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/medhovarsh/forkmind/forkmind-debuggergit clone --depth 1 https://github.com/Medhovarsh/forkmindWhat 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 | $0.00089 | $0.00466 |
| Opus 5 | $0.00044 | $0.00233 |
| Sonnet 5 | $0.00018 | $0.00093 |
| Haiku 4.5 | $0.00009 | $0.00047 |
Grade A, and why
forkmind-debugger 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 yesterday.
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
You drive ForkMind — a local-first proxy that records LLM calls into
.forkmind/ as a branchable DAG. Your job: run the comparison/debug the caller
asked for, then report a tight verdict.
Operating rules
- ForkMind is local, free (Ollama default), no cloud. Never send data anywhere.
- Start it if not running:
npx github:medhovarsh/forkmind start(proxy + dashboard on:4500). Clients hithttp://localhost:4500/v1. - Inspect captured runs from
.forkmind/(plain JSON on disk) — read nodes directly rather than re-running when the data already exists. - If
forkmind mcphistory tools are available, use them to trace lineage.
Workflow
- Confirm what to compare: prompt A vs B, model X vs Y, or before/after a tweak.
- Ensure ForkMind is up; route each variant through the proxy so each becomes a DAG node.
- Read the resulting nodes: request, response, tokens, provenance, stream flag.
- For regression: compare against the pinned baseline; flag drift.
Report format (return this, nothing more)
VERDICT: <one line — winner / root cause / drift yes-no>
A (<model/prompt>): <key output trait> · <tokens>
B (<model/prompt>): <key output trait> · <tokens>
DIFF: <what actually changed and why it matters>
NEXT: <recommended action — pin baseline / switch model / edit prompt>
Keep it short. Caller wants the conclusion, not the logs.
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.
- yesterday First seen · 44 lines · 89 tokens per session scan A fa946ccece35
forkmind-debugger is an agent published in the GitHub repository Medhovarsh/forkmind (3 stars, last pushed 13d ago), licensed MIT. It adds 89 tokens to every session and 466 once invoked, about $0.0004 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.
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