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 thienanblog/awesome-ai-agent-skills --skill agents-md-generatorgit clone --depth 1 https://github.com/thienanblog/awesome-ai-agent-skillsWrote 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/thienanblog/awesome-ai-agent-skills/agents-md-generator)<a href="https://agentmods.dev/skills/thienanblog/awesome-ai-agent-skills/agents-md-generator"><img src="https://agentmods.dev/badge/skills/thienanblog/awesome-ai-agent-skills/agents-md-generator/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/thienanblog/awesome-ai-agent-skills/agents-md-generator"><img src="https://agentmods.dev/badge/skills/thienanblog/awesome-ai-agent-skills/agents-md-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Memory Poisoning · line 48 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
- medium Memory Poisoning · line 77 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.00044 | $0.00892 |
| Opus 5.5 | $0.00018 | $0.00357 |
| Sonnet 5.5 | $0.00009 | $0.00178 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
agents-md-generator 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 27d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md Generator
Produce a small instruction layer containing verified project facts that materially change agent behavior. General engineering knowledge belongs outside always-loaded instructions.
Delegation
Run in the main conversation by default. Delegation can increase usage: obtain explicit approval for the proposed agent count and scope before using subagents. Reuse that approval within its bounds; ask again before expanding the approved count or scope.
Discover the instruction chain
Inspect existing instructions, their ownership, scopes, and symlinks before editing. Preserve a working shared source such as AGENTS.md pointing to CLAUDE.md; do not replace it solely to impose a preferred filename. Add compatibility files only when the requested tool support needs them. Keep global user configuration outside a repository-instruction task.
For an unfamiliar or multi-tool repository, the bundled detector can locate instruction paths and declared framework/tool signals:
"<skill-directory>/scripts/detect-agent-context" --root "<repository-root>" --format json
On Windows use scripts/detect-agent-context.cmd with the same arguments. Python provides JSON; native Bash/PowerShell fallbacks emit a smaller text report. Resolve scripts relative to this skill, not the target project. Direct file inspection is sufficient for a known, narrow edit.
The detector indexes selected public manifests and instruction/config paths without reading credentials, environment files, or instruction contents. Treat it as an index: read the applicable instructions and verify commands in their source before persisting them. Include global paths only when that investigation is authorized and relevant.
Read discovery.md for detector details or tool-compatibility.md when loading order and supported tool behavior affect the output.
Select durable rules
Keep a rule when it is non-obvious, supported by project evidence, useful across tasks, and concrete enough to follow. Good candidates include exact command runners, generated-file ownership, unusual business boundaries, required checks, and repository-specific deployment or approval constraints.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 260 B
- references/discovery.md 5.1 KB
- references/merge-and-verify.md 3.2 KB
- references/output-template.md 2.6 KB
- references/tool-compatibility.md 3.0 KB
- scripts/detect-agent-context 587 B
- scripts/detect-agent-context.cmd 1.8 KB runs code
- scripts/detect-agent-context.ps1 7.0 KB runs code
- scripts/detect-agent-context.py 32 KB runs code
- scripts/detect-agent-context.sh 8.1 KB runs code
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.
- 27d ago Changed · -2 lines 8f6af6189385
- 1mo ago Changed · -126 lines · -35 tokens per session 4c6c5a3f0b99
- 1mo ago First seen · 181 lines · 79 tokens per session scan A 8fdd1aacf6b6
agents-md-generator is a skill published in the GitHub repository thienanblog/awesome-ai-agent-skills (70 stars, last pushed 27d ago), licensed Apache-2.0. It adds 44 tokens to every session and 892 once invoked, about $0.0002 per session on Opus 5.5. 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.
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