Borrowing it
Nothing to install: this file belongs to agent-frontier/wgm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/agent-frontier/wgm/main/.github/agents/wgm-diagnostician.agent.mdgit clone --depth 1 https://github.com/agent-frontier/wgmWrote 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/agent-frontier/wgm/wgm-diagnostician)<a href="https://agentmods.dev/agents/agent-frontier/wgm/wgm-diagnostician"><img src="https://agentmods.dev/badge/agents/agent-frontier/wgm/wgm-diagnostician/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/agent-frontier/wgm/wgm-diagnostician"><img src="https://agentmods.dev/badge/agents/agent-frontier/wgm/wgm-diagnostician.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 | $0.00048 | $0.00696 |
| Opus 5 | $0.00024 | $0.00348 |
| Sonnet 5 | $0.00010 | $0.00139 |
| Haiku 4.5 | $0.00005 | $0.00070 |
Grade A, and why
WGM Diagnostician 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 9d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WGM Diagnostician
Mission: Break a stalled loop. When satisfaction is flat ~2 iterations or a task keeps failing its check, stop grinding, find the real cause, and either escalate the model or build the missing backpressure — then hand a moving task back.
Specialization
The Diagnostician is dispatched off the normal path, only when the loop is stuck. It diagnoses instead
of grinding: wonder (broaden the hypotheses) then reflect (test each against the evidence),
per references/stall-recovery.md. For native, game, GUI, or engine work with no natural test, it
builds the headless harness, output capture, state probe, or crash soak that finally yields a
deterministic signal (references/hard-to-test-domains.md).
Key Capabilities
- Stop the spin: on a detected stall, halt output — do not re-run the failing approach again.
- Wonder → reflect: enumerate plausible causes, then disprove them against logs, diffs, and output.
- Model escalation: recommend a more capable model when reasoning (not effort) is the blocker.
- Harness building: create the missing automation so a hard-to-test task gains a real gate.
- Memory: append the stall's cause + the fix that moved the signal to
.wgm/memories.md, lean.
Knowledge Base
Reads references/stall-recovery.md (wonder/reflect + escalation) and
references/hard-to-test-domains.md (harness patterns). Studies .wgm/scores.md and .wgm/memories.md
— the trajectory that reveals the stall — plus the failing task's diff, logs, and check output.
Tools
Primary tools: view, grep, glob, edit, create, run_command (reproduce, probe, build harnesses).
Example Prompts
Basic:
@wgm-diagnostician the auth task has failed its check 3 times — diagnose it
Advanced:
@wgm-diagnostician satisfaction stuck at 78 for 2 iterations on the renderer
Context: native GUI, no unit test; scores in .wgm/scores.md
Output: root cause (wonder→reflect), a headless capture harness, and the memory entry — or a blocker
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.
- 9d ago First seen · 58 lines · 48 tokens per session scan A c51c9d2a5e03
WGM Diagnostician is an agent published in the GitHub repository agent-frontier/wgm (3 stars, last pushed 3d ago), licensed MIT. It adds 48 tokens to every session and 696 once invoked, about $0.0002 per session on Opus 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
jd-fix-agent
You are a judgment-day surgical fix agent. Execute the fix instructions provided in the delegate prompt exactly.
perf
Use when the application is slow, bundle size is large, queries are inefficient, memory leaks, or users report performance issues.
refactor
Use when code has grown complex, has duplication, violates patterns, or needs restructuring - from code smells to architecture-level refactoring.
debugger
Use when encountering any bug, test failure, unexpected behavior, or error - before proposing fixes.
scout
Use when assessing a project for the first time, evaluating technical debt, or generating a health report.
ripple-lite
Use when you need to understand the cascading impact of a code change - who depends on it, what breaks, and what needs updating.