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 commands/phuryn/pm-brain/hypothesizegit clone --depth 1 https://github.com/phuryn/pm-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 | $0.00000 | $0.00789 |
| Opus 5 | $0.00000 | $0.00394 |
| Sonnet 5 | $0.00000 | $0.00158 |
| Haiku 4.5 | $0.00000 | $0.00079 |
Grade A, and why
hypothesize 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/hypothesize
Generate or refresh hypotheses for a feature. Works pre-ship (proactive, organized by the 5 risk areas) or post-ship (data-derived from analytics / interviews / churn — "why is retention dropping?"). Same schema either way; the Origin field distinguishes them.
Input
A feature slug (/hypothesize weekly-digest), a problem statement (/hypothesize "mid-market onboarding drop-off"), or an existing hypothesis file to refresh (/hypothesize hypotheses/weekly-digest.md).
If the feature has no existing file yet, the command creates one. If it does, the command refreshes — opening new candidate hypotheses where evidence has accumulated, and surfacing any existing ones whose evidence has gone stale.
Loads
hypotheses/<slug>.mdif it exists (refresh mode)knowledge/product/features/<slug>.md(if the feature exists in product knowledge)knowledge/strategy.md— non-goals, priorities, north-star (so the hypotheses don't violate strategic constraints)knowledge/users/insights.mdand any relevantpersonas.md/segments.md- Recent
ingestion/filtered to this feature / problem space (interviews, meeting notes, market signals — last ~10 entries) decisions/filtered to ones that constrain or invalidate hypothesis space for this featurehypotheses/_SCHEMA.md— load before writing or refreshing any hypothesis file
Updates
hypotheses/<slug>.md— created (new feature) or appended (refresh mode). Each new hypothesis carries: belief, origin (proactive | data-derived from<source>), confidence (always start atlowfor new hypotheses), evidence-for / evidence-against (tagged), open questions, test plan, decision trigger, status (active).hypotheses/INDEX.md— add the file under the appropriate status section if newly created; update status if a hypothesis flipped state.- Maintenance log entry — one-line note that hypotheses were generated/refreshed for
<feature>with a count of new and modified.
Hard constraints:
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 · 42 lines · 0 tokens per session scan A 824688d9bbee
hypothesize is a command published in the GitHub repository phuryn/pm-brain (537 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 789 tokens. 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.
Other commands, from other repositories
obsidian-ingest
Ingest a source into the vault - the vault rewrites itself around new knowledge. Every ingest updates entities, rewrites stale claims, synthesizes new concepts, and resolves contradictions.
create-command
Create a new obsidian-second-brain command via interview - zero markdown editing required.
obsidian-architect
Scan a codebase and write a maintained set of architecture notes into the vault - overview, per-module notes, key decisions. Re-run to refresh without clobbering your edits.
obsidian-visualize
Generate a visual canvas map of your vault - see the shape of your second brain and how knowledge connects.
podcast
Extract metadata, transcript, and summary from a podcast episode, saved as an AI-first note in the vault.
youtube
Extract transcript, metadata, and top comments from a YouTube video - summarized via Gemini (free tier) or Grok and saved to vault. Add --visual to also read the video's frames (scene detection).