TOON skill", "build a skill from this NIP spec"), run the NIP-to-TOON pipeline ("run the NIP-to-TOON pipeline", "execute the skill pipeline for this NIP"), produce a TOON-compatible Nostr skill ("I need a skill for reactions on TOON", "build a long-form content skill for TOON"), or ask how to make a TOON-compliant skill from any NIP specification ("how do I make a TOON-compatible skill from a NIP?", "what's the process for converting a NIP to a skill?"). Produces skill-creator-compatible output with TOON context injection, social context generation, eval creation, and compliance validation. |
NIP-to-TOON Skill Pipeline
Convert any Nostr NIP into a TOON-aware Claude Agent Skill. This pipeline produces a complete skill directory following skill-creator anatomy: SKILL.md with frontmatter, references, and evals. Every generated skill is TOON-first (ILP-gated writes, TOON-format reads) and socially aware.
NIP Classification
Before starting the pipeline, classify the target NIP. Classification determines which TOON context sections and compliance assertions are injected.
| Classification | Criteria | Injected Context |
|---|
| Read-only | NIP defines filters/queries but no new publishable event kinds (e.g., NIP-50 Search) | TOON read model, toon-format-check |
| Write-capable | NIP defines event kinds the agent creates/publishes (e.g., NIP-25 Reactions) | TOON write model, fee calculation, toon-write-check, toon-fee-check |
| Both | NIP defines both queryable and publishable event kinds (e.g., NIP-23 Long-form) | All write + read checks |
Universal checks applied regardless: social-context-check, trigger-coverage.
The 13-Step Pipeline
Step 1: NIP Analysis
Read the NIP specification. Extract:
- Event kinds defined (which ones does the agent create vs query?)
- Tag structures (required and optional tags)
- Content format (plain text, JSON, markdown, binary references)
- Filter patterns (how to query these events)
- Classify as read-only / write-capable / both
Flag any TOON-specific considerations: does this NIP overlap with an excluded NIP (NIP-13, NIP-42, NIP-47, NIP-57, NIP-98)? If so, document what ILP replaces.
Step 2: TOON Context Injection
Read toon-protocol-context.md for the canonical protocol details.
For write-capable NIPs: inject TOON write model section explaining publishEvent() usage for the specific event kind, fee calculation for typical payload sizes of this NIP's events, and error handling (F04 insufficient payment).
For read-capable NIPs: inject TOON read model section documenting that responses come in TOON-format strings, not JSON objects.
For all NIPs: inject relay discovery context (enriched NIP-11 /health endpoint, kind:10032 pricing events).
Step 3: Social Context Layer
Read social-context-template.md.
Generate a ## Social Context section specific to this NIP. The section must answer:
- When is this interaction appropriate?
- What does paying to perform this action mean socially?
- What are the context-specific norms?
- What are the anti-patterns for this interaction type?
Add a pointer to nostr-social-intelligence for deeper social judgment.
Test: Could this Social Context section apply to any NIP? If yes, it is too generic. Rewrite.
Step 4: Skill Authoring
Read skill-structure-template.md.
Generate the skill's SKILL.md:
- YAML frontmatter with ONLY
name and description (no other fields)
- Description: 80-120 words with social-situation triggers, not just protocol-technical triggers
- Body: under 500 lines / ~5k tokens, imperative form
- "When to read each reference" section
## Social Context section (from Step 3)- Level 3 references:
references/nip-spec.md, references/toon-extensions.md, references/scenarios.md
Step 5: Eval Generation
Read eval-generation-guide.md.
Generate evals/evals.json:
- 8-10
should_trigger: true queries (include social-situation triggers)
- 8-10
should_trigger: false queries (must not overlap with nostr-protocol-core or nostr-social-intelligence)
- 4-6 output evals with
id, prompt, rubric (correct/acceptable/incorrect), and assertions
Step 6: TOON Assertions
Read toon-compliance-assertions.md.
Auto-inject TOON compliance assertions into the output evals based on NIP classification:
- Write-capable:
toon-write-check, toon-fee-check
- Read-capable:
toon-format-check
- All:
social-context-check, trigger-coverage
Step 7: Description Optimization
Read description-optimization-guide.md.
Run scripts.run_loop with 20 trigger queries spanning protocol-technical and social-situation triggers. Max 5 iterations. Select best_description based on trigger accuracy.
Step 8: With/Without Testing
Spawn parallel subagents:
- With skill: Agent with the generated skill loaded, runs against output eval prompts
- Without skill: Baseline Claude without the skill, same prompts
Save results to with_skill/outputs/ and without_skill/outputs/.
Step 9: Grading
Produce grading.json from with/without comparison:
- Each assertion:
{ "text": "...", "passed": true/false, "evidence": "..." }
- Summary: total assertions, passed count, failed count
Step 10: Benchmarking
Produce benchmark.json:
- Pass rate (percentage of assertions passed)
- Timing: mean +/- standard deviation per eval
- Token usage: mean tokens per eval response
Step 11: TOON Compliance Validation
Run TOON-specific assertion checks from Step 6. Any failure = red = skill is not TOON-ready. Fix and re-run from the failing step.
Step 12: Eval Viewer
Generate HTML review via eval-viewer/generate_review.py. The review displays: trigger eval results, output eval results with assertion details, TOON compliance status, benchmark summary.
Step 13: Iterate
Collect feedback.json from the review. Read feedback, identify which steps need refinement, and re-run the pipeline from the appropriate step. Save iteration outputs to iteration-2+/ directories.
When to Read Each Reference
Social Context
This pipeline creates skills that other agents use for social interactions on TOON. The pipeline itself is a meta-tool -- it shapes how agents understand social norms across dozens of interaction types. A defect in the pipeline's social context template propagates to every downstream skill. Treat social context generation with the same rigor as protocol correctness: generic social context is a defect, not a placeholder. Every generated ## Social Context section must be specific to the NIP's interaction type, reflecting the real social dynamics of that interaction on a paid relay network.
Integration with Other Skills
nostr-protocol-core (Story 9.1): Provides the write/read model details. Generated skills reference it for protocol mechanics.nostr-social-intelligence (Story 9.0): Provides universal social judgment. Generated skills point to it for deeper social guidance.skill-creator: Defines the skill anatomy this pipeline follows. Generated skills conform to skill-creator format.
