dogma: Skill for Claude Code

.github/skills/corpus-sweep/SKILL.md

corpus-sweep is a skill for Claude Code from EndogenAI/dogma. It costs 78 tokens per session (1,073 once invoked), scanned A, original, Apache-2.0.

A process for scanning at least 20 research papers, documents, or agent files in batches to find patterns and produce a short findings summary.

In plain words
What is it for?
Use it to compare many existing documents, inspect a research folder, or find recurring patterns across an agent fleet before synthesis.
Why use it?
It reduces the effort of reading a large collection when you need an overview before making a decision or combining findings.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions AGENTS.md.

This is EndogenAI/dogma's own configuration. It tells Claude Code how to work on dogma itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything dogma configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is uv run python scripts/fetch_all_sources.py.

Reuse

Borrowing it

Nothing to install: this file belongs to EndogenAI/dogma. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/EndogenAI/dogma/main/.github/skills/corpus-sweep/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/EndogenAI/dogma

Made for: Claude Code.

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 corpus-sweep

README.md
[![agentmods](https://agentmods.dev/badge/skills/endogenai/dogma/corpus-sweep.svg)](https://agentmods.dev/skills/endogenai/dogma/corpus-sweep)
Your own site
<a href="https://agentmods.dev/skills/endogenai/dogma/corpus-sweep"><img src="https://agentmods.dev/badge/skills/endogenai/dogma/corpus-sweep.svg" alt="Measured on agentmods" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,073 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00078 $0.01073
Opus 5 $0.00039 $0.00536
Sonnet 5 $0.00016 $0.00215
Haiku 4.5 $0.00008 $0.00107

Measured 8d ago against content hash 55451dc085df, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

corpus-sweep 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 8d ago.

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.

.github/skills/corpus-sweep/SKILL.md · 120 lines

How it starts

The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Corpus Sweep

Governing Axiom

This skill enacts the Endogenous-First axiom from MANIFESTO.md and is governed by AGENTS.md § Programmatic-First Principle. Before sweeping any corpus, check whether a prior sweep result is already cached in .cache/sources/ or recorded in the active session scratchpad — re-sweeping a cached corpus wastes tokens and violates Local Compute-First.

"Scaffold from existing system knowledge and external best practices." — MANIFESTO.md § 1 Endogenous-First


When to Use

  • A synthesis or implementation phase requires patterns found across ≥20 source documents.
  • The Orchestrator needs a cross-cutting summary before delegating to a Synthesizer.
  • A research epic requires an initial landscape report before forming hypotheses.
  • You are auditing agent files, research docs, or session scratchpads for a recurring pattern.

Do not use this skill for:

  • Single-document reads (use read_file directly).
  • Live web fetches (use the source-caching skill instead).
  • Tasks where the required context fits within a single grep_search or semantic_search call.

Workflow

  1. Pre-warm cache — before reading any source file, run:

    uv run python scripts/fetch_all_sources.py
    

    Skip if .cache/sources/manifest.json was updated within the current session.

  2. Read source index — identify the target corpus:

    • For research sources: read .cache/sources/manifest.json to list all cached URLs.
    • For internal docs: list docs/research/, docs/guides/, or .github/agents/ as appropriate.
    • Record the total document count before batching.
  3. Batch-read docs — divide the corpus into batches of 5–10 files. For each batch:

    • Use read_file (for long docs) or grep_search (for keyword scanning).
    • Record findings per document: filename, key claim, relevant quote (≤50 words), and cross-references noted.
    • Stop scanning a document once sufficient signal is found — avoid reading full content when a heading scan suffices.

Read the full file on GitHub · 120 lines

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. 8d ago First seen · 120 lines · 78 tokens per session scan A 55451dc085df

Subscribe to this mod's changes

corpus-sweep is a skill published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 14d ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,073 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

air-blackbox-sales-agent

AIR Blackbox's autonomous sales prospecting agent. Finds Python AI projects on GitHub that need EU AI Act compliance, identifies the right person to contact (CEO, CTO, lead maintainer), runs a free compliance scan, and drafts personalized outreach emails that convert to engagement. The sales flow: free scan as the…

airblackbox/airblackbox · 248 tokens

interpret-results

Interprets AIR Blackbox scan results and maps findings to specific EU AI Act articles, recitals, and remediation steps. Use when the user has scan output and wants to understand what to fix, why it matters, or how to prioritize.

airblackbox/airblackbox · 49 tokens

compliance-scan

Scans a Python AI project for EU AI Act compliance gaps using AIR Blackbox. Use when the user asks to check compliance, scan their code, audit their AI project, or mentions EU AI Act, Articles 9-15, or compliance checking.

airblackbox/airblackbox · 51 tokens

nio-external-score

Nio external scoring-endpoint snapshot. Use when the user asks about their current Nio/risk scores from configured external scoring endpoints — e.g. "what's my Nio score", "tell me the nio score", "check my external/guardrail endpoint scores", "what do my scoring services return right now", "list the current risk…

core0-io/nio · 97 tokens

nio-scan

Nio code/skill execution-risk scanner. Use when the user wants to scan a file, repo, directory, or skill for execution risks — e.g. "scan this code for risks", "is this file/plugin dangerous", "check this repo for malicious code", "run nio scan on ". Focused single-purpose skill; for other Nio operations use /nio.

core0-io/nio · 81 tokens

constitutional-ai

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

synthetic-sciences/openscience · 58 tokens