plugins/bin/.apm/skills/diagnose/SKILL.md

@@ -1,6 +1,9 @@
 ---
 name: diagnose
-description: Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
+description: >
+  Use when the user says "diagnose this" or "debug this", reports something
+  broken, throwing, or failing, or says something got slow. Not filing or
+  triaging a reported bug -> `triage`. Not test-first feature work -> `tdd`.
 ---
 
 # Diagnose
@@ -15,40 +18,9 @@ When exploring the codebase, use the project's domain glossary to get a clear me
 
 Spend disproportionate effort here. **Be aggressive. Be creative. Refuse to give up.**
 
-### Ways to construct one — try them in roughly this order
+Read `references/feedback-loops.md` — even if you already have a signal. Ten ways to build a loop ordered by cost, how to sharpen the one you have, and what to do when the bug resists reproduction. An unsharpened loop is usually not good enough yet.
 
-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 `scripts/hitl-loop.template.sh` so the loop is still structured. 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's debuggable.
-
-### 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.
+Do not proceed to Phase 2 until you have a loop you believe in. If you cannot build one, stop and say so explicitly, listing what you tried — never hypothesise without a signal.
 
 ## Phase 2 — Reproduce
 
@@ -57,7 +29,7 @@ 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).
+- [ ] The failure is reproducible across multiple runs. If it is intermittent, `references/feedback-loops.md` defines the rate high enough to debug against — go back to Phase 1 and raise it.
 - [ ] 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.
@@ -98,11 +70,11 @@ A correct seam is one where the test exercises the **real bug pattern** as it oc
 
 If a correct seam exists:
 
-1. Turn the minimised repro into a failing test at that seam.
+1. Turn the Phase 1 loop into a failing test at that seam, narrowed to the symptom captured in Phase 2.
 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.
+5. Re-run the Phase 1 feedback loop against the original, un-narrowed scenario.
 
 ## Phase 6 — Cleanup + post-mortem
 

plugins/bin/.apm/skills/diagnose/references/feedback-loops.md

@@ -0,0 +1,40 @@
+# Constructing and sharpening a feedback loop
+
+A feedback loop is a fast, deterministic, agent-runnable pass/fail signal for the bug. Build the right one and the bug is 90% fixed. This file covers the whole arc: building a loop, sharpening one you already have, and escalating when the bug resists reproduction.
+
+## 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 `../scripts/hitl-loop.template.sh` so the loop is still structured. Captured output feeds back to you.
+
+## 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.
+
+## Intermittent bugs — raise the reproduction rate
+
+If the loop only sometimes fails, 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's debuggable.
+
+## When you genuinely cannot build a loop
+
+Stop and say so explicitly. List what you tried. Ask the user for:
+
+- access to whatever environment reproduces it,
+- a captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or
+- permission to add temporary production instrumentation.
+
+Do **not** proceed to hypothesise without a loop. A hypothesis you cannot falsify against a signal is a guess, and the fix that follows it is unverifiable.

plugins/bin/skills/diagnose/SKILL.md

@@ -1,6 +1,9 @@
 ---
 name: diagnose
-description: Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
+description: >
+  Use when the user says "diagnose this" or "debug this", reports something
+  broken, throwing, or failing, or says something got slow. Not filing or
+  triaging a reported bug -> `triage`. Not test-first feature work -> `tdd`.
 ---
 
 # Diagnose
@@ -15,40 +18,9 @@ When exploring the codebase, use the project's domain glossary to get a clear me
 
 Spend disproportionate effort here. **Be aggressive. Be creative. Refuse to give up.**
 
-### Ways to construct one — try them in roughly this order
+Read `references/feedback-loops.md` — even if you already have a signal. Ten ways to build a loop ordered by cost, how to sharpen the one you have, and what to do when the bug resists reproduction. An unsharpened loop is usually not good enough yet.
 
-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 `scripts/hitl-loop.template.sh` so the loop is still structured. 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's debuggable.
-
-### 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.
+Do not proceed to Phase 2 until you have a loop you believe in. If you cannot build one, stop and say so explicitly, listing what you tried — never hypothesise without a signal.
 
 ## Phase 2 — Reproduce
 
@@ -57,7 +29,7 @@ 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).
+- [ ] The failure is reproducible across multiple runs. If it is intermittent, `references/feedback-loops.md` defines the rate high enough to debug against — go back to Phase 1 and raise it.
 - [ ] 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.
@@ -98,11 +70,11 @@ A correct seam is one where the test exercises the **real bug pattern** as it oc
 
 If a correct seam exists:
 
-1. Turn the minimised repro into a failing test at that seam.
+1. Turn the Phase 1 loop into a failing test at that seam, narrowed to the symptom captured in Phase 2.
 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.
+5. Re-run the Phase 1 feedback loop against the original, un-narrowed scenario.
 
 ## Phase 6 — Cleanup + post-mortem
 

plugins/bin/skills/diagnose/references/feedback-loops.md

@@ -0,0 +1,40 @@
+# Constructing and sharpening a feedback loop
+
+A feedback loop is a fast, deterministic, agent-runnable pass/fail signal for the bug. Build the right one and the bug is 90% fixed. This file covers the whole arc: building a loop, sharpening one you already have, and escalating when the bug resists reproduction.
+
+## 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 `../scripts/hitl-loop.template.sh` so the loop is still structured. Captured output feeds back to you.
+
+## 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.
+
+## Intermittent bugs — raise the reproduction rate
+
+If the loop only sometimes fails, 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's debuggable.
+
+## When you genuinely cannot build a loop
+
+Stop and say so explicitly. List what you tried. Ask the user for:
+
+- access to whatever environment reproduces it,
+- a captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or
+- permission to add temporary production instrumentation.
+
+Do **not** proceed to hypothesise without a loop. A hypothesis you cannot falsify against a signal is a guess, and the fix that follows it is unverifiable.