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 netgrade-digital/shared-agents --skill capture-learninggit clone --depth 1 https://github.com/netgrade-digital/shared-agentsWrote 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/netgrade-digital/shared-agents/capture-learning)<a href="https://agentmods.dev/skills/netgrade-digital/shared-agents/capture-learning"><img src="https://agentmods.dev/badge/skills/netgrade-digital/shared-agents/capture-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/netgrade-digital/shared-agents/capture-learning"><img src="https://agentmods.dev/badge/skills/netgrade-digital/shared-agents/capture-learning.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 | $0.00050 | $0.01161 |
| Opus 5.5 | $0.00020 | $0.00464 |
| Sonnet 5.5 | $0.00010 | $0.00232 |
| Haiku 4.5 | $0.00005 | $0.00116 |
Grade A, and why
capture-learning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Capture Learning
Write team learnings to pending/ — drafts awaiting human review.
Ask first (required)
After non-trivial tasks, the agent must ask:
„Soll ich ein Team-Learning in shared-agents anlegen?"
Only write if the user says yes. Never silently write to pending.
When content is worth capturing
Propose when all apply:
- Insight is reusable beyond this session
- Would help a teammate or future agent
- No secrets or customer-specific data
Skip when trivial, already documented, or user said no.
Where to write (pending only)
Canonical path only — see docs/canonical-paths.md.
# Resolve before write (never use workspace-relative paths):
sa pending path YYYY-MM-DD-short-slug
# Or: "$SHARED_AGENTS_HOME/scripts/learning-path.sh" YYYY-MM-DD-short-slug
Mandatory
- Write only under the path from
sa pending path(typically$SHARED_AGENTS_HOME/team/learnings/pending/). - Use the absolute path in the Write/edit tool — not paths from the open project or Core dev checkout.
- Do not commit learnings into the public Core-Repo.
Forbidden
Development/Work/shared-agents/learnings/(Core has no learnings)- Customer project repos (
.cursor/, projectdocs/, etc.) approved/(human/PR only)
Never write to approved/ — that is human/PR territory.
File format
---
id: project-YYYY-MM-short-slug
project: project-name
domain: [tag1, tag2]
tags: [keyword1, keyword2]
versions: [shopware:6.6.10, php:8.3.14]
confidence: high
source: task
created: YYYY-MM-DD
author: github-or-name
---
## Kontext
What problem or situation triggered this.
## Erkenntnis
The reusable insight in 1–3 sentences.
## Anwendung
Concrete steps or rule of thumb for next time.
## Links
- path/to/file or PR URL (optional)
Versions (required when a stack applies)
Always set versions: for framework/runtime-specific learnings. Use [] only when no stack version is relevant (e.g. pure process/infra docs).
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 · 133 lines · 50 tokens per session scan A 4fc75af09379
capture-learning is a skill published in the GitHub repository netgrade-digital/shared-agents (5 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 1,161 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 2026-08-31.
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