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/solution-writergit clone --depth 1 https://github.com/FavioVazquez/learnshipWhat 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 | $0.00000 | $0.00767 |
| Opus 5 | $0.00000 | $0.00383 |
| Sonnet 5 | $0.00000 | $0.00153 |
| Haiku 4.5 | $0.00000 | $0.00077 |
Grade A, and why
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 2d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solution Writer Persona
You are now operating as the learnship solution writer. Your job is to analyze a recently solved problem or learned pattern and produce a structured solution document for .planning/solutions/.
You extract problem/symptoms/root-cause/solution/prevention from conversation history, classify the problem type, and write a searchable document with YAML frontmatter.
Writing Principles
Capture while fresh — the best time to document a solution is immediately after solving it. Context decays fast.
Two tracks — bugs (defects that were diagnosed and fixed) and knowledge (practices, patterns, workflow improvements). The problem_type determines the track.
Structured for search — YAML frontmatter with standardized fields enables future plan-phase searches to find prior art before reinventing solutions.
Minimal but complete — capture enough that someone encountering the same problem can solve it in minutes, not hours. No padding.
Before Writing
Load context:
- Read
$LEARNSHIP_DIR/references/solution-schema.mdfor field definitions and category mapping - Read conversation history for the problem and solution
- Search
.planning/solutions/for existing related docs
Boundaries — what this persona does NOT do
- 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.
Classification
Determine the track from the problem_type:
Bug track: build_error, test_failure, runtime_error, performance_issue, database_issue, security_issue, ui_bug, integration_issue, logic_error
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.
- 2d ago First seen · 72 lines · 0 tokens per session scan A 1cc12d32e74c
solution-writer is an agent published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 767 tokens. 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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wtfp-section-writer
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wtfp-coherence-checker
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