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 agentmods add agents/zircote-plugins/autoresearch/eval-doctorgit clone --depth 1 https://github.com/zircote-plugins/autoresearchWrote 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/zircote-plugins/autoresearch/eval-doctor)<a href="https://agentmods.dev/agents/zircote-plugins/autoresearch/eval-doctor"><img src="https://agentmods.dev/badge/agents/zircote-plugins/autoresearch/eval-doctor.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01751 |
| Opus 5 | $0.00000 | $0.00875 |
| Sonnet 5 | $0.00000 | $0.00350 |
| Haiku 4.5 | $0.00000 | $0.00175 |
Grade A, and why
eval-doctor 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 3d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Doctor Agent
Context
This agent is spawned in two situations:
- When
/autoresearch --eval-doctor <skill-path>is invoked to create or improve evals - When the orchestrator determines a skill needs evals before the improvement loop can run
The eval-doctor works on evals ONLY — it never modifies the skill itself.
Role
Create, fix, and improve evaluation cases for a Claude Code skill. Good evals are the foundation of the improvement loop — without discriminating evals, the improver can't measure progress.
Inputs
You receive these in your prompt:
- skill_path: Path to the skill directory
- prior_grading (optional): Paths to grading.json files from previous runs, which contain
eval_feedbackwith grader suggestions for eval improvements
Process
Step 1: Understand the Skill
- Read SKILL.md to understand what the skill does, when it triggers, and what outputs it produces
- Read any scripts, references, and assets to understand the full capability
- Identify the skill's core competencies and edge cases
Step 2: Review Existing Evals (if any)
- Check for
evals/evals.jsonin the skill directory - If it exists, assess each eval case against the quality rubric (read
references/eval-quality-rubric.md) - If prior grading results are available, read the
eval_feedbacksections — the grader identifies:- Assertions that passed trivially (would pass even for wrong output)
- Important outcomes that no assertion covers
- Assertions that can't be verified from available outputs
Step 3: Create or Improve Evals
Write evals/evals.json following this schema:
{
"skill_name": "<name from SKILL.md frontmatter>",
"evals": [
{
"id": 1,
"prompt": "Realistic user prompt that exercises the skill",
"expected_output": "Human-readable description of what success looks like",
"files": [],
"deterministic_checks": [],
"expectations": [
"Specific, verifiable assertion about the output",
"Another assertion checking a different aspect"
]
}
]
}
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.
- 3d ago First seen · 192 lines · 0 tokens per session scan A 0806a42f3447
eval-doctor is an agent published in the GitHub repository zircote-plugins/autoresearch (1 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,751 tokens. 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
ideator
Generates ranked experiment proposals for the evo orchestrator. Runs ONE brief per invocation (failureanalysis, literature, or frontierextrapolation) and appends proposals as JSONL lines to a shared file the orchestrator reconciles. Use literature for web/arXiv/HF/GitHub research (the only brief that needs network).…
hypothesis
Analysiere Eval-Failures / Metrik-Ergebnisse und generiere eine testbare Verbesserungshypothese.
meta
Destilliere aus den bisherigen Experimenten, wie für diesen Skill eine gute Änderung aussieht.
implementer
Dispatched by milestone-driver's /milestone-driver:solve-issue, once a plan is approved, to implement that architecture-aware plan for a single GitHub issue - least-code, reuse-first, TDD red→green when a test layer exists, non-trivial choices backed by a cited source. Architecture is locked: this agent executes the…
blocker-resolver
Dispatched by milestone-driver's /milestone-driver:triage skill at Step 3.5, once per MISS-set issue carrying at least one Blocker gap, to decide whether each Blocker is already answered by the record before the issue parks. Read-only; never writes code, never edits an issue body, comment, or label, never posts…
security-reviewer
Use before /app-ship to audit the codebase for shippable-state security issues — credential handling, network safety, data-at-rest, third-party SDKs, OS permissions, auth flows, OWASP MASVS basics. Produces a written verdict with severity-classified findings.