diagnose

name	diagnose
description	Disciplined diagnosis loop for hard bugs and performance regressions. Six phases — build feedback loop → reproduce → hypothesise → instrument → fix + regression test → cleanup + post-mortem. Phase 1 (the feedback loop) is the entire skill — everything else is mechanical once you have a fast deterministic pass/fail signal. On verified fix, captures **Evidence via MCP-first path** (`mcp__forgeplan__forgeplan_new` + `forgeplan_link`) with CLI fallback when MCP is not connected. Use when a bug is non-trivial, intermittent, performance-related, or has resisted obvious fixes. Triggers (EN/RU) — "diagnose this", "debug this", "find the root cause", "performance regression", "intermittent bug", "продиагностируй", "найди причину бага", "отладь", "/diagnose".
origin	forgeplan

Diagnose

Discipline for hard bugs. Skip phases only when explicitly justified — the phases are not stages of a tutorial, they are guardrails against the failure modes that wreck most debugging sessions.

Adapted from Matt Pocock's diagnose skill. Same six-phase structure; integrates with our project-context system.

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Project context (read first)

@docs/agents/build-config.md @docs/agents/paths.md @CONTEXT.md

build-config.md tells you the project's test/typecheck/lint commands — the foundation of any feedback loop. paths.md tells you where source and tests live. CONTEXT.md gives the domain glossary so hypotheses use the right vocabulary.

If docs/agents/ is missing — auto-detect from package.json / Cargo.toml / go.mod / pyproject.toml / Makefile.

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When to use

• The bug is non-trivial, intermittent, or has resisted an obvious fix.
• Performance regression — something got slower, no one knows why.
• The user said: "diagnose this", "debug this", "find the root cause", "продиагностируй", "найди причину бага".
• Before audit when reviewing a fix — audit checks whether the fix is sound; diagnose finds what to fix in the first place.

When NOT to use

• The bug is obvious from the stack trace and a one-line fix works — just fix it.
• The user wants a code review of an unrelated change — that's audit.
• The user wants to map an unfamiliar feature — that's research.

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Phase 1 — Build a feedback loop

This is the skill. Everything else is mechanical. If you have a fast, deterministic, agent-runnable pass/fail signal for the bug, you will find the cause — bisection, hypothesis-testing, and instrumentation all just consume that signal. If you don't have one, no amount of staring at code will save you.

Spend disproportionate effort here. Be aggressive. Be creative. Refuse to give up.

Ways to construct one — try them in roughly this order

1. Failing test at whatever seam reaches the bug — unit, integration, e2e.
2. Curl / HTTP script against a running dev server.
3. CLI invocation with a fixture input, diffing stdout against a known-good snapshot.
4. Headless browser script (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
5. Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
6. Throwaway harness. Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
7. Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
8. Bisection harness. If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can git bisect run it.
9. Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs.
10. HITL bash script. Last resort. If a human must click, drive them with a structured read -p loop so the loop is still tight. Captured output feeds back to you.

Build the right feedback loop and the bug is 90% fixed.

Iterate on the loop itself

Treat the loop as a product. Once you have a loop, ask:

• Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)
• Can I make the signal sharper? (Assert on the specific symptom, not "didn't crash".)
• Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)

A 30-second flaky loop is barely better than no loop. A 2-second deterministic loop is a debugging superpower.

Non-deterministic bugs

The goal is not a clean repro but a higher reproduction rate. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not — keep raising the rate until it is.

When you genuinely cannot build a loop

Stop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do not proceed to hypothesise without a loop.

Do not proceed to Phase 2 until you have a loop you believe in.

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Phase 2 — Reproduce

Run the loop. Watch the bug appear.

Confirm:

• The loop produces the failure mode the user described — not a different failure that happens to be nearby. Wrong bug = wrong fix.
• The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).
• You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.

Do not proceed until you reproduce the bug.

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Phase 3 — Hypothesise

Generate 3–5 ranked hypotheses before testing any of them. Single-hypothesis generation anchors on the first plausible idea.

Each hypothesis must be falsifiable: state the prediction it makes.

Format: "If is the cause, then will make the bug disappear / will make it worse."

If you cannot state the prediction, the hypothesis is a vibe — discard or sharpen it.

Show the ranked list to the user before testing if they're watching. Domain knowledge re-ranks instantly ("we just deployed a change to #3", "we already ruled out #1 last week"). Cheap checkpoint, big time saver. Don't block on it — proceed with your ranking if the user is AFK or autopilot.

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Phase 4 — Instrument

Each probe must map to a specific prediction from Phase 3. Change one variable at a time.

Tool preference:

1. Debugger / REPL inspection if the env supports it. One breakpoint beats ten logs.
2. Targeted logs at the boundaries that distinguish hypotheses.
3. Never "log everything and grep".

Tag every debug log with a unique prefix, e.g. [DEBUG-a4f2]. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.

Perf branch. For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, performance.now(), profiler, query plan), then bisect. Measure first, fix second.

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Phase 5 — Fix + regression test

Write the regression test before the fix — but only if there is a correct seam for it.

A correct seam is one where the test exercises the real bug pattern as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.

If no correct seam exists, that itself is the finding. Note it. The codebase architecture is preventing the bug from being locked down. Flag this for Phase 6.

If a correct seam exists:

1. Turn the minimised repro into a failing test at that seam.
2. Watch it fail.
3. Apply the fix.
4. Watch it pass.
5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.

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Phase 6 — Cleanup + post-mortem

Required before declaring done:

• Original repro no longer reproduces (re-run the Phase 1 loop).
• Regression test passes (or absence of seam is documented).
• All [DEBUG-...] instrumentation removed (grep the prefix).
• Throwaway prototypes deleted (or moved to a clearly-marked debug location).
• The hypothesis that turned out correct is stated in the commit / PR message — so the next debugger learns.

