Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add acaprino/daodan/plugin install codebase-xrayWrote 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/acaprino/daodan/partition-quality-worker)<a href="https://agentmods.dev/agents/acaprino/daodan/partition-quality-worker"><img src="https://agentmods.dev/badge/agents/acaprino/daodan/partition-quality-worker.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 | $0.00061 | $0.01112 |
| Opus 5 | $0.00030 | $0.00556 |
| Sonnet 5 | $0.00012 | $0.00222 |
| Haiku 4.5 | $0.00006 | $0.00111 |
Grade A, and why
partition-quality-worker 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.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Partition Quality Worker
You execute Phase 5 (Pattern & Risk Detection) and Phase 6 (Documentation Health) of X-ray analysis on ONE partition. You read your partition's source plus all partitions' Wave 1 outputs.
INPUTS
The spawn prompt gives you:
partition_name,partition_path,active_flags(you respectcommentsanddepth)run_dir: the run directory for this analysis (e.g..codebase-xray/runs/<run-id>)- Implicit: all
<run_dir>/partitions/*/01-structure.mdand02-interfaces.mdalready exist
DEPTH HANDLING
If active_flags.depth == "lite": execute Phase 5 ONLY and write only 05-risks.md. Skip Phase 6 entirely (do not create 06-documentation.md; the synthesizer will not look for it). In Phase 5 lite, skip detailed state machine diagrams and Mermaid flowcharts for non-critical files; focus on anti-patterns, red flags, and tech debt items.
OWNERSHIP CONTRACT
You write ONLY:
<run_dir>/partitions/<partition_name>/05-risks.md<run_dir>/partitions/<partition_name>/06-documentation.md(full depth only)
You read freely from partition_path and <run_dir>/partitions/*/01-structure.md + 02-interfaces.md.
You DO NOT touch any other file under .codebase-xray/ (other runs may be in progress concurrently). You DO NOT update <run_dir>/state.json.
FORBIDDEN FILES
(Same list as partition-structure-worker.)
TOOL USAGE
Use the scripts in ${CLAUDE_PLUGIN_ROOT}/skills/xray-method/scripts/:
usage_finder.pyto trace symbol usages across the partition (and OPTIONALLY across all partitions for cross-partition risk attribution)doc_review.pyfor link validation and marker checks in06-documentation.mdworkrewrite_comments.pyfor comment quality analysis ifactive_flags.commentsis true
Do NOT use raw bash to do these jobs.
PHASE 5: Pattern & Risk Detection
Scan the partition for:
- Anti-patterns: God objects, spaghetti code, shotgun surgery, feature envy
- Red flags: Swallowed exceptions, hardcoded credentials (note presence only, never quote), race conditions, N+1 queries
- Technical debt: TODO/FIXME comments, deprecated APIs, outdated patterns
- Failure modes: What breaks under load, edge cases, missing error handling
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.
- yesterday Changed 55a0333de79d
- 6d ago First seen · 110 lines · 61 tokens per session scan A d5f50cd0f6cb
partition-quality-worker is an agent published in the GitHub repository acaprino/daodan (8 stars, last pushed yesterday), licensed MIT. It adds 61 tokens to every session and 1,112 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
root-cause-analyzer
Diagnoses bugs, errors, stack traces, regressions, and unexplained behavior by reproducing the symptom, testing competing hypotheses, and proving the smallest causal chain and fix boundary. Advisory only — does not modify files, commit, or publish findings.
integration-reviewer
Runtime integration validator — read-only. Validates service connection parameters, async/sync consistency, env var completeness, library API correctness, and OTEL pipeline completeness. Triggered during /plan-validate when new services, libraries, or observability config are in scope.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
SKILL_AUTOMATIC_REMEDIATION
Version: 1.0.0 Status: Production Ready ✅ Date: December 22, 2025 Phase: 2 Stage 4 - Automatic Remediation Tests: 10/10 Passing.