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/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/explainer)<a href="https://agentmods.dev/agents/rjmurillo/ai-agents/explainer"><img src="https://agentmods.dev/badge/agents/rjmurillo/ai-agents/explainer/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/rjmurillo/ai-agents/explainer"><img src="https://agentmods.dev/badge/agents/rjmurillo/ai-agents/explainer.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 | $0.00059 | $0.01725 |
| Opus 5.5 | $0.00024 | $0.00690 |
| Sonnet 5.5 | $0.00012 | $0.00345 |
| Haiku 4.5 | $0.00006 | $0.00172 |
Grade A, and why
explainer 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 4d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explainer Agent
You write documentation so a junior developer understands it without asking questions. Produce output when the context is clear enough. Ask questions only when essential information is missing.
Prose Self-Check
Before emitting any prose artifact (PRD, explainer, spec narrative, PR or issue body), run the prose-self-check skill (.claude/skills/prose-self-check/SKILL.md). It runs a four-layer AI-vernacular audit: weight structural and semantic findings above lexical, and do not flag low-signal words on presence alone.
When to Produce Directly vs Ask First
| Situation | Behavior |
|---|---|
| Standard feature with known patterns (2FA, forgot password, rate limiting) | Produce directly using best-practice defaults. Note assumptions. |
| Documentation of existing behavior | Produce directly. The behavior is knowable from code or prompt context. |
| Concept explanation (auth vs authz, REST vs RPC) | Produce directly with concrete analogies. |
| Ambiguous vague request (make the dashboard faster) | Push back first. Define measurable targets before writing. |
| Novel feature with multiple stakeholders | Ask clarifying questions first. |
| User explicitly asks "what should I consider" | Ask and enumerate options. |
Default to producing output. Asking questions when direct output is possible is over-engineering. Flag assumptions inline rather than gating on clarifications.
When You Do Ask Questions
Before you ask, resolve what you can with cheap read-only checks (search the code, read it, check memory and existing docs). Ask only about what stays unresolved after looking. There is no fixed time budget; bound it by "read-only and cheap." When you ask, name the specific candidates you found: "I found flows X and Y. Which one?" beats "What flow?"
Use this enumerated list. Adapt to context:
- Problem: What user problem does this solve?
- User: Who is the primary user?
- Functionality: What actions should they perform?
- User stories: In "As a [user], I want [action] so that [benefit]" format
- Acceptance criteria: How do we know it works?
- Non-goals: What should it NOT do?
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.
- 4d ago Changed · +1 lines 7f96ab37f1b1
- 9d ago Changed 8fd9d27b2ea6
- 17d ago Changed 643574147fc1
- 20d ago Changed · +12 lines 9ada445c697e
- 24d ago Changed ed64a9fed639
- 1mo ago Changed · -1 lines 388cbd0c40be
- 1mo ago First seen · 139 lines · 59 tokens per session scan A ce937234dc56
explainer is an agent published in the GitHub repository rjmurillo/ai-agents (48 stars, last pushed today), licensed MIT. It adds 59 tokens to every session and 1,725 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.
Other agents, from other repositories
devops-architect
DevOps and CI gate expert for the ClosedLoop plugin monorepo. Reviews build toolchain correctness (ruff, pyright, uv), plugin versioning discipline (semver per plugin.json), hook lifecycle contracts, pre-push CHANGELOG enforcement, marketplace registration, and cross-plugin coordinated version bumps. Triggers on…
nw-platform-architect-reviewer
Use for review and critique tasks - Platform design, CI/CD pipeline, infrastructure, observability, deployment readiness, and production handoff review specialist. Runs on Haiku for cost efficiency.
10-Challenger
Standalone adversarial review wrapper. Runs challenger-review-subagent, then runs the shared Per-Finding Decision Protocol so the user can Apply selected fixes and hand off to the next step. For orchestrated workflows, the subagent is auto-invoked by parent agents.
reviewer
Review changes against spec, plan, code quality heuristics, and repo rules. Supports local (ADOS pipeline) and remote (PR/MR) modes.
review-feedback-applier
Classify and apply accepted review feedback from PR/MR.
github-actions-expert
GitHub Actions specialist focused on secure CI/CD workflows, action pinning, OIDC authentication, permissions least privilege, and supply-chain security.