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 skills add productstein/holomime --skill diagnosegit clone --depth 1 https://github.com/productstein/holomimeWrote 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/skills/productstein/holomime/diagnose)<a href="https://agentmods.dev/skills/productstein/holomime/diagnose"><img src="https://agentmods.dev/badge/skills/productstein/holomime/diagnose/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/skills/productstein/holomime/diagnose"><img src="https://agentmods.dev/badge/skills/productstein/holomime/diagnose.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 | $0.00075 | $0.00492 |
| Opus 5.5 | $0.00030 | $0.00197 |
| Sonnet 5.5 | $0.00015 | $0.00098 |
| Haiku 4.5 | $0.00007 | $0.00049 |
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 2mo 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
Behavioral Diagnosis
Run holomime's 8 rule-based detectors against conversation logs to identify behavioral drift patterns.
Usage
npx holomime diagnose $ARGUMENTS
If no path is provided, look for conversation logs in the current directory. Common locations:
.holomime/logs/— holomime alignment session logs- Exported chat transcripts (Claude, ChatGPT, JSONL)
What It Detects
| Detector | What It Catches |
|---|---|
| Apology | Excessive "sorry", "apologize", "pardon" |
| Hedging | "maybe", "perhaps", "possibly" stacking (3+ per response) |
| Sycophancy | Excessive agreement, especially with contradictions |
| Boundary | Should-refuse situations, boundary violations |
| Error Spiral | Repeated errors without recovery |
| Sentiment | Skewed positive/negative ratio |
| Formality | Register inconsistency (formal ↔ informal oscillation) |
| Retrieval | Fabrication, hallucination, overconfidence markers |
For full detector reference, see detectors.md.
Output
Each pattern found includes:
- Severity: concern (high) or warning (moderate)
- Prevalence: percentage of responses affected
- Examples: actual excerpts from the conversation
Gotchas
- If you get "no logs found", specify the path explicitly:
npx holomime diagnose ./path/to/logs - Supported formats:
claude(Claude export),openai(ChatGPT export),jsonl(generic),otel(OpenTelemetry) - This is rule-based (regex + word patterns) — zero LLM API calls, runs in < 5 seconds
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.
- 2mo ago First seen · 49 lines · 75 tokens per session scan A 4295682c5a64
diagnose is a skill published in the GitHub repository productstein/holomime (1 stars, last pushed 7mo ago), licensed MIT. It adds 75 tokens to every session and 492 once invoked, about $0.0003 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.
diagnose compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| diagnoseproductstein/holomime | Skill | 1 | 7mo ago | MIT | A | 75 | Claude Code |
| learning-from-experienceFlyFission/nuclear-grade-context-engineering | Skill | 33 | 2d ago | MIT | A | 65 | Claude Code |
| reporting-shared-defectsFlyFission/nuclear-grade-context-engineering | Skill | 33 | 2d ago | MIT | A | 93 | Claude Code |
| responding-to-incidentsFlyFission/nuclear-grade-context-engineering | Skill | 33 | 2d ago | MIT | A | 80 | Claude Code |
| thumbgate-doctorIgorGanapolsky/ThumbGate | Skill | 30 | today | MIT | A | 168 | Claude Code |
| prevention-rulesIgorGanapolsky/ThumbGate | Skill | 30 | today | MIT | A | 18 | Cursor |
| shellcheck-configurationaisa-group/skill-inject | Skill | 101 | 4d ago | MIT | B | 35 | Claude Code, Codex |