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debug

Systematic debugging with root cause investigation. Four phases: investigate, analyze, hypothesize, implement. Iron Law: no fixes without root cause.

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含まれるファイル(2)

  • SKILL.md13.4 KB
  • SKILL.md.tmpl5.6 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

<!-- AUTO-GENERATED from SKILL.md.tmpl — do not edit directly --> <!-- Regenerate: bun run gen:skill-docs -->

Preamble (run first)

_UPD=$(~/.claude/skills/gstack/bin/gstack-update-check 2>/dev/null || .claude/skills/gstack/bin/gstack-update-check 2>/dev/null || true)
[ -n "$_UPD" ] && echo "$_UPD" || true
mkdir -p ~/.gstack/sessions
touch ~/.gstack/sessions/"$PPID"
_SESSIONS=$(find ~/.gstack/sessions -mmin -120 -type f 2>/dev/null | wc -l | tr -d ' ')
find ~/.gstack/sessions -mmin +120 -type f -delete 2>/dev/null || true
_CONTRIB=$(~/.claude/skills/gstack/bin/gstack-config get gstack_contributor 2>/dev/null || true)
_BRANCH=$(git branch --show-current 2>/dev/null || echo "unknown")
echo "BRANCH: $_BRANCH"
_LAKE_SEEN=$([ -f ~/.gstack/.completeness-intro-seen ] && echo "yes" || echo "no")
echo "LAKE_INTRO: $_LAKE_SEEN"

If output shows UPGRADE_AVAILABLE <old> <new>: read ~/.claude/skills/gstack/gstack-upgrade/SKILL.md and follow the "Inline upgrade flow" (auto-upgrade if configured, otherwise AskUserQuestion with 4 options, write snooze state if declined). If JUST_UPGRADED <from> <to>: tell user "Running gstack v{to} (just updated!)" and continue.

If LAKE_INTRO is no: Before continuing, introduce the Completeness Principle. Tell the user: "gstack follows the Boil the Lake principle — always do the complete thing when AI makes the marginal cost near-zero. Read more: https://garryslist.org/posts/boil-the-ocean" Then offer to open the essay in their default browser:

open https://garryslist.org/posts/boil-the-ocean
touch ~/.gstack/.completeness-intro-seen

Only run open if the user says yes. Always run touch to mark as seen. This only happens once.

AskUserQuestion Format

ALWAYS follow this structure for every AskUserQuestion call:

  1. Re-ground: State the project, the current branch (use the _BRANCH value printed by the preamble — NOT any branch from conversation history or gitStatus), and the current plan/task. (1-2 sentences)
  2. Simplify: Explain the problem in plain English a smart 16-year-old could follow. No raw function names, no internal jargon, no implementation details. Use concrete examples and analogies. Say what it DOES, not what it's called.
  3. Recommend: RECOMMENDATION: Choose [X] because [one-line reason] — always prefer the complete option over shortcuts (see Completeness Principle). Include Completeness: X/10 for each option. Calibration: 10 = complete implementation (all edge cases, full coverage), 7 = covers happy path but skips some edges, 3 = shortcut that defers significant work. If both options are 8+, pick the higher; if one is ≤5, flag it.
  4. Options: Lettered options: A) ... B) ... C) ... — when an option involves effort, show both scales: (human: ~X / CC: ~Y)

Assume the user hasn't looked at this window in 20 minutes and doesn't have the code open. If you'd need to read the source to understand your own explanation, it's too complex.

Per-skill instructions may add additional formatting rules on top of this baseline.

Completeness Principle — Boil the Lake

AI-assisted coding makes the marginal cost of completeness near-zero. When you present options:

  • If Option A is the complete implementation (full parity, all edge cases, 100% coverage) and Option B is a shortcut that saves modest effort — always recommend A. The delta between 80 lines and 150 lines is meaningless with CC+gstack. "Good enough" is the wrong instinct when "complete" costs minutes more.
  • Lake vs. ocean: A "lake" is boilable — 100% test coverage for a module, full feature implementation, handling all edge cases, complete error paths. An "ocean" is not — rewriting an entire system from scratch, adding features to dependencies you don't control, multi-quarter platform migrations. Recommend boiling lakes. Flag oceans as out of scope.
  • When estimating effort, always show both scales: human team time and CC+gstack time. The compression ratio varies by task type — use this reference:
Task typeHuman teamCC+gstackCompression
Boilerplate / scaffolding2 days15 min~100x
Test writing1 day15 min~50x
Feature implementation1 week30 min~30x
Bug fix + regression test4 hours15 min~20x
Architecture / design2 days4 hours~5x
Research / exploration1 day3 hours~3x
  • This principle applies to test coverage, error handling, documentation, edge cases, and feature completeness. Don't skip the last 10% to "save time" — with AI, that 10% costs seconds.

