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 zimalabs/code-decisions --skill decisiongit clone --depth 1 https://github.com/zimalabs/code-decisionsWrote 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/zimalabs/code-decisions/decision)<a href="https://agentmods.dev/skills/zimalabs/code-decisions/decision"><img src="https://agentmods.dev/badge/skills/zimalabs/code-decisions/decision/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/zimalabs/code-decisions/decision"><img src="https://agentmods.dev/badge/skills/zimalabs/code-decisions/decision.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 | $0.00023 | $0.00904 |
| Opus 5.5 | $0.00009 | $0.00362 |
| Sonnet 5.5 | $0.00005 | $0.00181 |
| Haiku 4.5 | $0.00002 | $0.00090 |
Grade A, and why
decision 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/decision
Parse $ARGUMENTS to determine intent and route to the appropriate action.
CLI Usage
The SessionStart hook injects a Decision CLI: ... line into conversation context with the correct PYTHONPATH prefix. Always use that exact prefix when running CLI commands. For brevity, this document writes python3 -m decision <command> — prepend the injected PYTHONPATH="..." when executing.
Intent Detection
Explicit subcommands take priority — /decision search <query> and /decision capture <statement> bypass intent detection entirely.
Search (default): questions, keywords, lookups, --tags, --stats, --coverage, or anything ambiguous.
Capture: declarative statement with both a choice and a reason ("chose X because Y", "going with X instead of Y"). Without a reason, prefer search — the user may be looking up an existing decision.
Manage: admin verbs — edit, undo, dismiss, debug, review, enrich, tree, tour, history, help.
No arguments: show brief status with python3 -m decision stats --json and suggest quick actions.
Search
- Check pre-seeded results first — a hook runs FTS5 search automatically when
/decision <keywords>is invoked. If results exist in conversation context, present them directly. - If pre-seeded results are empty or insufficient: use
Grepto search.claude/decisions/files for keywords, thenReadmatching files to present results. This avoids permission prompts. Only fall back topython3 -m decision search <keywords>via CLI if native tools fail. - Flags:
--tags→python3 -m decision tags,--stats→python3 -m decision stats,--coverage→python3 -m decision coverage
Capture
- Search existing decisions first — if one covers the same topic, edit it in place instead of creating a new file.
- Write directly to
.claude/decisions/{slug}.mdfollowing the decision template (injected at session start). The content-validation hook will reject and guide you if anything is wrong. - Confirm briefly:
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 · 70 lines · 23 tokens per session scan A 68855066eb21
decision is a skill published in the GitHub repository zimalabs/code-decisions (5 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 904 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 2026-08-31.
Other skills, from other repositories
captain-recall
Use when the user asks WHY or WHAT-HAPPENED questions about this system — historical decisions, past incidents, why code/config is shaped a certain way, where something is deployed, who decided what, or the state of work in flight. Queries the tsubasa knowledge graph for cited answers.
captain-inject
Use when the user STATES A FACT about the system or tells the captain to remember/learn something — "update your knowledge", "remember this", "we dropped X for Y", "the outage was caused by Z", environment URLs, team contacts, deployment flows, tribal knowledge, corrections to what the Captain believes. Validates…
handoff
Write a short handoff note for the next chat or a teammate, with what is done, what is open, what comes next and what was rejected and why. Saves it to project memory so the next chat sees it at the start, and prints it so it can be pasted into a message or a pull request. Use when the user ends a piece of work…
recall
Must be used at the start of any non-trivial task involving code changes, debugging, repo exploration, file inspection, or environment/tooling investigation to surface stored guidance before analysis or tool use.
alive:session-history
Revive sessions (quick or heavy), browse, and search — 'what happened recently?', 'find the session where we discussed X', 'revive yesterday's session'. For single-session recall and multi-session browsing. If the human needs to merge multiple sessions into one working context or detect conflicts between parallel…
knowledge-priming-refiner
Facilitate a structured conversation to create a project-specific knowledge base document. Produces a knowledge-base.md that primes AI with the project's tech stack, architecture, trusted sources, and project structure. Use when the user says 'set up knowledge base', 'prime the project', 'onboard AI', 'create…