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 skills/drvoss/everything-copilot-cli/diagnosenpx skills add drvoss/everything-copilot-cli --skill diagnosegit clone --depth 1 https://github.com/drvoss/everything-copilot-cliWhat 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 | $0.00034 | $0.00825 |
| Opus 5 | $0.00017 | $0.00413 |
| Sonnet 5 | $0.00007 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
diagnose 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 2d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose
Diagnose is for problems that are still poorly shaped. Before deep debugging, build the smallest feedback loop that proves whether each change helps or hurts. A fast loop usually does more for bug finding than another round of guesswork.
When to Use
- The symptom is real, but the shortest reliable repro is still unclear
- A bug, regression, or performance issue needs a faster test loop before fixing
- Multiple causes seem plausible and you need to rank them instead of chasing all of them
- The system is large enough that targeted instrumentation beats broad logging
When NOT to Use
| Instead of diagnose | Use |
|---|---|
| You already have a stable repro and need root-cause discipline | systematic-debugging |
| The failure is a compiler, type, or dependency error | fix-build-errors |
| The issue is security-sensitive | security-scan or pr-security-review |
The 6-Step Loop
1. Build the feedback loop first
Create the fastest signal that tells you whether you are closer to the answer:
- a narrow failing test
- a single command that reproduces the symptom
- a benchmark or script with stable inputs
If a proposed fix does not improve that loop, it is too early to trust it.
2. Reproduce
Capture the exact symptom, input, and environment. Shrink it until it is cheap to rerun.
3. Rank 3-5 hypotheses
Do not hold one vague hunch in your head. Write a short ranked list:
- most likely
- plausible alternative
- annoying edge case
Then test them in order, demoting the ones the evidence weakens.
4. Instrument narrowly
Add only the probes needed to separate the top hypotheses. Prefer:
- one focused log or metric
- one temporary assertion
- one small trace around the suspect boundary
Avoid "log everything" unless you have no tighter cut.
5. Fix and add the regression check
Once one hypothesis is confirmed, make the smallest durable fix and lock it in with the same feedback loop that exposed it.
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.
- 2d ago First seen · 107 lines · 34 tokens per session scan A b3688445e459
diagnose is a skill published in the GitHub repository drvoss/everything-copilot-cli (45 stars, last pushed 6d ago), licensed MIT. It adds 34 tokens to every session and 825 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 2026-08-30.
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