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 qkitzero/kage-bunshin --skill learninggit clone --depth 1 https://github.com/qkitzero/kage-bunshinWrote 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/qkitzero/kage-bunshin/learning)<a href="https://agentmods.dev/skills/qkitzero/kage-bunshin/learning"><img src="https://agentmods.dev/badge/skills/qkitzero/kage-bunshin/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/qkitzero/kage-bunshin/learning"><img src="https://agentmods.dev/badge/skills/qkitzero/kage-bunshin/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 | $0.00045 | $0.00823 |
| Opus 5.5 | $0.00018 | $0.00329 |
| Sonnet 5.5 | $0.00009 | $0.00165 |
| Haiku 4.5 | $0.00005 | $0.00082 |
Grade A, and why
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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Skill
A workflow that creates an Agent Team of Researcher and Analyst to perform structured retrospectives through evidence-based analysis and discussion.
Workflow
Step 1: Understand the Retrospective Target
Accurately understand the retrospective target presented by the user. Clarify:
- Subject (project, sprint, decision, event, etc.)
- Time period (from when to when)
- Stakeholders & context
- Original goals & expectations
Use open questions to help the user elaborate on details.
Step 2: Retrieve Past Notebook Context
If NOTEBOOK_PATH is set, search for past lessons related to the retrospective target:
- Grep
$NOTEBOOK_PATH/learnings/and$NOTEBOOK_PATH/research/for keywords from the target (searching title, tags, and body) - Read up to 3 matching entries (frontmatter + first 200 characters of body)
- Include the retrieved content as context when creating the team
Skip this step if zero matches or NOTEBOOK_PATH is not set.
Step 3: Create Agent Team
Create a team with the following teammates using their agent definitions from .claude/agents/:
Teammates:
researcher— gathers evidence, data, and context about what happenedanalyst— performs structured retrospective analysis
Team instructions:
Retrospective target: [Subject]
Time period: [Period]
Original goals: [Goals and expectations]
[Past Notebook context if found]
Collaboration protocol:
1. Researcher gathers evidence: project history, outcomes, metrics, related context from Notebook and codebase
2. Analyst performs initial retrospective analysis (what went well, what didn't, patterns, key learnings)
3. Researcher challenges the analysis — verifies claims against evidence, surfaces overlooked factors
4. Analyst refines the analysis based on Researcher's input, deepens root cause analysis
5. Continue iterating until both agree the lessons are well-evidenced and actionable (aim for 2-3 rounds)
Rules:
- Output language: Use the language specified by OUTPUT_LANGUAGE env var. If not set, match the user's language (default: English)
- If NOTEBOOK_PATH is set: Researcher saves evidence summary to $NOTEBOOK_PATH/research/, Analyst saves retrospective to $NOTEBOOK_PATH/learnings/
- Each agent writes its own deliverables with structured frontmatter (Analyst must use type: learning)
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 · 116 lines · 45 tokens per session scan A aafc1f07df5a
learning is a skill published in the GitHub repository qkitzero/kage-bunshin (0 stars, last pushed 6mo ago), licensed MIT. It adds 45 tokens to every session and 823 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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