thorough-digest

thorough-digest is a skill for Claude Code, Codex from specter119/skills. It costs 58 tokens per session (487 once invoked), scanned A, original, MIT.

A skill for processing every item in a collection of local files and combining the findings into one clear account.

In plain words
What is it for?
Batch-reviewing local files, extracting points from each one, and synthesizing the results into a narrative.
Why use it?
It reduces the chance that files or sections are skipped when reviewing a folder or other large set of materials.

Skill for Claude CodeCodex

Written for Claude Code and Codex: allowed-tools in frontmatter, but also agents/openai.yaml present. Also seen: mentions subagents.

Part of the skills plugin — 10 skills shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/specter119/skills/thorough-digest
Any agent
npx skills add specter119/skills --skill thorough-digest
Clone the repo
git clone --depth 1 https://github.com/specter119/skills

Made for: Claude Code, Codex.

Or install skills, the plugin that ships this one along with the rest of its 10 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for thorough-digest

README.md
[![agentmods](https://agentmods.dev/badge/skills/specter119/skills/thorough-digest.svg)](https://agentmods.dev/skills/specter119/skills/thorough-digest)
Your own site
<a href="https://agentmods.dev/skills/specter119/skills/thorough-digest"><img src="https://agentmods.dev/badge/skills/specter119/skills/thorough-digest.svg" alt="Measured on agentmods" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 487 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00058 $0.00487
Opus 5 $0.00029 $0.00244
Sonnet 5 $0.00012 $0.00097
Haiku 4.5 $0.00006 $0.00049

Measured 5d ago against content hash 85d57326745b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

thorough-digest scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/render_docx.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/thorough-digest/SKILL.md · 51 lines

What it actually says

Thorough Digest

Responsible for exhaustive processing and parallel synthesis of local materials; does not handle external web research.

USE FOR

  • Batch processing of local files, directories, sections, or material collections
  • Item-by-item extraction of key points with no items allowed to be skipped
  • Building a unified narrative or comprehensive report from existing materials

DO NOT USE FOR

  • Requires fetching new information from the web
  • Only processing one or two documents
  • The primary goal is writing slides or a formal report rather than first digesting materials

Execution Skeleton

  1. First inventory all input items to ensure the scope is clear.
  2. Follow workflow to determine grouping and parallelization strategy.
  3. Give each sub-agent an explicit item list, output path, and the constraint that no items may be skipped.
  4. Aggregate findings from each group; if needed, chain an external research skill to fill gaps.

Reference Map

Output Contract

  • Default deliverables: inventory, grouping results, per-group findings, and final synthesis
  • Must be able to report total input count, processed count, and coverage status

Collaboration and Handoff

  • If the synthesis phase reveals critical fact gaps, hand off to an external research skill
  • If materials are fully digested and the next step is writing a formal deliverable, hand off to a writing or presentation-structure skill
Files

What ships with it

8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 51 lines · 58 tokens per session scan A 85d57326745b

Subscribe to this mod's changes

thorough-digest is a skill published in the GitHub repository specter119/skills (2 stars, last pushed 29d ago), licensed MIT. It adds 58 tokens to every session and 487 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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