Anti-patterns — DON'T do this:

  • BAD: "Choose B — it covers 90% of the value with less code." (If A is only 70 lines more, choose A.)
  • BAD: "We can skip edge case handling to save time." (Edge case handling costs minutes with CC.)
  • BAD: "Let's defer test coverage to a follow-up PR." (Tests are the cheapest lake to boil.)
  • BAD: Quoting only human-team effort: "This would take 2 weeks." (Say: "2 weeks human / ~1 hour CC.")

Contributor Mode

If _CONTRIB is true: you are in contributor mode. You're a gstack user who also helps make it better.

At the end of each major workflow step (not after every single command), reflect on the gstack tooling you used. Rate your experience 0 to 10. If it wasn't a 10, think about why. If there is an obvious, actionable bug OR an insightful, interesting thing that could have been done better by gstack code or skill markdown — file a field report. Maybe our contributor will help make us better!

Calibration — this is the bar: For example, $B js "await fetch(...)" used to fail with SyntaxError: await is only valid in async functions because gstack didn't wrap expressions in async context. Small, but the input was reasonable and gstack should have handled it — that's the kind of thing worth filing. Things less consequential than this, ignore.

NOT worth filing: user's app bugs, network errors to user's URL, auth failures on user's site, user's own JS logic bugs.

To file: write ~/.gstack/contributor-logs/{slug}.md with all sections below (do not truncate — include every section through the Date/Version footer):

# {Title}

Hey gstack team — ran into this while using /{skill-name}:

**What I was trying to do:** {what the user/agent was attempting}
**What happened instead:** {what actually happened}
**My rating:** {0-10} — {one sentence on why it wasn't a 10}

## Steps to reproduce
1. {step}

## Raw output

{paste the actual error or unexpected output here}


## What would make this a 10
{one sentence: what gstack should have done differently}

**Date:** {YYYY-MM-DD} | **Version:** {gstack version} | **Skill:** /{skill}

Slug: lowercase, hyphens, max 60 chars (e.g. browse-js-no-await). Skip if file already exists. Max 3 reports per session. File inline and continue — don't stop the workflow. Tell user: "Filed gstack field report: {title}"

Completion Status Protocol

When completing a skill workflow, report status using one of:

  • DONE — All steps completed successfully. Evidence provided for each claim.
  • DONE_WITH_CONCERNS — Completed, but with issues the user should know about. List each concern.
  • BLOCKED — Cannot proceed. State what is blocking and what was tried.
  • NEEDS_CONTEXT — Missing information required to continue. State exactly what you need.

Escalation

It is always OK to stop and say "this is too hard for me" or "I'm not confident in this result."

Bad work is worse than no work. You will not be penalized for escalating.

  • If you have attempted a task 3 times without success, STOP and escalate.
  • If you are uncertain about a security-sensitive change, STOP and escalate.
  • If the scope of work exceeds what you can verify, STOP and escalate.

Escalation format:

STATUS: BLOCKED | NEEDS_CONTEXT
REASON: [1-2 sentences]
ATTEMPTED: [what you tried]
RECOMMENDATION: [what the user should do next]

Systematic Debugging

Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST.

Fixing symptoms creates whack-a-mole debugging. Every fix that doesn't address root cause makes the next bug harder to find. Find the root cause, then fix it.


Phase 1: Root Cause Investigation

Gather context before forming any hypothesis.

  1. Collect symptoms: Read the error messages, stack traces, and reproduction steps. If the user hasn't provided enough context, ask ONE question at a time via AskUserQuestion.

  2. Read the code: Trace the code path from the symptom back to potential causes. Use Grep to find all references, Read to understand the logic.

  3. Check recent changes:

    git log --oneline -20 -- <affected-files>
    

    Was this working before? What changed? A regression means the root cause is in the diff.

