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token-optimizer

Route broad repository discovery to a cheaper worker model using each client's native subagents, keeping the main agent for decisions and targeted verification. Use when asked to "reduce token usage", "delegate bulk reading", "set up a cheap reader agent", or "why is my context filling up".

Quality

82%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

77%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 dense, highly actionable operational script with excellent sequencing and genuine validation/feedback loops on its destructive operations, weakened by editorial padding and by monolithic inlining where reference files were clearly intended — including one reference to a file that is absent from the bundle.

Suggestions

Trim the design-history and rationale prose (remove-mode preamble, step 10b justification, the VS Code limitation paragraphs in step 9) to the operative instruction plus a one-line reason, moving conciseness from 'some unnecessary explanation' toward 'efficient'.

Ship references/token-optimizer.md (referenced at the top of the body but absent from the bundle), or remove the dangling pointer; if shipped, split the setup install script and the benchmark record-field/cost-model detail into it so SKILL.md stays an overview with clearly signaled one-level-deep references.

Move the per-mode long reference material (doctor's probe-version logic, remove's hash-manifest semantics, benchmark's JSONL schema) into per-mode reference files linked from each mode header, which would both fix the dangling-reference problem and let progressive disclosure score against real bundle structure.

DimensionReasoningScore

Conciseness

Most content is non-obvious operational detail Claude would not know (exact jq hook-registration idioms, client caps, ordering constraints), but extended design-history and justification prose pads the body — e.g. the remove-mode preamble 'Two properties have to hold at once, and an earlier revision traded one away for the other', the step 10b rationale paragraph, and the multi-paragraph VS Code limitations narrative could each be tightened without losing the instruction. This sits between 'mostly efficient but includes some unnecessary explanation' and 'efficient; minor instances of over-explanation' — the verbosity is more than minor, but it is not explaining concepts Claude already knows, so 3 rather than 2.

3 / 5

Actionability

Nearly every step is copy-paste ready: exact bash blocks ('mkdir -p .token-optimizer', the full jq add_group program for .claude/settings.json, the awk model:-rewrite in 10b), a complete .token-optimizer.yaml template, concrete per-mode validation commands, and explicit output formats for doctor/report. Placeholder variables ($client, $chosen_mode, INSTALLED_ASSETS) are each defined by surrounding steps, and the few abstract passages (benchmark record fields) deliberately mirror a manifest file. This matches the anchor 'Fully executable; copy-paste ready code or commands; specific examples cover the common cases'.

5 / 5

Workflow Clarity

Each of the eight modes has a numbered, explicitly-ordered sequence with per-mode validation blocks, and the risky operations carry real feedback loops: an unverified probe downgrades redirect to audit and names the command to re-run ('Run .token-optimizer/probe/run-probe.sh to verify delegation, then re-run setup'), and remove recomputes each hash before any deletion, preserves mismatches, and uses rmdir instead of rm -rf. Ordering constraints are stated with reasons (manifest 'Run LAST', 'Last: drop the manifest, then the directories'). This matches the top anchor for clear sequence, explicit validation, and error-recovery loops.

5 / 5

Progressive Disclosure

The body has real structure (per-mode headers, per-mode Validation sections), but it is a ~680-line monolith in which content that clearly belongs in separate files is inlined (the full benchmark JSONL field list, the remove-mode hash-manifest semantics, the complete setup install script), and its single reference — 'Read references/token-optimizer.md before responding' — is a one-line pointer to a file that does not exist in this bundle (no references/ directory is present), so the pointer is neither clearly signaled per-section nor loadable. This is above 'minimal structure / buried references' (2) because headers and validation sections make it navigable, but below 'most content appropriately placed' (4) because of the dangling reference and the bulk detail that should live in reference files.

3 / 5

Total

16

/

20

Passed

Description

83%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 in third-person voice with a clear what, an explicit when-clause carrying natural quoted trigger phrases, and a distinctive niche; its only weaknesses are incomplete enumeration of the skill's capabilities and a few missing synonym triggers.

DimensionReasoningScore

Specificity

The description names several concrete actions — 'Route broad repository discovery to a cheaper worker model using each client's native subagents', 'keeping the main agent for decisions and targeted verification' — which goes beyond the 1-2 actions of the score-3 anchor, but coverage has gaps: none of the skill's eight operational modes (inspect, setup, doctor, benchmark, report, remove) are hinted at, so it is not the comprehensive coverage of the score-5 anchor.

4 / 5

Completeness

Both questions are answered explicitly: the 'what' is concrete ('Route broad repository discovery to a cheaper worker model using each client's native subagents, keeping the main agent for decisions and targeted verification') and the 'when' is an explicit 'Use when asked to' clause carrying concrete trigger phrases. This matches the top anchor: clearly and explicitly answers both what AND when with concrete trigger phrases, and the neighboring score-4 anchor ('when could be more explicit') does not fit since the when-clause is fully explicit.

5 / 5

Trigger Term Quality

Four quoted natural phrases — "reduce token usage", "delegate bulk reading", "set up a cheap reader agent", "why is my context filling up" — read as things a user would actually say, giving good keyword coverage. It falls short of the score-5 'comprehensive coverage including synonyms' anchor because common variations like 'context window', 'save tokens', 'token cost', or 'use a cheaper model' are missing.

4 / 5

Distinctiveness Conflict Risk

The niche — routing bulk repository reads to a cheaper worker model via each client's native subagent mechanism — is distinctive and its triggers ('delegate bulk reading', 'cheap reader agent') are unlikely to fire for an unrelated skill. Minor overlap remains with closely related skills (general context-management or subagent-orchestration skills), so it is 'mostly distinct; minor overlap risk' rather than the score-5 'clear niche; minimal conflict risk'.

4 / 5

Total

17

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (688 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

13

/

16

Passed

Repository

nitinjain999/platform-skills

Path

skills/token-optimizer/SKILL.md

Reviewed

Table of Contents

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