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codex-huge-context

Codex 1M context: direct OpenAI Responses API inference, safe Astra/Sol/Terra/Luna input headroom, Keychain delivery, and Mac fleet rollout.

64

Quality

80%

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SecuritybySnyk

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tessl review fix ./skills/codex-huge-context/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

81%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.

The body is an exceptionally actionable, well-validated runbook: exact configs, scripts, verification commands with expected outputs, and a thorough failure policy. Its weaknesses are repetition of the split-configuration invariant, inline time-stamped observations, and fleet/autoreview operational state that should be split into reference files.

Suggestions

Move the fleet host inventory (per-host identity, checkout, and pending-work state) into a dedicated reference file, keeping only the rollout procedure and per-host result checklist in SKILL.md.

State the split-configuration invariant once (e.g., in the "Atomic provider" section) and have the "Required files" and "Failure policy" sections reference it instead of restating it.

Quarantine version- and date-stamped observations (Codex 0.144.6/0.162 behavior, 2026-10-12 latency measurements) in a versioned notes or "old patterns" section so they don't age the core instructions.

Reference preflight.rb with a bundle-relative path alongside the deployment path so the skill works from the bundle itself.

DimensionReasoningScore

Conciseness

The body is dense, expert-level operational detail with no padding of concepts Claude already knows, but the split-configuration invariant is restated across the "Atomic provider" section, the "Required files" section, and the "Failure policy", and time-stamped/versioned observations ("Codex 0.144.6 checks", "measured 2026-10-12", "Peter, 2026-10-12") sit inline rather than in a quarantined section. Not 2 because nothing is padded fluff; not 4 because more than minor tightening is possible.

3 / 5

Actionability

Fully executable throughout: exact TOML config blocks, exact JSON catalogue values, a complete copy-paste zsh auth wrapper, verification commands with expected outputs ("475000 for the base... 875900 for astra-1m"), and per-error remediation steps. Matches the anchor for copy-paste ready commands covering the common cases.

5 / 5

Workflow Clarity

Multi-step processes are clearly sequenced with explicit validation: the preflight gates every launch/resume, the app-server restart sequence is numbered, the fleet rollout requires auditing all hosts before mutating one at a time, and the failure policy provides feedback loops for each error mode. Validation is present for the batch fleet operation, so the workflow-clarity cap does not apply.

5 / 5

Progressive Disclosure

Good section structure with clear headers and a profile table, and the complex validation logic is properly externalized into a real scripts/preflight.rb that implements the exact 922000/700000 contract and profile set the body documents. Not 5 because the ~90 lines of fleet host inventory and managed-autoreview operational state are time-sensitive content that belongs in reference files, and the script is referenced only via its absolute deployment path rather than a bundle-relative link; not 3 because structure is good and script references are clearly signaled.

4 / 5

Total

17

/

20

Passed

Description

70%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.

The description is specific and distinctive, naming concrete technologies and a clear niche, but it omits any "Use when..." trigger clause, which limits discoverability and caps completeness. It also references Terra/Luna models that the skill body does not cover, a minor accuracy gap.

Suggestions

Add an explicit trigger clause, e.g. "Use when configuring, repairing, or auditing Codex's 1M context setup, direct OpenAI API inference, or its fleet rollout."

Align model names with the skill body (Astra/Sol only) or remove Terra/Luna if the skill does not actually handle them.

Include natural trigger variations users would say, such as "context window", "million-token context", and "compaction threshold".

DimensionReasoningScore

Specificity

"direct OpenAI Responses API inference, safe Astra/Sol/Terra/Luna input headroom, Keychain delivery, and Mac fleet rollout" lists several concrete, specific capabilities rather than vague language. Not 5 because it enumerates components rather than comprehensive actions, and it names Terra/Luna models the skill body does not actually cover.

4 / 5

Completeness

The "what" is clear (direct API inference route, input headroom, Keychain delivery, fleet rollout), but there is no "Use when..." clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric guideline. Not 4 because the "when" is entirely absent, not merely imprecise.

3 / 5

Trigger Term Quality

Natural terms a user would say are present: "Codex", "1M context", "OpenAI Responses API", "Keychain", "Mac fleet rollout". Not 5 because common variations like "context window", "million tokens", or "compaction" are missing.

4 / 5

Distinctiveness Conflict Risk

"Codex 1M context" combined with direct Responses API routing, Keychain delivery, and Mac fleet rollout is a clear niche with distinct triggers and minimal conflict risk with other skills. Not 4 because the specific combination is unique enough that overlap risk is negligible.

5 / 5

Total

16

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

steipete/agent-scripts

Path

skills/codex-huge-context/SKILL.md

Reviewed

Table of Contents

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