Optimize

This skill orchestrates optimize-skill-instructions, setup-skill-performance, and optimize-skill-performance into a single end-to-end optimization cycle.

The full cycle takes 1–2 hours depending on how many scenarios and improvement iterations are needed. Set this expectation with the user upfront.

Overview

Review SKILL.md → Apply quick wins → Generate scenarios → Activation check → Content evals → Analyze → Fix → Re-run → Report
└── optimize-skill-instructions ──┘  └─────────── setup-skill-performance ───────────┘  └──────────── optimize-skill-performance ────────────┘

Two distinct eval types run in this cycle:

• Activation eval — observes which skill self-activates per scenario (does NOT force activation). Tests routing/description quality. ~2–3 min.
• Content eval — forces activation, runs baseline vs. with-context, scores the rubric. Tests content quality. ~10–15 min per scenario per agent.

Both apply to every tile (single-skill and multi-skill alike). The variable is ordering, not whether to run them.

Run labels

Every tessl eval run invocation MUST include --label <run-label> so the run is identifiable in tessl eval list. The label is a short, human-readable description of what the run is about — not a structured ID.

Compose <run-label> from whatever helps you recognise the run later when scanning the list. Typical ingredients:

• Eval type — e.g. activation, baseline, initial evals, verification
• What was being tested or changed — e.g. description rewrite, plan-solution fixes, clean scenario
• Model in parens, when comparing models — e.g. (haiku-4-5), (sonnet-4-6)
• Version when iterating — e.g. v0.5.0, v4

Examples:

• repro-clean-scenario
• task-prep v0.3.0 baseline
• task-prep v0.5.0 plan-solution fixes
• v4-final-verification
• skill-insights activation (haiku-4-5)
• skill-insights initial evals (haiku-4-5)

Keep it concise — what the run was about should be obvious without opening it.

Key commands

tessl skill review skills/<name>/SKILL.md     # review a skill (Step 1)
tessl scenario generate <tile-path> --count=5 # generate scenarios (Step 2)
tessl eval run <tile-path> --solver=activation --label <run-label> # test skill routing
tessl eval run <tile-path> --agent=claude:claude-sonnet-4-6 --label <run-label> # scored eval
tessl eval view --last --json                 # check results

Step 1: Review best practices

Invoke the optimize-skill-instructions skill. This runs tessl skill review on the tile's skill(s), surfaces scoring dimensions and quick wins, and applies approved changes.

Entry criteria: The tile has at least one SKILL.md.

Exit criteria: Review score is presented, approved quick wins are applied. Move to Step 2.

If the review score is already high (>= 85%) and the user is satisfied, skip to Step 2 without changes.

Step 2: Run setup-skill-performance (full pipeline scope)

Invoke the setup-skill-performance skill with scope = "Full pipeline". Skip the scope question — go straight to Phase 1.

Before invoking, decide eval ordering by skill count:

ls skills/*/SKILL.md 2>/dev/null | wc -l

• Multi-skill tile (count > 1): run Phase 4a (activation) BEFORE Phase 4b (content). Routing problems surface fast and prevent wasted content-eval time on misrouted scenarios.
• Single-skill tile (count == 1): run 4a and 4b in parallel, or 4a first if you prefer serial. A bad description means the skill never fires regardless of skill count, so 4a is required either way.

Work through all phases of setup-skill-performance (Find Tile → Generate Scenarios → Download & QC → Activation Check → Content Evals → View Results → Next Steps). Key parameters:

• Generate 3–5 scenarios from the tile
• Quality-check downloaded criteria for anti-patterns before running
• Default agent: claude:claude-sonnet-4-6

Decision point after results: If the activation check has been run and reviewed AND the content eval average is ≥ 85% with no regressions, stop and report success. Otherwise, continue to Step 3.

Step 3: Classify and prioritize

Before invoking optimize-skill-performance, do a quick triage of the results:

• If baseline is ≥ 80% on most scenarios: The scenarios may be too easy. Consider regenerating harder scenarios before trying to improve the tile.
• If regressions exist (with-context < baseline): These are highest priority — the tile is actively hurting.
• If with-context has room to grow: Proceed to optimize-skill-performance.

Step 4: Run optimize-skill-performance

Invoke the optimize-skill-performance skill starting from Phase 1 (it will detect the existing results).

Work through the improve cycle:

1. Analyze results — classify every criterion into buckets (working / gap / redundant / regression)
2. Diagnose root causes by reading the failing criteria and the tile files
3. Apply targeted, minimal fixes to the appropriate files
4. Re-run evals
5. Compare before/after

Iteration rule: Run up to 2 improve iterations. After the second, report results and stop — the user should review before investing more time.

Step 5: Report

Present a final summary. Activation and content results are reported separately because they measure different things — activation observes natural firing, content forces activation and scores task performance.

Optimization Complete

  Tile:         <tile-name>
  Review score: XX% → YY%
  Scenarios:    N scenarios
  Iterations:   X (1 setup + Y improve rounds)

  Activation Results (natural activation, no forcing)
    Scenarios where a skill fired:
      - Scenario A → fired: skills/<name>
      - Scenario C → fired: skills/<name>
    Scenarios where NO skill fired:
      - Scenario B
      - Scenario D

  Task Eval Results (forced activation)
    Scenario A:  baseline XX% → with-context YY%  (Δ +ZZ)
    Scenario B:  baseline XX% → with-context YY%  (Δ +ZZ)
    ...
    Average:     XX% → YY%

  Cross-reference (where the two eval types meet)
    No-activation but high baseline (no skill needed — routing is fine):
      - Scenario B (88% baseline) — agent already handles it
    No-activation AND low baseline (real routing gap — skill helps but doesn't fire):
      - Scenario D (25% baseline → 90% with-context) — suggested description edit: …

  Criteria improved:  [list]
  Still failing:      [list with brief reason]

  Eval runs:
    Activation: [URL]
    Content:    [URL]

If criteria remain stuck after 2 iterations, note whether the gap is addressable via documentation (suggest specific follow-up) or is inherently hard for the agent (suggest accepting or replacing the scenario).

When to stop

Stop when:

• Review score is high AND the activation check has been run and reviewed with the user AND content eval average ≥ 85% with no regressions
• 2 improve iterations have been completed
• The user says they're satisfied
• Further improvements would require restructuring the tile significantly (suggest this as a separate effort)

Note: activation findings (zero-firing skills, scenarios with no activation) drive follow-up actions (description rewrites, scenario edits) but are not a numeric pass/fail gate. The gate is "ran and reviewed", not a coverage percentage — natural activation is scenario-driven, so "X of Y skills fired" is not a useful score.