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/quantumbfs/sci-brain/flownpx skills add QuantumBFS/sci-brain --skill flowgit clone --depth 1 https://github.com/QuantumBFS/sci-brainWhat 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 | $0.00087 | $0.02793 |
| Opus 5 | $0.00044 | $0.01396 |
| Sonnet 5 | $0.00017 | $0.00559 |
| Haiku 4.5 | $0.00009 | $0.00279 |
Grade A, and why
flow 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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Flow
A deep-thinker that conquers one hard problem by autonomous search, modeled on a CDCL/DPLL SAT solver. Given a goal, it iterates — assume, follow consequences, hit walls, learn from the walls, jump back, and re-aim when truly stuck — until a solution emerges or it converges on an equally-valuable reachable goal.
Scope. Goal-locked, fully autonomous, domain-agnostic. This is not brainstorm-ideas
(open-ended, collaborative, research-only). Use flow when you already have a specific hard target
and want a relentless solver thrown at it. KB-optional: if <project>/.knowledge/INDEX.md exists,
treat its papers as a fact source for propagation; never require it.
The mental model
| Solver concept | Here |
|---|---|
| Variable assignment (Decide) | what-if: assume a new condition |
| Unit propagation (BCP) | simulate: run consequences forward, reflect |
| Conflict | a contradiction or dead-end on the current branch |
| Conflict analysis + learned clause | the note taken after every trial |
| Non-chronological backtracking | backjump to the real cause, not one step |
| Restart (clauses kept) | pivot: re-aim the goal; notes survive |
| Decision heuristic (VSIDS) | pick the lever that shrinks distance most + is easiest; favor conditions seen in recent conflicts |
State to track
Maintain these throughout (in the journal file, see below):
- GOAL — restated crisply, with a concrete success test: how will I know it is solved?
- TRAIL — ordered list of decisions + propagated facts, each tagged with a decision level.
- GOAL_STACK — subgoals spawned by what-if recursion (current goal on top).
- NOTES — learned clauses:
trigger conditions → outcome → reusable lesson. These are the whole point — they prune future search and guide backjumps. - distance — a rough estimate of how far the current state is from GOAL.
- no_progress — consecutive trials with no distance drop (drives PIVOT).
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.
- 2d ago First seen · 204 lines · 87 tokens per session scan A a8a3cda63453
flow is a skill published in the GitHub repository QuantumBFS/sci-brain (81 stars, last pushed 5d ago), licensed MIT. It adds 87 tokens to every session and 2,793 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 2026-08-30.
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