Then ask: what would have prevented this bug? If the answer involves architectural change (no good test seam, tangled callers, hidden coupling), capture it as a finding and surface to the user — likely a follow-up audit or an rfc draft. Make the recommendation after the fix is in, not before — you have more information now than when you started.

If the bug uncovered a missing project rule (e.g. "always validate inputs at this boundary"), add it to CONTEXT.md or CLAUDE.md so future sessions don't repeat it.

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Output format (final report)

# Diagnose: <bug summary>

**Status**: FIXED | PARTIAL FIX | UNRESOLVED
**Time**: <h:mm>  **Phases entered**: 1–6 (or "stopped at Phase N: <reason>")

## Feedback loop
- Type: <test/curl/cli/replay/harness/fuzz/bisect/differential/hitl>
- Speed: <Xs per cycle>  Determinism: <%>  Sharpness: <what it asserts>

## Reproduction
- Symptom: <exact failure mode>
- Rate: <100% / X% / N out of M runs>

## Hypotheses
1. ✅/❌ <H1> — prediction: <…> — verdict: <evidence>
2. ✅/❌ <H2> — prediction: <…> — verdict: <evidence>
3. ✅/❌ <H3> — prediction: <…> — verdict: <evidence>

## Root cause
<one paragraph — what was actually wrong, why>

## Fix
- File: `<path:line>` — <what changed>
- Regression test: `<path>` (or "no correct seam — see Findings")

## Findings
- <architectural / process insights worth surfacing>

## Cleanup
- [ ] Loop removed / archived: <where>
- [ ] DEBUG tags removed: <prefix grepped>
- [ ] Prototypes deleted: <files>

---

Related skills

• audit — for code-quality review of the fix, or for architectural findings surfaced in Phase 6.
• research — when the bug area is unfamiliar; do a quick research pass before Phase 1.
• restore — to recover context on the bug area before starting (recent commits, related TODOs).
• rfc — when Phase 6 surfaces an architectural change worth proposing formally.

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Anti-patterns

• ❌ Skipping Phase 1. Hypothesising before you can reproduce wastes hours. Build the loop first.
• ❌ Single hypothesis. Anchors on the first plausible idea. Always generate 3–5.
• ❌ Untagged debug logs. They survive into commits. Tag with a unique prefix you can grep.
• ❌ Logs for performance bugs. Logs lie about timing. Use measurements (profiler, performance.now, query plan).
• ❌ Test at the wrong seam. A passing unit test that doesn't exercise the real bug pattern is false confidence.
• ❌ "Done" without re-running the original loop. A green regression test ≠ a fixed bug. Re-run the original repro.
• ❌ Architectural recommendation before the fix. You don't know enough yet. Recommend in Phase 6, not Phase 1.

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Forgeplan integration

This skill is forgeplan-aware with hybrid MCP/CLI dispatch per PRD-022. A verified fix is Evidence-worthy — /diagnose captures it in the artifact graph automatically.

Probe MCP availability (one call)

# True if the forgeplan MCP server is wired in this session
have_mcp = "mcp__forgeplan__forgeplan_new" in available_tools

If have_mcp is unclear, attempt mcp__forgeplan__forgeplan_health(). Connection error → have_mcp = False, fall through to CLI block.

When /diagnose <bug> finds a fix — MCP-first flow (have_mcp = True)

# Create Evidence for the verified fix
evid = mcp__forgeplan__forgeplan_new(
    kind="evidence",
    title=f"<bug>: root cause = <X>; fix = <Y>; verified by <test/repro>"
)
EVID_ID = evid["id"]

# Body: include the diagnostic trace (what you tried, what worked)
mcp__forgeplan__forgeplan_update(
    id=EVID_ID,
    body="<diagnostic trace + structured fields: verdict=supports|refutes, "
         "congruence_level=3 (CL3 same-context), evidence_type=measurement|test_result>"
)

# Link to the parent feature PRD (if known)
if PARENT_PRD:
    mcp__forgeplan__forgeplan_link(source=EVID_ID, target=PARENT_PRD, relation="informs")

# If the bug surfaced a deeper architectural issue, capture problem + solution
if architectural_issue:
    prob = mcp__forgeplan__forgeplan_new(kind="problem", title="<problem card>")
    mcp__forgeplan__forgeplan_link(source=prob["id"], target=PARENT_PRD, relation="informs")
    if multiple_solutions_warranted:
        sol = mcp__forgeplan__forgeplan_new(
            kind="solution",
            title=f"<solution portfolio for {prob['id']}>"
        )

CLI fallback (have_mcp = False, forgeplan on $PATH)

forgeplan new evidence "<bug>: root cause = <X>; fix = <Y>; verified by <test/repro>"
# Body: include the diagnostic trace
# Structured Fields: verdict / congruence_level / evidence_type
forgeplan link EVID-MMM PRD-NNN --relation informs

# Architectural issue
forgeplan new problem "<problem card>"
forgeplan link PROB-NNN PRD-MMM --relation informs
forgeplan new solution "<solution portfolio for PROB-NNN>"

No-forgeplan environment

If neither MCP nor forgeplan CLI is reachable: tell the user "diagnose complete; forgeplan not detected — capture findings in your project's own issue tracker manually". Don't silently skip.

Want this orchestrated for you?

For complex diagnose tasks that warrant a full lifecycle (PRD → fix → evidence → activate), pair this skill with forgeplan-workflow — /forge-cycle "fix <bug>" will route, shape, build, and capture evidence in one flow.