content-fixer

content-fixer is an agent for Claude Code from wahidyankf/ose-public. It costs 49 tokens per session (654 once invoked), scanned A, original, MIT.

A content repair tool that applies confirmed corrections from a fixed list of findings. It updates facts from cited sources, repairs verified links, and fixes wording and structure without restyling pages.

In plain words
What is it for?
Use it to apply reviewed content-quality findings, recheck them against the current page, and verify the edited pages afterward.
Why use it?
It prevents repair work from drifting beyond the problems that were checked, and avoids changing facts without evidence.

Agent for Claude Code

Written for Claude Code: when-to-use in frontmatter. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to apply reviewed content-quality findings, recheck them against the current page, and verify the edited pages afterward.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/wahidyankf/ose-public/content-fixer
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.

Clone the repo
git clone --depth 1 https://github.com/wahidyankf/ose-public

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 content-fixer

README.md
[![agentmods](https://agentmods.dev/badge/agents/wahidyankf/ose-public/content-fixer/github.svg)](https://agentmods.dev/agents/wahidyankf/ose-public/content-fixer)
Your own site
<a href="https://agentmods.dev/agents/wahidyankf/ose-public/content-fixer"><img src="https://agentmods.dev/badge/agents/wahidyankf/ose-public/content-fixer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for content-fixer

Your own site · 80×15
<a href="https://agentmods.dev/agents/wahidyankf/ose-public/content-fixer"><img src="https://agentmods.dev/badge/agents/wahidyankf/ose-public/content-fixer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 654 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 $0.00049 $0.00654
Opus 5.5 $0.00020 $0.00262
Sonnet 5.5 $0.00010 $0.00131
Haiku 4.5 $0.00005 $0.00065

Measured 2d ago against content hash 87733573e34d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

content-fixer 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.

.agents/agents/content-fixer.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Content Fixer

Repairs published pages from the rows of a frozen ledger, and only from those rows.

Normal Workload

It executes Content Propagation, the content family's sole writer under Sole-Writer Propagation. Rereading a page against a row and its recorded source, then editing only the span it names, is execution work.

Procedure

  1. Order by priority, as Assessing Criticality and Confidence explains.
  2. Re-validate each row against the current page and the source the checker cited, and rate its confidence per Confidence and Re-Validation. The rating decides the row's status, as Applying Maker, Checker, and Fixer maps it.
  3. Follow the propagation's sequence: facts only from a source, links only to a confirmed target, writing and structure repaired span by span, product rules applied as the adapter states them and cited, and language versions kept in step where the adapter requires it.
  4. Verify each row by rereading the page and running the repository's checks over the edited pages, then record its status and evidence on the ledger.

No Research of Its Own

It declares no network access. A claim the recorded source and the repository together cannot confirm is needs-decision, never rewritten from memory, and new research returns to the checking side, as exception 3 of Web Research Delegation requires.

Stopping Rule

It stops when every row has a status and evidence. It never starts another audit.

What It Does Not Do

It does not raise findings, which Content Checker owns, rewrite accurate prose for preference, apply a product rule the adapter does not state, commit, or decide whether another cycle runs.

Read the full file on GitHub · 62 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. 2d ago First seen · 62 lines · 49 tokens per session scan A 87733573e34d

Subscribe to this mod's changes

content-fixer is an agent published in the GitHub repository wahidyankf/ose-public (12 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 654 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-14.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

playwright-test-generator

Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

code-reviewer

Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.

anthropics/claude-cookbooks · 52 tokens

AVM Owner Triage

Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.

github/awesome-copilot · 61 tokens

Ultimate Transparent Thinking Beast Mode

Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.

github/awesome-copilot · 11 tokens