Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jonathanung/finesse --skill meta-promptinggit clone --depth 1 https://github.com/jonathanung/finesseWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jonathanung/finesse/meta-prompting)<a href="https://agentmods.dev/skills/jonathanung/finesse/meta-prompting"><img src="https://agentmods.dev/badge/skills/jonathanung/finesse/meta-prompting.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00015 | $0.04468 |
| Opus 5 | $0.00008 | $0.02234 |
| Sonnet 5 | $0.00003 | $0.00894 |
| Haiku 4.5 | $0.00002 | $0.00447 |
Grade A, and why
meta-prompting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 408 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph-Loop Prompt Construction
When drafting a ralph-loop prompt via Finesse, follow these rules. A ralph-loop prompt is NOT a conversation — it's a specification for an autonomous agent that re-reads it from scratch on every iteration with zero memory.
The 10 Mandatory Attributes
Every prompt you produce MUST have all 10. If any are missing, the validation agents will reject it.
1. Binary Completion Criteria
Every requirement must be checkable by running a command. If the agent can't programmatically verify success, it can't know when to stop.
- YES: "All tests pass", "Coverage > 80%", "No linter errors", "API returns 200 for GET /users"
- NO: "Code is clean", "Make it good", "Properly structured"
2. Explicit Completion Signal
The prompt must state exactly what to output and when:
"Output <promise>COMPLETE</promise> when ALL of the following are true: [list]."
Never leave this implicit.
3. Self-Diagnosing Failure Instructions
The agent WILL get stuck. The prompt must say what to do when stuck:
"If stuck on the same error for 3+ attempts, step back, read the error trace fully, and try an alternative approach. If blocked for N+ iterations, document what's blocking progress and output <promise>BLOCKED</promise>."
4. Ordered Phases
"Build auth, then products, then cart" gives clear progression. A flat list of 20 unordered requirements is ambiguous about what to tackle next when the agent has no memory of what it was planning.
5. Verification Commands Baked In
Don't assume the agent will figure out how to check its work:
"After each change, run npm test and npm run lint. If either fails, fix before moving on."
6. Guardrails Against Failure Modes
Explicit "Do NOT" rules based on known pitfalls:
- "Do NOT rewrite files from scratch. Make targeted edits."
- "Do NOT delete existing tests to make the suite pass."
- "Read the actual error message before attempting a fix."
- "Do NOT add unnecessary abstractions or extra files."
Note: Git commit and push rules are configured per-plan during Plan Construction — see Git Configuration Rules section below.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 408 lines · 15 tokens per session scan A 6c62f37ecdbf
meta-prompting is a skill published in the GitHub repository jonathanung/finesse (4 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 4,468 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and boundary review.
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…