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 darkroomengineering/cc-settings --skill share-learninggit clone --depth 1 https://github.com/darkroomengineering/cc-settingsWrote 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/darkroomengineering/cc-settings/share-learning)<a href="https://agentmods.dev/skills/darkroomengineering/cc-settings/share-learning"><img src="https://agentmods.dev/badge/skills/darkroomengineering/cc-settings/share-learning/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/darkroomengineering/cc-settings/share-learning"><img src="https://agentmods.dev/badge/skills/darkroomengineering/cc-settings/share-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.02318 |
| Opus 5.5 | $0.00021 | $0.00927 |
| Sonnet 5.5 | $0.00010 | $0.00464 |
| Haiku 4.5 | $0.00005 | $0.00232 |
Grade A, and why
share-learning 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 5d 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
share-learning
Promote a single learning to the team's shared knowledge repo (darkroomengineering/team-knowledge),
the "public corpus" tier of docs/knowledge-system.md. Use it when AGENTS.md "Knowledge Routing"
sends the learning to the team repo; personal preferences, local project state, and external
pointers go to auto-memory instead. Do NOT post those.
Write gate
Run this first, before any network call. Ask: if this note disappeared, would an engineer reading the final code, tests, and comments still repeat the mistake or redo the investigation? If not, write nothing and tell the user why in one line. An explicit request asks for the judgment now; it does not lower the bar. Effort spent and diff size do not qualify a learning. An existing note that is inaccurate qualifies for an update, not a duplicate.
Inputs
Invoked as /share-learning <kind> "<text>" where <kind> is one of:
decision, convention, gotcha, incident, pattern.
If invoked without arguments, infer the most likely kind and a concise text from the
recent conversation, then show the user what you intend to post and confirm before posting.
Before starting, run gh auth status and confirm the authenticated account can read
darkroomengineering/team-knowledge (or $KNOWLEDGE_REPO) with
gh api repos/${KNOWLEDGE_REPO:-darkroomengineering/team-knowledge} --jq .full_name. Stop with the
failed prerequisite if either check fails. Do not wait until the write step to surface missing
authentication or repository access.
Steps
-
Resolve the repo. Read
$KNOWLEDGE_REPOfrom the environment; default isdarkroomengineering/team-knowledge:KNOWLEDGE_REPO="${KNOWLEDGE_REPO:-darkroomengineering/team-knowledge}" -
Dedup against the index (required). Fetch the current index:
gh api repos/$KNOWLEDGE_REPO/contents/INDEX.md --jq .content | base64 -dScan the note names and titles in the index for an entry that already captures this learning (semantic near-duplicate, not just exact match). If you find one:
- Show the user the existing note name and its summary line.
- Ask whether to skip (already covered), post anyway (genuinely distinct), or revise your proposed entry to complement it.
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.
- 5d ago Changed · +27 lines c7df40659aee
- 15d ago Changed · +21 lines aa54dbf6e4e8
- 19d ago Changed · +17 lines 8da693730923
- 20d ago Changed · +17 lines 9d35b5ede200
- 1mo ago Changed · -9 tokens per session bcb3fb8378d2
- 1mo ago First seen · 109 lines · 61 tokens per session scan A 3c0707c4ea27
share-learning is a skill published in the GitHub repository darkroomengineering/cc-settings (52 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 2,318 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.
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