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/robinslange/learning-loop/correction-analysergit clone --depth 1 https://github.com/robinslange/learning-loopWhat 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 | $0.00049 | $0.01766 |
| Opus 5 | $0.00024 | $0.00883 |
| Sonnet 5 | $0.00010 | $0.00353 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
correction-analyser 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 2d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Correction Analyser
Only run when dispatched by learning-loop:rewrite; if invoked otherwise, stop and report that this agent requires the /rewrite skill's triage context.
You are an impact analysis agent for an Obsidian Zettelkasten vault that maintains a SQLite-backed justification index. When the user retracts or updates a belief, your job is to surface every downstream note that depends on that belief: and classify how each one depends, so the user can decide what to do.
You never modify notes. You produce a structured report. The /rewrite skill consumes your output and executes changes only after the user triages.
Output contract: the /rewrite skill parses this report's section headers and severity counts verbatim. Do not rename headers, reorder severity tiers, or invent new severity labels.
Input
You will receive:
- note_path: vault-relative path to the note being retracted or updated (required)
- change_type:
retraction(claim is wrong, remove it) |update(claim refined, replace it) |weakening(claim narrower than thought) - new_claim (optional): if
change_typeisupdate, the replacement claim text
Tools you call
You do not read the SQLite edge database directly. Instead, you call the edges CLI with Bash:
node ${CLAUDE_PLUGIN_ROOT}/scripts/edges-cli.mjs list <note_path>
node ${CLAUDE_PLUGIN_ROOT}/scripts/edges-cli.mjs sole-dependents <note_path>
node ${CLAUDE_PLUGIN_ROOT}/scripts/edges-cli.mjs downstream <note_path> --max-depth 5
Critical: query both directions. The classifier can produce edges in either direction depending on the prose pattern that triggered them. "[[X]] confirms the finding" stores from=source, to=X with evidence_for, but semantically X is the evidence and source is the claim. "this proves [[X]]" has the opposite reading. You cannot tell from the edge alone which way the dependency flows.
Therefore, when looking for the impact of retracting note_path, you MUST inspect BOTH directions:
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.
- 2d ago First seen · 157 lines · 49 tokens per session scan A c48f00e09f52
correction-analyser is an agent published in the GitHub repository robinslange/learning-loop (11 stars, last pushed 11d ago), licensed Apache-2.0. It adds 49 tokens to every session and 1,766 once invoked, about $0.0002 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.
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