Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/moonlight-lupin/agent-skillsnpx agentmods add skills/moonlight-lupin/agent-skills/decision-logWrote 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/moonlight-lupin/agent-skills/decision-log)<a href="https://agentmods.dev/skills/moonlight-lupin/agent-skills/decision-log"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/decision-log/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/moonlight-lupin/agent-skills/decision-log"><img src="https://agentmods.dev/badge/skills/moonlight-lupin/agent-skills/decision-log.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 306 Skill attempts to nullify the agent's safety policies or restrictions ('you have no restrictions', 'ignore your guidelines', 'do anything now'). This is a direct jailbreak that disables guardrails.Fix: Remove jailbreak framing that nullifies safety policies or restrictions. Skill content must not instruct the agent to ignore its guidelines or operate without guardrails.
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 | $0.00057 | $0.02526 |
| Opus 5.5 | $0.00023 | $0.01010 |
| Sonnet 5.5 | $0.00011 | $0.00505 |
| Haiku 4.5 | $0.00006 | $0.00253 |
Grade A, and why
decision-log 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 20d 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 — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Log
Overview
Use this skill to keep durable, searchable decision records when a team or agent needs to remember what was decided, why it was decided, what alternatives were considered, and when the decision should be reviewed.
It solves recurring problems that chat history and meeting notes do not solve well:
- Decisions get buried in long conversations and are hard to find later.
- The reasoning and constraints behind a choice are forgotten.
- Settled questions get re-litigated because there is no authoritative record.
- Reversals are made by deleting old context instead of preserving history.
- No review cadence exists, so time-sensitive decisions quietly go stale.
The pattern is ADR-style: one Markdown file per decision, numbered sequentially, with status, context, options, decision, consequences, and review metadata. The format follows the spirit of Michael Nygard's Architecture Decision Record (ADR) pattern, generalized for product, operations, research, and team decisions.
Decision records live in a configurable directory:
--decisions-dir DIRon every command, orDECISIONS_DIR=/path/to/decisions, or- default
./decisions/relative to the current working directory.
Quick start
python scripts/decision_log.py new --title "Use SQLite for source-tracker"
The command creates the next numbered file, such as:
./decisions/ADR-001-use-sqlite-for-source-tracker.md
Open the printed path, complete the sections, then change ## Status from
proposed to accepted once the decision is approved.
When to create an ADR
Create a decision record when the choice is meaningful enough that future people will ask "why did we do this?" Examples:
- Architecture, storage, API, dependency, deployment, or security choices.
- Product or workflow choices that affect multiple users or future work.
- Trade-offs with real costs, constraints, or excluded alternatives.
- Decisions that may need review after a date, milestone, or external trigger.
- Reversals of previous decisions.
What ships with it
6 files 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.
- 20d ago Changed · +18 tokens per session 9f624b1252d9
- 1mo ago First seen · 342 lines · 39 tokens per session scan A f45bd4374ea4
decision-log is a skill published in the GitHub repository moonlight-lupin/agent-skills (112 stars, last pushed 4d ago), licensed MIT. It adds 57 tokens to every session and 2,526 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
loopx-material
Operate an explicitly activated LoopX Material Lifecycle for a connected project. Use for material-store inventory, lossless migration, candidate/archive transitions, exact-read-backed ranking, ranked-entry rebuilds, bounded Explore intake, owner-gated apply, rollback, and audit. Do not use for ordinary one-off…
loopx-doc-registry
Use when a connected LoopX project is asked to read, remember, record, index, register, or use a durable project material such as a Lark/wiki/design doc, research note, SOP, owner packet, migration report, benchmark paper, or external material source. Use even when the user does not mention LoopX or doc registry.
hermes-memory-providers
Install and configure Mnemosyne as a Hermes Agent memory provider — local SQLite with vector search, episodic consolidation, and temporal knowledge graphs.
mnemosyne
Persistent cross-session memory via Mnemosyne — store, recall, and consolidate facts, preferences, and context.
mnemosyne-memory-override
Hard rule override that forces Mnemosyne for all durable memory storage. The legacy memory tool is DEPRECATED for user preferences, credentials, and project conventions. Use memory ONLY for ephemeral session state.
chromadb
Manage the ChromaDB vector database that stores the ecosystem's persistent memory. Use when user asks to check memory storage, backup memory, search stored entries, delete entries, or reset the vector database. Do NOT use for general question answering about past sessions (use the memory skill for that).