Summarizer skills
Skill for turning source text into faithful, machine-readable JSON summaries.
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Produce faithful, information-dense summaries from raw text and return them as structured JSON for both human and agent consumption. Use when Codex needs to summarize articles, documentation, code explanations, transcripts, notes, or similar text into a consistent schema with a short summary, key points, entities, actionable insights, and a confidence score.
SKILL.md
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Summarizer
Summarize source text into a compact JSON object that is easy for people to read and reliable for downstream agents to parse. Favor factual compression, explicit uncertainty, and consistent field behavior over stylistic flourish.
Output Contract
Return exactly one JSON object matching the schema in references/output-contract.md.
Apply these rules every time:
- Use only information supported by the source.
- Keep
summaryat 150 words or fewer. - Return
key_pointswith 5 to 10 items unless the source is too short to support that many; in that case, return the maximum faithful count. - Return
actionable_insightsonly when the source contains actions, recommendations, decisions, risks, or next steps that matter. - Set
titletonullwhen the source does not provide or strongly imply one. - Keep
entitiesto concrete named items or stable concepts that improve retrieval or follow-on reasoning. - Set
confidencebased on source clarity, completeness, and ambiguity, not on writing quality.
Workflow
- Read the full source before compressing it.
- Identify the document type: technical, narrative, or mixed.
- Extract the central claim, supporting facts, key entities, and any decisions or actions.
- Remove repetition, examples that do not change meaning, and low-signal filler.
- Write the
summaryas a dense paragraph that preserves the source's actual claims. - Fill the remaining fields with short, non-overlapping items.
- Run the self-check in references/output-contract.md before returning the JSON.
Adaptation Rules
Technical Input
- Preserve original terminology, component names, APIs, constraints, and causal structure.
- Keep implementation details that affect behavior, safety, or compatibility.
- Prefer precise nouns and verbs over simplification that changes meaning.
- Surface decisions, tradeoffs, prerequisites, failures, and next actions in
actionable_insightswhen present.
Narrative Input
- Emphasize what happened, why it matters, and the main takeaways.
- Keep chronology only when it changes interpretation.
- Extract the people, organizations, places, or themes that anchor the story.
- Do not force action items when the source is descriptive only.
Field Guidance
summary
Write one compact paragraph. Lead with the main point, then include the most decision-relevant supporting facts. Do not copy long phrases from the source unless exact wording is required for accuracy.
key_points
Use short statements with one idea each. Prefer coverage over redundancy. Include the facts another agent would need to reason about the source without rereading it.
entities
Include named tools, systems, products, standards, organizations, people, or recurring domain concepts. Omit generic words that do not improve retrieval.
actionable_insights
Include only items that a reader could act on or use to make a decision. Good candidates:
- explicit recommendations
- unresolved risks or blockers
- next steps
- decisions already made
- operational implications
Use an empty array when no actionable insight is present.
confidence
Use a bounded estimate:
0.9-1.0: source is clear, direct, and internally consistent0.7-0.89: source is mostly clear with limited ambiguity0.4-0.69: source is partial, noisy, or somewhat ambiguous0.0-0.39: source is fragmentary, contradictory, or hard to interpret
Lower confidence when the source is incomplete, transcript-like, highly inferential, or missing context needed to connect the main points.
Failure Modes To Avoid
- inventing missing facts, motives, or outcomes
- repeating the same idea across
summaryand multiple list fields - overfilling
entitieswith generic terms - turning descriptive text into fake action items
- flattening technical distinctions that change meaning
- exceeding the schema or word-count constraints