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 skills add rp1-run/rp1 --skill code-investigategit clone --depth 1 https://github.com/rp1-run/rp1Wrote 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/rp1-run/rp1/code-investigate)<a href="https://agentmods.dev/skills/rp1-run/rp1/code-investigate"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/code-investigate/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.
<a href="https://agentmods.dev/skills/rp1-run/rp1/code-investigate"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/code-investigate.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00031 | $0.00831 |
| Opus 5.5 | $0.00012 | $0.00332 |
| Sonnet 5.5 | $0.00006 | $0.00166 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
code-investigate 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 1mo 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Root Cause Investigator
This command invokes the bug-investigator sub-agent for systematic issue analysis of the provided problem statement.
Before invoking the sub-agent or registering artifacts, derive EFFECTIVE_ISSUE_ID:
- If
ISSUE_IDis non-empty, use it unchanged. - Otherwise derive a deterministic lowercase kebab-case slug from
PROBLEM_STATEMENT. - Collapse repeated separators, trim leading/trailing
-, and if the slug would be empty useinvestigation.
Use EFFECTIVE_ISSUE_ID consistently for workspace paths, report paths, and agent inputs.
First emit: Generate RUN_ID as a UUID. Derive RUN_NAME from the problem statement: a brief summary (max 60 chars) prefixed with "Investigate: ".
On session start, emit the status change:
rp1 agent-tools emit \
--workflow code-investigate \
--type status_change \
--run-id {RUN_ID} \
--name "Investigate: {brief summary}" \
--step investigating \
--data '{"status": "running"}'
STATE-MACHINE
stateDiagram-v2
[*] --> investigating
investigating --> [*] : done
Invoke the bug-investigator agent with the freeform problem statement and derived issue id:
{% dispatch_agent "rp1-dev:bug-investigator" %} PROBLEM_STATEMENT={PROBLEM_STATEMENT}, ISSUE_ID={EFFECTIVE_ISSUE_ID}, KB_ROOT={kbRoot}, WORK_ROOT={workRoot} {% enddispatch_agent %}
The agent will:
- Analyze problem statement and gather context
- Form and test hypotheses systematically
- Add temporary debugging (tracked and reverted)
- Trace code execution paths
- Identify root cause with evidence
- Generate comprehensive investigation report
- Propose solutions with effort estimates
- Report back with findings
Emit btw_update events for key findings during the investigation:
rp1 agent-tools emit \
--workflow code-investigate \
--type btw_update \
--run-id {RUN_ID} \
--data '{"message": "{finding summary}"}'
After the agent writes the investigation report, register it as an artifact:
rp1 agent-tools emit \
--workflow code-investigate \
--type artifact_registered \
--run-id {RUN_ID} \
--step investigating \
--data '{"path": "issues/{EFFECTIVE_ISSUE_ID}/investigation_report.md", "storageRoot": "work_dir", "format": "markdown"}'
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.
- 1mo ago First seen · 108 lines · 31 tokens per session scan A d0c90d07f08d
code-investigate is a skill published in the GitHub repository rp1-run/rp1 (38 stars, last pushed today), licensed Apache-2.0. It adds 31 tokens to every session and 831 once invoked, about $0.0001 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 2026-09-07.
Other skills, from other repositories
oracle-debug
Disciplined debugging methodology. Triggers on bug reports, test failures, "debug this", "diagnose this", unexpected behavior, build failures, integration issues, or performance regressions. Find root cause before a permanent corrective fix; contain urgent harm safely first.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production, security-sensitive logic, irreversible operations), or any time a confident output would be cheaper to verify now…
audit
Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.
debugging-and-error-recovery
Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.
database-optimizer
Optimizes database queries and improves performance across PostgreSQL and MySQL systems. Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.
ai-slop-cleaner
Clean AI-generated code slop with a regression-safe, deletion-first workflow and optional reviewer-only mode.