  4. Reproduce: Can you trigger the bug deterministically? If not, gather more evidence before proceeding.

Output: "Root cause hypothesis: ..." — a specific, testable claim about what is wrong and why.


Phase 2: Pattern Analysis

Check if this bug matches a known pattern:

PatternSignatureWhere to look
Race conditionIntermittent, timing-dependentConcurrent access to shared state
Nil/null propagationNoMethodError, TypeErrorMissing guards on optional values
State corruptionInconsistent data, partial updatesTransactions, callbacks, hooks
Integration failureTimeout, unexpected responseExternal API calls, service boundaries
Configuration driftWorks locally, fails in staging/prodEnv vars, feature flags, DB state
Stale cacheShows old data, fixes on cache clearRedis, CDN, browser cache, Turbo

Also check:

  • TODOS.md for related known issues
  • git log for prior fixes in the same area — recurring bugs in the same files are an architectural smell, not a coincidence

Phase 3: Hypothesis Testing

Before writing ANY fix, verify your hypothesis.

  1. Confirm the hypothesis: Add a temporary log statement, assertion, or debug output at the suspected root cause. Run the reproduction. Does the evidence match?

  2. If the hypothesis is wrong: Return to Phase 1. Gather more evidence. Do not guess.

  3. 3-strike rule: If 3 hypotheses fail, STOP. Use AskUserQuestion:

    3 hypotheses tested, none match. This may be an architectural issue
    rather than a simple bug.
    
    A) Continue investigating — I have a new hypothesis: [describe]
    B) Escalate for human review — this needs someone who knows the system
    C) Add logging and wait — instrument the area and catch it next time
    

Red flags — if you see any of these, slow down:

  • "Quick fix for now" — there is no "for now." Fix it right or escalate.
  • Proposing a fix before tracing data flow — you're guessing.
  • Each fix reveals a new problem elsewhere — wrong layer, not wrong code.

Phase 4: Implementation

Once root cause is confirmed:

  1. Fix the root cause, not the symptom. The smallest change that eliminates the actual problem.

  2. Minimal diff: Fewest files touched, fewest lines changed. Resist the urge to refactor adjacent code.

  3. Write a regression test that:

    • Fails without the fix (proves the test is meaningful)
    • Passes with the fix (proves the fix works)
  4. Run the full test suite. Paste the output. No regressions allowed.

  5. If the fix touches >5 files: Use AskUserQuestion to flag the blast radius:

    This fix touches N files. That's a large blast radius for a bug fix.
    A) Proceed — the root cause genuinely spans these files
    B) Split — fix the critical path now, defer the rest
    C) Rethink — maybe there's a more targeted approach
    

Phase 5: Verification & Report

Fresh verification: Reproduce the original bug scenario and confirm it's fixed. This is not optional.

Run the test suite and paste the output.

Output a structured debug report:

DEBUG REPORT
════════════════════════════════════════
Symptom:         [what the user observed]
Root cause:      [what was actually wrong]
Fix:             [what was changed, with file:line references]
Evidence:        [test output, reproduction attempt showing fix works]
Regression test: [file:line of the new test]
Related:         [TODOS.md items, prior bugs in same area, architectural notes]
Status:          DONE | DONE_WITH_CONCERNS | BLOCKED
════════════════════════════════════════

Important Rules

  • 3+ failed fix attempts → STOP and question the architecture. Wrong architecture, not failed hypothesis.
  • Never apply a fix you cannot verify. If you can't reproduce and confirm, don't ship it.
  • Never say "this should fix it." Verify and prove it. Run the tests.
  • If fix touches >5 files → AskUserQuestion about blast radius before proceeding.
  • Completion status:
    • DONE — root cause found, fix applied, regression test written, all tests pass
    • DONE_WITH_CONCERNS — fixed but cannot fully verify (e.g., intermittent bug, requires staging)
    • BLOCKED — root cause unclear after investigation, escalated

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Turn any installed skill (gstack, custom, third-party) into an ATO agent the user can summon into war-rooms. Reads a SKILL.md, extracts the persona, strips runtime boilerplate, and writes an agent file at `.claude/agents/<slug>.md` (project-scoped) or `~/.claude/agents/<slug>.md` (global). Prompts for a model roster (primary + 1-2 alts) so cross-family dispatch in war-rooms produces real disagreement. Companion to `ato-warroom` — that skill summons agents this skill creates. Use when asked "turn this skill into an agent", "register X as a war-room agent", or when scoping a new persona before a war-room runs.

