feat(kyberforge): execute plugin-to-apm marketplace conversion
Why: ADR-0015 established that Microsoft APM (apm.yml + .apm/) should replace this repo's hand-authored plugin.json/marketplace.json model, with those files becoming compiled output of `apm pack` instead of files edited by hand via the (now-retired) plugin-author/marketplace-author skills. Issue #90 was the deferred execution of that decision, gated on #88 (apm tooling) and #89 (apm-native agent-author/skill-author routing). Implementation notes: - All six plugins (bin, core, git, gitea, kyberforge, lint) now carry apm.yml + .apm/{skills,agents,hooks} as their authoring source. Skills moved with a plain git mv (content-identical across targets). Agents were re-authored, not moved: per ADR-0016, .apm/agents/*.agent.md compiles verbatim to both Claude and Copilot, so plugin-scope agents now carry only name/description/model/source_keys -- no tools: field, no Claude-only knobs (isolation, maxTurns, effort, memory, permissionMode). - Root apm.yml registers all 7 marketplace packages (6 local plus mattpocock-skills as a remote entry) under versioning: per_package, matching this repo's existing independent-plugin-versioning practice. - .claude-plugin/marketplace.json and every plugin's plugin.json are now apm-pack-compiled output, verified against the prior hand-maintained content: same names/descriptions/versions/licenses/authors, only cosmetic serialization differences (JSON key order, owner email vs. url, Unicode escaping). - plugin-author and marketplace-author are retired now that apm-based authoring fully replaces their job; kyberforge bumped 1.3.1 -> 1.4.0 for that removal, and the root marketplace catalog bumped 0.3.1 -> 0.3.2 to match, per the version-bump convention now documented in apm-workflow's reference docs instead of a dedicated script (apm has no native version-bump automation). - Fixed hardcoded pre-.apm/ path assumptions across .pre-commit-config.yaml, .pre-commit-hooks.yaml, scripts/check-scope-walkup-sync.sh, scripts/sync-vale-styles.sh, scripts/check-vale-style-sync.sh, six plugins' root plugin.json (stale skills/hooks/agents pointer fields that check-manifests.sh validates), and several tests/*.bats and tests/*.sh fixtures -- including a bats REPO_ROOT relative-path depth bug (10 files, one extra .apm/ directory level to walk up) and a vale probe-path isolation regression introduced mid-fix. - Corrected empirically-wrong assumptions surfaced this session in apm-workflow/apm-install's own reference docs: `apm marketplace package add` does not accept local paths (only owner/repo remote shorthand -- local packages are registered by editing apm.yml's marketplace.packages[] directly); `apm compile` is a consumer-side AGENTS.md/CLAUDE.md generator, not the plugin.json producer, and hard-fails on skill/agent-only packages without --clean; `apm plugin init <name>` nests a stray subdirectory when run with a positional name arg from inside a same-named directory; no native Copilot marketplace output profile exists; .mcp.json is merged into the compiled plugin.json content-aware and target-scoped, with no dependencies.mcp entry needed for simple passthrough; pipx is the correct pip fallback on externally-managed Python environments. - Renamed agent-author's copilot.agent.md template asset to copilot.agent.md.template so apm compile's recursive *.agent.md glob stops misparsing the placeholder template as a real agent primitive. Impact: plugin.json and marketplace.json are compiled artifacts from here on -- editing them by hand is no longer the workflow; edit apm.yml/.apm/ and run apm pack. CONTEXT.md's Plugin/Plugin marketplace glossary entries reflect this. ADR-0001 is marked superseded, ADR-0006 moot, and ADR-0010 updated for the new .apm/agents/ path (project/user scope unaffected, per ADR-0016). Full local verification: claude plugin validate --strict on all 6 plugins, apm audit --ci, apm marketplace check, check-manifests.sh, and the full test suite (165/165 bats, 13/13 shell scripts) all pass clean. Fixes: #90 Refs: #88, #89 ADR: 0015 ADR: 0016 Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ub96PyaSRD9BHPktotj1pC
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plugins/bin/.apm/skills/diagnose/SKILL.md
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plugins/bin/.apm/skills/diagnose/SKILL.md
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---
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name: diagnose
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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.
