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/toml0006/hive-mind/decidegit clone --depth 1 https://github.com/toml0006/hive-mindWrote 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/commands/toml0006/hive-mind/decide)<a href="https://agentmods.dev/commands/toml0006/hive-mind/decide"><img src="https://agentmods.dev/badge/commands/toml0006/hive-mind/decide.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.00853 |
| Opus 5 | $0.00005 | $0.00426 |
| Sonnet 5 | $0.00002 | $0.00171 |
| Haiku 4.5 | $0.00001 | $0.00085 |
Grade A, and why
decide 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 4d 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
Capture a decision from this conversation into the hive-mind decision graph.
Arguments (optional): $ARGUMENTS — the decision, stated plainly. If empty, infer the most recent settled decision from the conversation.
Steps:
- Draft the record from the conversation. Do not invent content; if a field has no
support in the conversation, leave it empty and say so.
- statement — the ruling, one sentence, imperative. "Use X, not Y."
- decision_key — a stable lowercase kebab-case topic that survives wording
changes, such as
python-package-manager. - rationale — why. Cite the actual constraint that forced it.
- alternatives_rejected — every approach considered and dropped, each with its reason. This field is the point of the exercise: it is what stops the same approach being proposed again next month.
- scope —
repo(this repo),client(all of this client's repos), orpractice(every project, engineering-craft rulings). - decided_at — when the ruling was settled. Use the conversation timestamp; never substitute the current time when recording an older ruling.
- Show the draft and ask for confirmation or edits. Do not call the tool yet.
- On confirmation, call
record_decisionwith the confirmed fields, includingdecision_keyanddecided_at. - If this decision replaces an existing one, first call
recall_decisionsto find the old uuid, then usesupersede_decisioninstead — never record a contradicting decision alongside the one it replaces.
Review mode
If $ARGUMENTS starts with review, switch to reviewing the candidate queue —
decisions extracted automatically from earlier sessions, none of which are in the
graph yet.
-
List pending candidates:
uv run --project "${HIVE_MIND_HOME:?Set HIVE_MIND_HOME to the Hive Mind checkout}" python -c " from hive_mind.candidates import pending for c in pending(): print(f'{c.id} [{c.scope}/{c.repo or c.client}] ' f'key={c.decision_key} decided={c.decided_at or \"unknown\"} ' f'{c.statement}') " -
Walk them ONE AT A TIME. For each, show decision_key, statement, rationale, alternatives_rejected, and decided_at, then ask: accept / edit / discard. If a legacy candidate has no decided_at, inspect its source session timestamp or ask the user; never use extracted_at as a silent substitute.
- accept — call
record_decisionwith the candidate's fields, then mark it resolved (below). Passsource='harvested', the candidate'ssession_id,decision_key, anddecided_at. Never callrecord_decisionbefore the user has said accept. - edit — apply their changes, confirm the revision, then accept.
- discard — mark it discarded. Say nothing further; a rejected candidate is a normal outcome, not a failure.
- accept — call
-
Mark resolved after each:
uv run --project "${HIVE_MIND_HOME:?Set HIVE_MIND_HOME to the Hive Mind checkout}" python -c " from hive_mind.candidates import resolve resolve('<candidate-id>', '<accepted|discarded>') " -
Stop after five in one sitting and offer to continue. A long review turns into rubber-stamping, which defeats the confirmation gate.
Extraction is deliberately noisy and the queue is where that noise is absorbed. If
most candidates are task summaries rather than decisions, say so — the fix is the
extraction prompt in hive_mind/extract.py, not more careful reviewing.
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.
- 4d ago First seen · 77 lines · 10 tokens per session scan A 65513402700e
decide is a command published in the GitHub repository toml0006/hive-mind (1 stars, last pushed 16d ago), licensed Apache-2.0. It adds 10 tokens to every session and 853 once invoked, about $0.0001 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.
Other commands, from other repositories
artel-remember
Write a fact, decision, or gotcha to Artel shared memory.
artel-tasks
Show open Artel tasks, or claim and start the next one.
artel-handoff
Save an Artel session handoff so the next session or agent can continue.
artel-recall
Search Artel shared memory for what the fleet already knows.
triage
Triage Linear issue with comprehensive analysis.
tokenless-stats
Show Tokenless compression statistics.