日本語の概要は準備中です。原文の説明を表示しています。

WillNigri/Agentic-Tool-Optimization342026年9月8日 更新

Before any multi-step work with a stated goal — a feature, a bugfix spanning multiple files, a QA sweep, a doc draft + iterations, a multi-day investigation — create an ATO Mission instead of doing it via bare `ato dispatch` calls. A Mission persists the goal + the verifiable success criteria, lets the coordinator tick drive the work across days, captures every event in a structured audit trail (SQLite + markdown narrative), and integrates parallel agents' work via merge strategies. Complement to `ato-warroom` (the cross-family decision before you start) and `ato-review` (the post-code-diff review). Missions is where multi-step work LIVES; war-rooms are where decisions ABOUT it get made; reviews are where the resulting commits get vetted. Fires when: the work has more than one decision point, a verifiable end state, or runs across more than one session. Use it for any ATO development that doesn't fit in a single dispatch.

日本語の概要は準備中です。原文の説明を表示しています。

WillNigri/Agentic-Tool-Optimization342026年9月8日 更新

Before committing any non-trivial change, dispatch the diff to a reviewer runtime via ATO (`ato dispatch <reviewer> --session <id>`), parse the numbered/severity-tagged findings, apply or defer each one with a recorded justification, then commit. Fights the "build passes therefore ship it" failure mode — what Garry Tan calls the AI agent complexity ratchet. Place in the v2.16 stack: this skill is the LAST gate. `ato-warroom` decides the design; `ato-mission` runs the multi-step work and produces the diff; `ato-review` checks the diff before commit. When the review is part of a Mission, dispatch the review with `--require-tools read_file,grep,git_diff,git_log` so the reviewer can walk the source itself instead of reasoning from a paraphrase (PR-1.5 tool surface). Receipts land in `execution_logs` and the Mission narrative. Fires automatically before commits touching public surface (CLI subcommands, Tauri commands, MCP tools, schema migrations, security boundaries) or whenever a diff exceeds ~50 LOC of behavior change.

日本語の概要は準備中です。原文の説明を表示しています。

WillNigri/Agentic-Tool-Optimization342026年9月8日 更新

Before any material decision — code chunk, plan, strategy, design, scope cut, push to GitHub — convene a war-room. The session driver takes the CEO seat: frame the tradeoff, summon specialist seats from whatever agent roster the user has built, dispatch a cross-family voice via `ato dispatch` so priors actually disagree, decide. A failure-mode filter (wrong assumptions / overcomplexity / orthogonal edits / imperative-over-declarative — Karpathy's four are one good default, swap in your own) runs on every dispatch. Place in the v2.16 stack: war-rooms DECIDE before code starts; `ato-mission` EXECUTES the work between decisions (multi-step, goal-driven, persisted across days); `ato-review` VERIFIES the resulting commits. Use a war-room for the design verdict, hand the verdict to a Mission, review the merged result. Fires before: sending a code draft to the user as final, opening a PR, pushing to a remote-tracking branch, committing >50 LOC of behavior change, or delivering a plan or strategic recommendation as the final answer.

日本語の概要は準備中です。原文の説明を表示しています。

WillNigri/Agentic-Tool-Optimization342026年9月8日 更新

browse

無料

Fast headless browser for QA testing and site dogfooding. Navigate any URL, interact with elements, verify page state, diff before/after actions, take annotated screenshots, check responsive layouts, test forms and uploads, handle dialogs, and assert element states. ~100ms per command. Use when you need to test a feature, verify a deployment, dogfood a user flow, or file a bug with evidence. Use when asked to "open in browser", "test the site", "take a screenshot", or "dogfood this".

日本語の概要は準備中です。原文の説明を表示しています。

WillNigri/Agentic-Tool-Optimization342026年9月8日 更新

Design consultation: understands your product, researches the landscape, proposes a complete design system (aesthetic, typography, color, layout, spacing, motion), and generates font+color preview pages. Creates DESIGN.md as your project's design source of truth. For existing sites, use /plan-design-review to infer the system instead. Use when asked to "design system", "brand guidelines", or "create DESIGN.md".

日本語の概要は準備中です。原文の説明を表示しています。

WillNigri/Agentic-Tool-Optimization342026年9月8日 更新

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