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 tranhieutt/software_development_department --skill diagnosegit clone --depth 1 https://github.com/tranhieutt/software_development_departmentWrote 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/tranhieutt/software_development_department/diagnose)<a href="https://agentmods.dev/skills/tranhieutt/software_development_department/diagnose"><img src="https://agentmods.dev/badge/skills/tranhieutt/software_development_department/diagnose.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 5.1 | $0.00058 | $0.03560 |
| Opus 5 | $0.00029 | $0.01780 |
| Sonnet 5 | $0.00012 | $0.00712 |
| Haiku 4.5 | $0.00006 | $0.00356 |
Grade A, and why
diagnose 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 8d 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 — 329 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: /diagnose — Complex Bug Diagnostic Pipeline
When to invoke (and when NOT to)
Use /diagnose when:
- Bug reproduces but root cause is unclear after one read-pass of the failing code
- Previous fix attempts have been reverted ≥ 2 times (symptoms return)
- Failure is intermittent (flaky test, race condition, timing-dependent)
- Failure occurs in unfamiliar code (agent has no prior context)
- User has explicitly requested
/diagnoseor "deep investigation" - Circuit Breaker (Rule 14) tripped on the specialist agent that normally handles this domain
Do NOT use /diagnose when:
- Cause is obvious (null ref, typo, missing import, incorrect import path)
- Fix is < 10 LOC and has a clear success check
- Bug is in code you just wrote this session (read-pass + local reasoning is faster)
- User wants a quick patch and has accepted the tradeoff
Pipeline overview
Feedback Loop -> Investigation -> Verification -> Solution -> Lead Programmer
(signal) (hypothesis) (devil's adv.) (tradeoffs) (assign + exec)
repro/check command investigation.json verification.json solution.json implementation
(fast deterministic (root_cause, (status: confirmed | (3 options: (delegates to
pass/fail signal) evidence[], refuted | inconclusive, Quick/Strategic/ backend-developer,
confidence) reproduction_steps) Future-Proof) qa-engineer, etc.)
Each stage produces a required artifact saved to .investigations/<task_id>/ and a handoff contract (per Rule 16) to the next agent.
Stage 0 — Feedback Loop
Goal: Build the fastest reliable pass/fail signal for the exact symptom before explaining the cause.
The feedback loop is the highest-leverage part of diagnosis. Do not proceed to root-cause analysis until there is a loop that can reproduce the user's symptom or a documented reason why no loop is possible.
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.
- 8d ago First seen · 329 lines · 58 tokens per session scan A 84d980cbe76d
diagnose is a skill published in the GitHub repository tranhieutt/software_development_department (72 stars, last pushed 3mo ago), licensed MIT. It adds 58 tokens to every session and 3,560 once invoked, about $0.0003 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.
Other skills, from other repositories
serena
Serena code intelligence — LSP-powered symbol navigation, diagnostics, and targeted code surgery. Activate before complex refactors, cross-file analysis, or when graph tools need symbol-level depth.
diagnose
Structured 6-phase debugging. Build feedback loop first, reproduce deterministically, hypothesize with ranked falsifiable theories, instrument one variable at a time, fix with regression test, cleanup. Use when a bug exists, tests fail unexpectedly, or behavior is wrong and cause is unknown.
fix
Plan-aware auto-fix loop after coding. Runs tests, lint, format, and graph checks. Updates plan task status. Use after execute/tdd.
zoom-out
One-shot module map — go up a layer of abstraction and get a domain-vocabulary module map of the codebase. Use when lost in unfamiliar code, after a long deep-dive session, or when you need to re-orient before planning.
debug
Use this prompt when you are facing a bug, error trace, or unexpected behavior. This skill forces the AI to act as a troubleshooter, analyzing root causes systematically rather than just guessing solutions.
verify-simplify
A final code-cleanup workflow that runs at the end of verification. It removes repeated or unnecessary logic after the other verification steps are complete.