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 skills/causify-ai/helpers/coding.annotate_with_todoainpx skills add causify-ai/helpers --skill coding.annotate_with_todoaigit clone --depth 1 https://github.com/causify-ai/helpersWrote 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/skills/causify-ai/helpers/coding.annotate_with_todoai)<a href="https://agentmods.dev/skills/causify-ai/helpers/coding.annotate_with_todoai"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/coding.annotate_with_todoai.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 | $0.00013 | $0.00404 |
| Opus 5 | $0.00006 | $0.00202 |
| Sonnet 5 | $0.00003 | $0.00081 |
| Haiku 4.5 | $0.00001 | $0.00040 |
Grade A, and why
coding.annotate_with_todoai 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 4d 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.
What it actually says
Goal
- The user will give you a file
<FILE>and you will annotate the file with TODOs based on the violated rules and conventions
Workflow
Step 1: Determine the Rules to Apply
-
If
<FILE>is a Python file use<RULES>.claude/skills/coding.rules.md
-
If
<FILE>is a Python testing file (e.g.,test/test_*.py) use<RULES>.claude/skills/coding.rules.md.claude/skills/testing.rules.md
-
Print the rule files
<RULES>to be used for each file in<FILE> -
Read the rule files
<RULES>carefully
Step 2: Annotate with TODOs
- Annotate all the points in
<FILE>with violations to rules in<RULES>adding a TODO, like# TODO(ai_gp): Apply <rule> before the point with violation - E.g.,
# TODO(ai_gp): Apply "Replace Checking Invariants with `assert_equal` - Do not use multiple `assertIn()` calls to check individual pieces of a string output; instead compare the entire output with `assert_equal()`" self.assertIn("Failed Tests Summary", actual) self.assertIn("test_method1", actual) self.assertIn("test_method2", actual) self.assertIn("Total failing tests: 2", actual)
Step 3: Verification
- Make sure that all the violations of the
<RULES>in<FILE>are caught and commented
Step 4: Ask User to Continue
- Ask the user to commit and then ask to continue
- If the user says to continue run the skill
/coding.todoai_gpon<FILE>
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.
- 4d ago First seen · 47 lines · 13 tokens per session scan A af910bfef8e1
coding.annotate_with_todoai is a skill published in the GitHub repository causify-ai/helpers (145 stars, last pushed today), licensed Apache-2.0. It adds 13 tokens to every session and 404 once invoked, about $0.0001 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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