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pick-model

Recommend optimal Claude model (haiku/sonnet/opus) for a task. Use when user asks "which model", "pick model", "model for", or before starting costly/complex tasks. Covers tech and non-tech tasks.

64

Quality

76%

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tessl review fix ./skills/agentic-harness/pick-model/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured decision-guide skill with genuinely actionable guidance: concrete matrices, quantified escalator rules, a copy-paste output template, and extensive worked examples, all properly backed by a real one-level-deep reference file. The main weakness is token efficiency — the long inline example tables and a redundant Usage Examples section duplicate content and inflate the always-loaded context.

Suggestions

Cut or collapse the "Usage Examples" bash section — its three cases restate examples already present in the Examples tables and anti-pattern guidance.

Move most of the four Examples tables into references/reference.md, keeping 2-3 exemplar rows per domain inline so the core matrix + escalator procedure stays lean.

Trim the "Why:" explanations in Anti-Patterns to one line each; the failure modes (benchmark drift, silent deprecation) are already familiar and the BAD/GOOD snippets carry the message.

DimensionReasoningScore

Conciseness

The decision matrices and escalator tables are high-signal, but the body runs ~240 lines with clear tightening opportunities: the "Usage Examples" bash blocks restates cases already covered by the four Examples tables, and the Anti-Patterns spend tokens explaining things Claude already knows (benchmark caveats, why hardcoding identifiers breaks). This fits 'mostly efficient but includes some unnecessary explanation or could be tightened' rather than level 4, whose over-explanation would be only minor.

3 / 5

Actionability

The guidance is fully executable for an instruction-only skill: a numbered procedure (parse $ARGUMENTS → classify against matrix → output via template), an exact copy-paste output format with a filled example, explicit +1-tier escalator rules with a cap, tie-breaker heuristics, and ~30 worked examples with rationale across all four task domains. This matches the top anchor's 'specific examples cover the common cases'.

5 / 5

Workflow Clarity

The three-step Instructions sequence is clear, escalator checking is integrated, and the anti-pattern "NEVER skip escalator checks for ambiguous tasks" with its BAD/GOOD example acts as a pre-commit checkpoint, matching 'clear sequence with most checkpoints present'. Not level 5 because there is no explicit verify step in the workflow itself (e.g., 're-check escalators before outputting') and no guidance for edge cases like empty $ARGUMENTS; not level 3 because the sequence and checkpoints are more explicit than 'checkpoints missing or implicit'.

4 / 5

Progressive Disclosure

The bundle structure is sound: a single one-level-deep reference (references/reference.md, 216 lines, verified to exist and contain the extended matrix/cost data as advertised) is clearly signaled in a References section with a description of its contents. It stops short of level 5 because a sizable chunk of inline material — the four Examples tables and especially the redundant "Usage Examples" section — reads like reference material that belongs in reference.md, leaving the overview longer than it needs to be.

4 / 5

Total

16

/

20

Passed

Description

78%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description that clearly states a single concrete capability and pairs it with explicit, naturally-phrased trigger conditions. Its main limitations are single-action scope and a couple of missing trigger synonyms plus the mildly ambiguous "model for" phrase.

Suggestions

Add trigger synonyms such as "choose a model", "model selection", or "fast vs. cheap model" to broaden natural-term coverage.

Qualify the "model for" trigger (e.g., "which/what model for this task") to reduce false triggers on data-modeling or 3D-modeling requests.

DimensionReasoningScore

Specificity

"Recommend optimal Claude model (haiku/sonnet/opus) for a task" names the domain and one concrete action (recommend a model, with the candidate tiers listed), but offers no secondary actions — matching the '1-2 concrete actions, not comprehensive' anchor. It is above level 2 because the action is concrete and enumerated rather than generic, and below level 4 because there are not several distinct specific actions.

3 / 5

Completeness

It explicitly answers both: what ("Recommend optimal Claude model (haiku/sonnet/opus) for a task") and when ("Use when user asks 'which model', 'pick model', 'model for', or before starting costly/complex tasks") with concrete trigger phrases, matching the top anchor. Not level 4 because the 'when' clause is already explicit and trigger-quoted, not merely present-but-imprecise.

5 / 5

Trigger Term Quality

Quoted triggers "which model", "pick model", "model for", plus "before starting costly/complex tasks", are natural phrases a user would say. It falls short of level 5's comprehensive synonym coverage (missing variations like "choose a model", "model selection", "fast/cheap model"), but is well above level 3's 'missing common variations' since multiple natural phrasings are quoted verbatim.

4 / 5

Distinctiveness Conflict Risk

The model-recommendation niche with quoted triggers is mostly distinct from other skills, matching 'mostly distinct; minor overlap risk'. Not level 5 because the trigger phrase "model for" is generic enough to fire on unrelated requests (e.g., "create a model for forecasting"), and "Covers tech and non-tech tasks" widens the surface slightly.

4 / 5

Total

16

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
pantheon-org/tekhne
Reviewed

Table of Contents

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