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.
git clone --depth 1 https://github.com/lckx777/copy-chief-blackWrote 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/commands/lckx777/copy-chief-black/investigate)<a href="https://agentmods.dev/commands/lckx777/copy-chief-black/investigate"><img src="https://agentmods.dev/badge/commands/lckx777/copy-chief-black/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/commands/lckx777/copy-chief-black/investigate"><img src="https://agentmods.dev/badge/commands/lckx777/copy-chief-black/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 | $0.00012 | $0.01917 |
| Opus 5 | $0.00006 | $0.00958 |
| Sonnet 5 | $0.00002 | $0.00383 |
| Haiku 4.5 | $0.00001 | $0.00192 |
Grade A, and why
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 11d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/investigate — Investigative Research Mode
Input: $ARGUMENTS
Structured investigation mode that generates questions and explicitly identifies biases BEFORE starting research. Ensures evidence is gathered for both confirming AND disconfirming hypotheses.
Why This Exists
Standard research suffers from confirmation bias: we search for what confirms what we already believe. This protocol forces EXPLICIT bias declaration and DELIBERATE disconfirming searches before synthesis.
Instructions
When this command is invoked with $ARGUMENTS:
Step 0: Handle Missing Input
If $ARGUMENTS is empty:
Usage: /investigate <topic>
Examples:
/investigate "CoQ10 efficacy for tinnitus"
/investigate "florayla avatar pain points"
/investigate "competitor VSL structure for constipation offers"
/investigate "shame as DRE in health niches"
/investigate "which hook format converts best for supplement VSLs"
/investigate is for STRUCTURED research with explicit bias control.
For VOC collection, use /audience-research-agent instead.
Phase 1: Question Generation (BEFORE Any Searching)
Generate 5-10 investigative questions organized by type. Do NOT search yet — generate questions from prior knowledge first.
Display:
INVESTIGATING: "[topic]"
PHASE 1 — QUESTIONS (before searching)
FACTUAL (what we can measure or verify):
Q1. [question about verifiable data or facts]
Q2. [question about measurable outcomes]
CAUSAL (why things happen):
Q3. [question about root cause or mechanism]
Q4. [question about what drives the outcome]
COMPARATIVE (how things relate):
Q5. [question comparing alternatives or options]
Q6. [question about differences between cases]
COUNTERFACTUAL (challenging assumptions):
Q7. What if the opposite were true?
Q8. What evidence would make me change my conclusion?
BIAS-CHECK (making assumptions explicit):
Q9. What am I assuming before I even start?
Q10. Who benefits from the conclusion I expect to find?
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.
- 11d ago First seen · 237 lines · 12 tokens per session scan A c4883a30612e
investigate is a command published in the GitHub repository lckx777/copy-chief-black (5 stars, last pushed 6mo ago), licensed MIT. It adds 12 tokens to every session and 1,917 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.