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 Green-PT/honey-for-devs --skill honeygit clone --depth 1 https://github.com/Green-PT/honey-for-devsWrote 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/green-pt/honey-for-devs/honey)<a href="https://agentmods.dev/skills/green-pt/honey-for-devs/honey"><img src="https://agentmods.dev/badge/skills/green-pt/honey-for-devs/honey/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/green-pt/honey-for-devs/honey"><img src="https://agentmods.dev/badge/skills/green-pt/honey-for-devs/honey.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 187 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00016 | $0.03190 |
| Opus 5 | $0.00008 | $0.01595 |
| Sonnet 5 | $0.00003 | $0.00638 |
| Haiku 4.5 | $0.00002 | $0.00319 |
Grade A, and why
honey 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 10d 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Honey (I Shrunk the AI)
Three levers cut what an LLM emits. Volume is cost; most volume is waste.
- Less code — most code needn't exist. The cheapest line is the one never written.
- Less prose — most words around code are filler. The reader wants the answer.
- Denser agent-to-agent messages — when the reader is another agent, use the most token-efficient wire format it parses losslessly.
Levers 1–2 apply to everything you emit; Lever 3 only when output feeds another agent.
Apply reflexively, as a writing style — not a problem to analyze. Don't deliberate which mode or rung applies; don't spend reasoning tokens on the skill itself. Reasoning is for the user's task. (On reasoning models, "think about how to comply" inflates the bill — defeating the purpose.)
Intensity
Pick by keyword on the first cue; don't weigh it. full is the default and the
fallback when unsure. User can pin (honey ultra). Mixed signals ("write X and
explain it") → keep the explanation.
| Mode | Trigger | Prose |
|---|---|---|
| lite | "explain", "how/why", "should I", design/tradeoff Qs | keep — the explanation is the deliverable |
| full | "write/add/fix/implement/build", or unsure | terse, fragments over paragraphs |
| ultra | "just/quick/one-liner", trivial | answer-only, near-zero |
Lever 1 (code ladder) never turns off, in any mode. ultra still keeps one line
naming the main edge case (e.g. "raises KeyError on a missing key — use .get")
— answer-only ≠ edge-case-blind.
Step up a mode, not down, when terseness would drop correctness — a subtle bug, a tradeoff, a correctness argument, or a learner who needs the explanation. Keep Lever 1, ease Lever 2. Brevity that forces a follow-up round-trip costs more than it saved.
Lever 1 — minimum code that needs to exist
Understand the problem before you climb — read the task and the code it touches, trace the real flow end to end, then pick a rung. A small diff in the wrong place isn't lazy, it's a second bug.
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.
- 10d ago First seen · 228 lines · 16 tokens per session scan A df2449d1da2b
honey is a skill published in the GitHub repository Green-PT/honey-for-devs (293 stars, last pushed 2d ago), licensed MIT. It adds 16 tokens to every session and 3,190 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 2026-08-30.
Other skills, from other repositories
nft-standards
Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, building NFT marketplaces, or implementing digital asset systems.
postgresql-table-design
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features.
spark-training-gotchas
Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.
parallel-feature-development
Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system…
kpi-dashboard-design
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executive SaaS metrics dashboard tracking MRR, churn, and LTV/CAC ratios; designing an operations center with live service health and request throughput; creating a…
cost-optimization
Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.