PoseCap: Skill for Codex

<background_information> Implements WORKFLOW.md §14 (Staged Spikes With Golden Fixtures) end-to-end. The skill is for cases where the spec is clear but the technique is uncertain across multiple plausible approaches. WORKFLOW §9 (TDG) assumes the path is known and validates end-to-end; §14 assumes the path is unknown and validates per stage.

The skill creates spikes/NNNN-<slug>/ and fills it stage-by-stage. The directory is throwaway by design — when the spike concludes, an ADR records the decision and the spike directory is deleted (promote-or-delete lifecycle' '

Codex auto-trigger on description keywords is less mature than Claude Code's. If auto-invocation does not fire when the user mentions an uncertain technique or asks to evaluate approaches, invoke this skill manually. </background_information>

Tests:

• Could ad-ground's four-source research surface a single happy path with a defensible deviation gate? If yes, run that instead.
• Are there ≥2 candidate techniques with materially different trade-offs that no source resolves? If no, this is not a spike.
• Is end-to-end validation feasible without per-stage debug? If yes, this is ad-task + ad-philosophy Goal-Driven Execution territory.

If spike warranted, confirm the recortte with the user and proceed.

Step 1 — discovery. List canonical approaches grounded in official docs and real examples. Pick one (or ≤3) by an explicit criterion.

Process:

• Search official documentation. Cite URL + version.
• Search public implementation references (open-source repos, Stack Overflow / forum answers, blog posts, gists) for solutions to the same recortte. Cite <source>:<locator> — <repo>:<path>:<line-range> for repos, <URL> for Stack Overflow / blog / gist — and fetch via tools; never paraphrase from training memory.
• Survey in-repo for analogous patterns. Cite <file>:<line> or "no analog found".
• Survey git history for prior attempts. Cite <commit-sha> or "no prior attempt".

Output: candidate-list markdown with techniques, sources, trade-offs, selection criterion, picked technique. NO code yet. User reviews before Step 2.

Step 2 — golden fixture. Curate inputs with rich expected outputs. JSON keyed by input path (recommended). Include edge cases (low light, partial occlusion, malformed inputs, large inputs, empty inputs) and difficulty tags.

Create the spike directory:

mkdir -p spikes/NNNN-<slug>/{fixtures,debug,eval}

NNNN = next 4-digit number after highest existing under spikes/.

The fixture is the contract the pipeline validates against. Treat like spec text — should not change once the spike runs unless ground truth changes.

Step 3 — pipeline with gates. One technique per stage. Each stage emits a debug artifact making its output inspectable.

Layout:

spikes/NNNN-<slug>/
├── README.md          # spike framing (Step 1 output)
├── fixtures/          # golden inputs + expected outputs
├── pipeline/          # one file per stage (01-preprocess, 02-detect, etc)
├── debug/             # per-stage debug artifacts (image / JSON / log row)
└── eval/              # evaluation results (Step 4)

Each stage takes (input, context), returns (output, debug-record). Debug-record written to debug/NN-<stage>/. Pipeline halts and reports stage on first divergence.

Step 4 — two-layer evaluation:

• End-to-end: pass rate against fixture inputs.
• Per-stage: for each input, where did pipeline diverge?

Output to spikes/NNNN-<slug>/eval/results.json:

{
  "fixture": "fixtures/golden.json",
  "end_to_end": { "total": 10, "passed": 7, "failed": 3 },
  "per_stage": { "01-preprocess": { "passed": 10, "failed": 0 }, ... },
  "failures": [{ "input": "...", "diverged_at": "02-detect", "debug_artifact": "..." }]
}

Per-stage layer is what makes the spike actionable.