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 QuantumBFS/sci-brain --skill how-to-analyze-dialoggit clone --depth 1 https://github.com/QuantumBFS/sci-brainWrote 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/quantumbfs/sci-brain/how-to-analyze-dialog)<a href="https://agentmods.dev/skills/quantumbfs/sci-brain/how-to-analyze-dialog"><img src="https://agentmods.dev/badge/skills/quantumbfs/sci-brain/how-to-analyze-dialog/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/quantumbfs/sci-brain/how-to-analyze-dialog"><img src="https://agentmods.dev/badge/skills/quantumbfs/sci-brain/how-to-analyze-dialog.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 | $0.00029 | $0.02011 |
| Opus 5.5 | $0.00012 | $0.00804 |
| Sonnet 5.5 | $0.00006 | $0.00402 |
| Haiku 4.5 | $0.00003 | $0.00201 |
Grade A, and why
how-to-analyze-dialog 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 1mo 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Installed resources
Keep the working directory at the user's project. Resolve this loaded SKILL.md
with Path(path).resolve() before locating resources; follow symlinks. Bare
helpers/, references/, and template paths are relative to that real skill
directory. A path written as skills/<name>/... means the installed <name>
skill's directory from the agent's skill catalog, not a path in the user's project.
Locate each dependency by its public skill name; copied skills need not be siblings.
If a dependency is absent, report the missing skill and install it before that step.
Shared writing files are bundled in how-to-write-ideas-report/references/.
Research dialog analysis
Classify research conversations and their prompting patterns across the six
academic dimensions below. This skill consumes exported history; it does not
select harnesses, scan native stores, or make transcript/PDF exports.
dump-chat-history owns that workflow. create-advisor owns advisor synthesis.
Phase 1 — Load exported dialog
Accept existing session/turn JSON files or history.json produced by
dump-chat-history. If history has not been exported, invoke that skill first,
passing any source, time range, project, and topic already provided; do not ask
the same selection questions again. Request its analysis handoff without a PDF
unless the user also wants a readable report.
For a history.json input, resolve the installed dump-chat-history directory
as DUMP_HISTORY_DIR and create a derived analysis workspace:
python3 "$DUMP_HISTORY_DIR/helpers/history.py" analysis <history.json> \
--outdir docs/dialog/analysis/<run-slug>/input
For existing session/turn JSON, copy the selected files into that fresh input/
directory first, retaining source locations and avoiding filename collisions.
Keep the original export immutable. The derived files retain source entry IDs
and full text; turn grouping follows transcript order within each session and
branch and is not evidence that a queued correction caused a particular answer.
If the export omits assistant messages, state that context limitation in analysis.
Use a fresh docs/dialog/analysis/<run-slug>/ for the classification outputs.
In Phases 2–4 below, <source> denotes this run workspace relative to
docs/dialog/; never write the enriched files back into the export directory.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 175 lines · 29 tokens per session scan A f41dac49c54a
how-to-analyze-dialog is a skill published in the GitHub repository QuantumBFS/sci-brain (101 stars, last pushed 23d ago), licensed MIT. It adds 29 tokens to every session and 2,011 once invoked, about $0.0001 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 2026-09-11.
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