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---
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# Diagnose
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A discipline for hard bugs. Skip phases only when explicitly justified.
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When exploring the codebase, use the project's domain glossary to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.
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## Phase 1 — Build a feedback loop
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**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.
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Spend disproportionate effort here. **Be aggressive. Be creative. Refuse to give up.**
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### Ways to construct one — try them in roughly this order
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1. **Failing test** at whatever seam reaches the bug — unit, integration, e2e.
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2. **Curl / HTTP script** against a running dev server.
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3. **CLI invocation** with a fixture input, diffing stdout against a known-good snapshot.
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4. **Headless browser script** (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
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5. **Replay a captured trace.** Save a real network request / payload / event log to disk; replay it through the code path in isolation.
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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.
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7. **Property / fuzz loop.** If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
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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.
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9. **Differential loop.** Run the same input through old-version vs new-version (or two configs) and diff outputs.
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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.
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Build the right feedback loop, and the bug is 90% fixed.
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### Iterate on the loop itself
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Treat the loop as a product. Once you have _a_ loop, ask:
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- Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)
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- Can I make the signal sharper? (Assert on the specific symptom, not "didn't crash".)
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- Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)
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A 30-second flaky loop is barely better than no loop. A 2-second deterministic loop is a debugging superpower.
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### Non-deterministic bugs
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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.
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### When you genuinely cannot build a loop
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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.
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Do not proceed to Phase 2 until you have a loop you believe in.
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## Phase 2 — Reproduce
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Run the loop. Watch the bug appear.
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Confirm:
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- [ ] The loop produces the failure mode the **user** described — not a different failure that happens to be nearby. Wrong bug = wrong fix.
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- [ ] The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).
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- [ ] You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.
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Do not proceed until you reproduce the bug.
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## Phase 3 — Hypothesise
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Generate **3–5 ranked hypotheses** before testing any of them. Single-hypothesis generation anchors on the first plausible idea.
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Each hypothesis must be **falsifiable**: state the prediction it makes.
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> Format: "If <X> is the cause, then <changing Y> will make the bug disappear / <changing Z> will make it worse."
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If you cannot state the prediction, the hypothesis is a vibe — discard or sharpen it.
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**Show the ranked list to the user before testing.** They often have domain knowledge that re-ranks instantly ("we just deployed a change to #3"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it — proceed with your ranking if the user is AFK.
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## Phase 4 — Instrument
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Each probe must map to a specific prediction from Phase 3. **Change one variable at a time.**
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Tool preference:
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1. **Debugger / REPL inspection** if the env supports it. One breakpoint beats ten logs.
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2. **Targeted logs** at the boundaries that distinguish hypotheses.
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3. Never "log everything and grep".
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**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.
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**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
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Write the regression test **before the fix** — but only if there is a **correct seam** for it.
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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.
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**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 the next phase.
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If a correct seam exists:
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1. Turn the minimised repro into a failing test at that seam.
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2. Watch it fail.
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3. Apply the fix.
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4. Watch it pass.
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5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.
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## Phase 6 — Cleanup + post-mortem
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Required before declaring done:
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- [ ] Original repro no longer reproduces (re-run the Phase 1 loop)
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- [ ] Regression test passes (or absence of seam is documented)
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- [ ] All `[DEBUG-...]` instrumentation removed (`grep` the prefix)
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- [ ] Throwaway prototypes deleted (or moved to a clearly-marked debug location)
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- [ ] The hypothesis that turned out correct is stated in the commit / PR message — so the next debugger learns
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**Then ask: what would have prevented this bug?** If the answer involves architectural change (no good test seam, tangled callers, hidden coupling) hand off to the `/improve-codebase-architecture` skill with the specifics. Make the recommendation **after** the fix is in, not before — you have more information now than when you started.
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