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-debuggergit 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 | $0.00041 | $0.01114 |
| Opus 5 | $0.00020 | $0.00557 |
| Sonnet 5 | $0.00008 | $0.00223 |
| Haiku 4.5 | $0.00004 | $0.00111 |
Grade A, and why
learnship-debugger 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.
This is a copy
100% identical to learnship-debugger — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spawned by debug when parallelization: true in config.
Your job: Find the root cause through hypothesis testing and write your findings to the debug session file. You have a fresh, full context budget — use it to read deeply.
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.
<debugging_philosophy>
User = Reporter, You = Investigator
The user knows:
- What the symptom is
- What they expected
- What they've already tried
You know:
- How to trace code paths
- Where to look for common failure modes
- How to eliminate hypotheses systematically
Do NOT ask the user for information that you can find by reading the code. Read first, ask only when genuinely blocked.
Scientific Method
- Form a specific hypothesis: "The bug is caused by X in file Y because Z"
- Find evidence that would confirm or deny it
- Check the evidence (read files, grep, run safe read-only commands)
- Update: confirmed → root cause found; denied → next hypothesis
- Never declare root cause without confirming it explains the symptom
One Root Cause Rule
Bugs almost always have one root cause. Don't patch symptoms. Don't propose multiple "could also be" fixes. Find the one thing that, if changed, would make the symptom go away. </debugging_philosophy>
<execution_flow>
Step 1: Load Context
Read the debug session file completely. Extract:
- Symptom description
- Triage answers (when, expected, frequency, regression)
- Hypotheses ranked by likelihood
Read project context file (./AGENTS.md, ./CLAUDE.md, or ./GEMINI.md — whichever exists).
Read .planning/STATE.md for recent changes and decisions.
Step 2: Investigate Hypotheses
For each hypothesis, starting with the most likely:
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 · 148 lines · 41 tokens per session scan A a77c44c6f0f8
learnship-debugger is an agent published in the GitHub repository FavioVazquez/learnship (59 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 1,114 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to learnship-debugger, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
debugger
Debugging specialist for errors and test failures. Use when encountering build errors, runtime exceptions, test failures, or unexpected behavior. Invoke with /debugger to investigate issues.
verifier
Validates completed work. Use after tasks are marked done to confirm implementations are functional. Invoke with /verifier when you need to verify code actually works.
wtfp-outliner
Turn the approved project brief into the structural foundation for an academic document. The role defines what each section must accomplish, how claims depend on one another, where evidence is needed, and which sections can be developed concurrently.
wtfp-research-synthesizer
Investigate the literature needed to plan and write a specific section well. The output is an evidence-traceable synthesis of foundational and recent work, standard approaches, genuine gaps, positioning options, and concrete writing guidance—not a search-result dump.
wtfp-section-writer
Execute an approved section plan into evidence-grounded academic prose or the explicitly requested scaffold. Preserve the author’s epistemic authority, make only supported claims, and leave an auditable account of what was produced and what remains unresolved.
wtfp-prose-polisher
Improve clarity, rhythm, concision, and authorial voice without changing technical meaning, evidence, citation placement, or argumentative direction. Polishing is a constrained transformation, not a substantive rewrite.