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 fmflurry/settings-opencode --skill ai-ecosystem-auditgit clone --depth 1 https://github.com/fmflurry/settings-opencodeWrote 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/fmflurry/settings-opencode/ai-ecosystem-audit)<a href="https://agentmods.dev/skills/fmflurry/settings-opencode/ai-ecosystem-audit"><img src="https://agentmods.dev/badge/skills/fmflurry/settings-opencode/ai-ecosystem-audit/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/fmflurry/settings-opencode/ai-ecosystem-audit"><img src="https://agentmods.dev/badge/skills/fmflurry/settings-opencode/ai-ecosystem-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00031 | $0.01297 |
| Opus 5.5 | $0.00012 | $0.00519 |
| Sonnet 5.5 | $0.00006 | $0.00259 |
| Haiku 4.5 | $0.00003 | $0.00130 |
Grade A, and why
ai-ecosystem-audit 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 1mo 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI ecosystem audit rubric
Use this rubric only for the reusable, read-only audit of repository-controlled OpenCode and Claude Code assets.
Inputs
/ecosystem-audit [--scope=all|parity|skills|agents|commands|config] [--strict] [--relevance=gc-platform|portable]
Defaults: --scope=all, --relevance=gc-platform.
allaudits every dimension.parityaudits mirrored OpenCode/Claude artifacts and necessary harness translations.skills,agents,commands, andconfigfocus detailed analysis on that artifact class, retaining only reference checks necessary for interpretation.--strictpromotes unverified references, incomplete mirrors, undocumented differences, and absent purpose/trigger/owner/output/mutation boundaries from risks/unknowns to findings. It never converts an inference into a confirmed fact.--relevance=gc-platformuses current repository stack markers and documented conventions.portableevaluates reusable harness design without requiring gc.platform-specific fit.
Audit boundary
Audit repository-controlled assets under .opencode/ and .claude/, plus project stack markers necessary to establish relevance. Exclude user-home configuration, secrets and secret-bearing files, node_modules, generated/build/cache directories, runtime state/runtime configuration, dependency lockfile contents, and product-source quality.
The audit is read-only: no edits, generated files, installs, hook execution, commits, remote actions, or delegation. Redact sensitive values.
Evidence standard
- Raw files in the current working tree are authoritative.
- Use CodeMemory first for orientation only when it is both available and permitted; treat it only as navigation context because it may be stale or incomplete.
- Cite every finding and factual inventory assertion as
path:line. - Classify conclusions as confirmed (direct current-file evidence), likely (bounded inference with evidence), or unknown (not established; name the missing evidence).
- Verify referenced paths, command routes, agents, prompts, skills, instruction links, and allowlists directly.
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.
- 1mo ago First seen · 112 lines · 31 tokens per session scan A 98fece5fb76c
ai-ecosystem-audit is a skill published in the GitHub repository fmflurry/settings-opencode (171 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 1,297 once invoked, about $0.0001 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 2026-09-03.
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