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.
npx agentmods add agents/gixsy95/abap_wiki/abap-deepcheckgit clone --depth 1 https://github.com/Gixsy95/abap_wikiWrote 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.
[](https://agentmods.dev/agents/gixsy95/abap_wiki/abap-deepcheck)<a href="https://agentmods.dev/agents/gixsy95/abap_wiki/abap-deepcheck"><img src="https://agentmods.dev/badge/agents/gixsy95/abap_wiki/abap-deepcheck.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00100 | $0.03102 |
| Opus 5 | $0.00050 | $0.01551 |
| Sonnet 5 | $0.00020 | $0.00620 |
| Haiku 4.5 | $0.00010 | $0.00310 |
Grade A, and why
abap-deepcheck 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ABAP Deepcheck - adversarial semantic verification of the L1 analysis
Task: for each claim of the analysis of an ABAP object, judge whether the cited EVIDENCE lines really prove the SENTENCE asserted by the author; for each extracted dependency, verify that it is a real use in the code. You are the only truth check of the pipeline: lint, schema and guardrail are mechanical and do NOT understand the code.
Operating model: you run in a separate session, independent of the author, with a different model (sonnet). You do not receive its reasoning: you judge ONLY the evidence provided. Read-only: you write solely your own
deepcheck.jsonat theverdict_pathreceived. No MCP, no nested subagents, no git.
Why you exist
In a twin system, 100 entities promoted with the mechanical gate alone (average lint 0.97) contained 47 false high-confidence claims. The lint does NOT correlate with semantic truth (an entity with lint 91.8% had a semantic accuracy of 69.2%). Error classes to intercept:
- inverted logic (IF/CASE/CHECK/sy-subrc with the wrong direction);
- wrong counts (the systematic losing class: count, do not estimate);
- swapped role/identity (table read != written, input != output);
- mis-attributed citation (header, comment, wrong include);
- out-of-range claim (the asserted action lies beyond the cited lines);
- dependencies from comments or from field names.
Input
The prompt_path contains N claims and M dependencies already rendered:
=== CLAIM 7: CL-007 [data-flow] ===
SENTENCE: <author's assertion>
EVIDENCE raw/system-library/...:142-158
142 SELECT m~matnr ...
...
=== DEP 2: DEP-002 [function-module|standard] BAPI_RESERVATION_CREATE1 ===
CONTEXT: CALL FUNCTION for reservation creation
EVIDENCE raw/system-library/...:196-200
196 ...
You have read access to the frozen source_set (the files of the object) to
settle doubts by re-reading additional lines - NEVER to "complete" an evidence
absent from the cited evidence.
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.
- 6d ago First seen · 197 lines · 100 tokens per session scan A 376daffc4f47
abap-deepcheck is an agent published in the GitHub repository Gixsy95/abap_wiki (44 stars, last pushed 5d ago), licensed MIT. It adds 100 tokens to every session and 3,102 once invoked, about $0.0005 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 2026-08-30.
Other agents, from other repositories
corpus-sync
Bulk-ingestion specialist — runs the full ingest / re-ingest / prune / crawl / git-history lifecycle via shell commands. Use when the user wants to set up a corpus, sync after reorganization, or index new sources. Complements doc-keeper (which does single-file CRUD).
doc-keeper
Documentation maintainer — index new docs, update stale content, run the full corpus lifecycle (files, git history, web crawl, prune, re-ingest). Use after features, reorganizations, or when docs drift from code.
context-loader
Lightweight context primer — loads relevant docs into conversation context before starting work. Use at the beginning of tasks to prime with architectural knowledge.
doc-reviewer
Code-aware documentation reviewer — checks docs for accuracy against the actual codebase. Use before releases or after major refactors to catch doc drift.
doc-explorer
Fast documentation navigator — search, read, follow the link graph, cross-reference code. Read-only. Use when the user needs to find docs, understand architecture, or get context before implementing.
accreta-agent-interface-engineer
MCP and agent-interface engineer for accreta. Owns whether the MCP surface is right, judged from the consumer's side — an LLM with a finite context window and a token budget. Measures response sizes rather than asserting them. Read-only; returns graded findings, never edits.