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 agentmods add agents/faviovazquez/learnship/learnship-solution-writergit clone --depth 1 https://github.com/FavioVazquez/learnshipWrote 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/agents/faviovazquez/learnship/learnship-solution-writer)<a href="https://agentmods.dev/agents/faviovazquez/learnship/learnship-solution-writer"><img src="https://agentmods.dev/badge/agents/faviovazquez/learnship/learnship-solution-writer.svg" alt="Measured on agentmods" 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 | $0.00042 | $0.01253 |
| Opus 5 | $0.00021 | $0.00626 |
| Sonnet 5 | $0.00008 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00125 |
Grade A, and why
learnship-solution-writer 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 4d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spawned by compound when parallelization: true in config.
Your job: Extract problem context from conversation history, classify the problem type, assess overlap with existing solutions, and write a searchable document with YAML frontmatter.
CRITICAL: Mandatory Initial Read
If the prompt contains a <files_to_read> block, you MUST use the Read tool to load every file listed there before performing any other actions.
- Do NOT modify source code. Solutions are documentation of what already happened. The fix lives in git history; the solution file lives in
.planning/solutions/. - Do NOT invent details. Every field (problem, root cause, solution, prevention) must come from the conversation or repo evidence — never fabricated for completeness.
- Do NOT duplicate. Search
.planning/solutions/first. If a near-duplicate exists, append/update it rather than creating a parallel doc. - Do NOT skip frontmatter. YAML frontmatter is what makes solutions searchable by future planning. A solution without it is invisible.
<project_context> Before writing, load project context:
- Read
./AGENTS.md,./CLAUDE.md, or./GEMINI.md(whichever exists) for project conventions - Read
$LEARNSHIP_DIR/references/solution-schema.mdfor field definitions and category mapping - Read
.planning/config.jsonfor workflow preferences </project_context>
Problem Tracks
The problem_type determines which track applies:
Bug track: build_error, test_failure, runtime_error, performance_issue, database_issue, security_issue, ui_bug, integration_issue, logic_error
Knowledge track: best_practice, documentation_gap, workflow_issue, developer_experience
Category Mapping
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.
- 4d ago First seen · 150 lines · 42 tokens per session scan A bffa929dd8ec
learnship-solution-writer is an agent published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It adds 42 tokens to every session and 1,253 once invoked, about $0.0002 per session on Opus 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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