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.
git clone --depth 1 https://github.com/kimgoetzke/coding-agent-configsWrote 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/agents/kimgoetzke/coding-agent-configs/generic-subagent)<a href="https://agentmods.dev/agents/kimgoetzke/coding-agent-configs/generic-subagent"><img src="https://agentmods.dev/badge/agents/kimgoetzke/coding-agent-configs/generic-subagent/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/agents/kimgoetzke/coding-agent-configs/generic-subagent"><img src="https://agentmods.dev/badge/agents/kimgoetzke/coding-agent-configs/generic-subagent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00025 | $0.00162 |
| Opus 5.5 | $0.00010 | $0.00065 |
| Sonnet 5.5 | $0.00005 | $0.00032 |
| Haiku 4.5 | $0.00003 | $0.00016 |
Grade A, and why
generic-subagent 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.
What it actually says
You are the fallback subagent. Complete the main agent's delegated task using local files and tools, then return a concise, evidence-based result.
Boundaries
- Do not search, browse, fetch, or otherwise research the web. Do not use network-capable shell commands as a workaround. If the task needs external information, stop and tell the main agent to use a web-research subagent.
- Stay within the delegated scope. Do not expand into unrelated review, refactoring, or investigation.
- Do not claim success without evidence from files or command output.
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 · 14 lines · 25 tokens per session scan A 105e100119ce
generic-subagent is an agent published in the GitHub repository kimgoetzke/coding-agent-configs (2 stars, last pushed 20d ago), licensed MIT. It adds 25 tokens to every session and 162 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 2026-08-31.
Other agents, from other repositories
codex-session-investigator
Answer questions about a Codex session JSONL by rendering it with session-view and inspecting the rendered transcript.
session-ask-analyst
Ask questions about a Pi session JSONL file (rehydration / forensics).
qa-reviewer
Goal-completion QA + fraud gate. Independently re-verifies delivered behavior against the goal's successCriteria, hunts edge cases and regressions, and hunts fake implementations (stubs, hardcoding, assertion theater); returns PASS or FAIL via a fixed C1–C12 checklist.
reviewer-architecture
Binary contract critic (panel seat 2 of 3) — integration lens. Attacks a drafted ClarificationGoalContract's subgoal composition, dependency edges, interface consistency, and shared-state hazards; returns APPROVE or REJECT via a fixed C1–C6 checklist.
reviewer-feasibility
Binary contract critic (panel seat 1 of 3) — feasibility lens. Attacks a drafted ClarificationGoalContract against the real codebase and returns APPROVE or REJECT via a fixed C1–C6 checklist.
reviewer-risk
Binary contract critic (panel seat 3 of 3) — coverage lens. Verifies a drafted ClarificationGoalContract fully covers the user's request, contains no placeholders, and states only binary-decidable success criteria with adequate evidence; returns APPROVE or REJECT via a fixed C1–C6 checklist.