debug-rule

A debugging workflow for taint-analysis rules. Taint analysis tracks data from a possibly unsafe source through a program to see whether it reaches a sensitive operation.

In plain words
What is it for?
It is for investigating rule samples that fail repeatedly or rules that pass tests but produce incorrect results on real scans.
Why use it?
It helps identify where an expected data flow disappears and whether the problem is in the rule, a missing library model, or the analysis engine.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/seqra/opentaint/debug-rule
Any agent
npx skills add seqra/opentaint --skill debug-rule
Clone the repo
git clone --depth 1 https://github.com/seqra/opentaint

Made for: Claude Code, Codex.

Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,194 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00040 $0.01194
Opus 5 $0.00020 $0.00597
Sonnet 5 $0.00008 $0.00239
Haiku 4.5 $0.00004 $0.00119

Measured yesterday against content hash cf2ce90c8012, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

debug-rule 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 yesterday.

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.

skills/debug-rule/SKILL.md · 66 lines

How it starts

The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: Debug Rule

Diagnose why a rule or approximation behaves unexpectedly on a model by tracing where taint is dropped, and decide who owns the fix: the rule, a missing library model, or the engine.

Inputs

Provided by the caller, fall back to the default value when omitted. Ask back only when a required input is missing and has no sensible default

  • project-root (optional) — root of the target project. Opentaint keeps all analysis artifacts under the fixed <project-root>/.opentaint/ directory, so every .opentaint/... path below resolves there. Default: current directory
  • rule (required) — the one rule whose sample or flow routes taint through the code under test, as <ruleSetRelativePath>.yaml:<shortId>. For an approximation, the rule whose sample routes taint through the approximated method
  • model (required) — the project model where the behavior shows up

Workflow

1. Reproduce and localize the kill

Reproduce the exact run that showed the problem — same model, rulesets, and applied approximation dirs — and trace where taint dies with a fact-reachability run. Run it directly as a foreground, blocking command and wait for exit — never background it or use Monitor:

opentaint test rule reachability <rule> \
  --project-model <model> \
  -o <results-dir>/report.sarif \
  --ruleset builtin --ruleset .opentaint/rules \
  --passthrough-approximations .opentaint/pass-through \
  --dataflow-approximations .opentaint/dataflow

<results-dir> is .opentaint/test-results/<name> for a test model, .opentaint/results for the main scan. The per-instruction facts are in the sibling <results-dir>/debug-ifds-fact-reachability.sarif, not the -o file — the -o SARIF only shows whether the rule fired. Read that sibling to find the kill:

  • a missed detection (a positive that won't pass, or a flow absent from a scan) — confirm a fact exists at the source; if none, the gap is in pattern-sources, not the flow. Otherwise walk the facts to the last instruction still carrying it and the first where it's gone — that gap is the kill
  • a spurious detection (a negative that fires) — the reverse: find where a fact appears with no tainted input reaching it

Read the full file on GitHub · 66 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. yesterday First seen · 66 lines · 40 tokens per session scan A cf2ce90c8012

Subscribe to this mod's changes

debug-rule is a skill published in the GitHub repository seqra/opentaint (149 stars, last pushed 2d ago), licensed Apache-2.0. It adds 40 tokens to every session and 1,194 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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