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Defame1297 5e232503c4 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

2026-08-12 18:21:24 +00:00

7.0 KiB

Raw Blame History

name, description

name	description
diagnose	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.

Diagnose

A discipline for hard bugs. Skip phases only when explicitly justified.

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.

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

Failing test at whatever seam reaches the bug — unit, integration, e2e.
Curl / HTTP script against a running dev server.
CLI invocation with a fixture input, diffing stdout against a known-good snapshot.
Headless browser script (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
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.
Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
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.
Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs.
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.

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.

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. 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.

Phase 4 — Instrument

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

Tool preference:

Debugger / REPL inspection if the env supports it. One breakpoint beats ten logs.
Targeted logs at the boundaries that distinguish hypotheses.
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.

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 the next phase.

If a correct seam exists:

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

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) 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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