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/001TMF/blatant-whyWrote 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/001tmf/blatant-why/plan-campaign)<a href="https://agentmods.dev/commands/001tmf/blatant-why/plan-campaign"><img src="https://agentmods.dev/badge/commands/001tmf/blatant-why/plan-campaign/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/001tmf/blatant-why/plan-campaign"><img src="https://agentmods.dev/badge/commands/001tmf/blatant-why/plan-campaign.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 | $0.00016 | $0.04159 |
| Opus 5.5 | $0.00006 | $0.01664 |
| Sonnet 5.5 | $0.00003 | $0.00832 |
| Haiku 4.5 | $0.00002 | $0.00416 |
Grade A, and why
by:plan-campaign scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 396 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/plan-campaign — Pre-Campaign Discussion
Capture design preferences through a focused discussion before launching a campaign. This ensures the right modality, epitope strategy, compute tier, scaffolds, and success criteria are locked in before any compute runs.
Instructions
Step 0: Read model profile
MODEL_PROFILE=$(cat .by/config.json 2>/dev/null | grep -o '"model_profile"[[:space:]]*:[[:space:]]*"[^"]*"' | grep -o '"[^"]*"$' | tr -d '"' || echo "balanced")
Model lookup for this command (runs in main session, no agent spawn):
| Agent | quality | balanced | budget |
|---|---|---|---|
| main session | opus | sonnet | sonnet |
Model lookup for research agents (spawned via Task):
| Agent | quality | balanced | budget |
|---|---|---|---|
| researchers (x4) | opus | sonnet | haiku |
| synthesizer (x1) | opus | sonnet | sonnet |
Step 1: Parse target input
Determine the input type from the user's argument:
- PDB ID: 4-character alphanumeric (e.g.,
1ABC,7XYZ) - UniProt accession: alphanumeric with pattern like
P12345orQ9UHD2 - Free text: treat as a target name or description for search
Record the parsed target identifier for use in Step 2.
Step 2: Quick target lookup (MUST use Agent tool — keeps MCP calls hidden)
You MUST use the Agent tool to research the target. This is NOT optional. When you call MCP tools directly, Claude Code shows raw JSON to the user which looks terrible. The Agent tool runs in background and returns only the summary.
DO THIS:
Agent(
prompt="Research the protein target '[target]'.
1. Call mcp__by-uniprot__uniprot_search with query '[target] human' to get accession, name, length
2. Call mcp__by-pdb__pdb_search with query '[target]' to get PDB structures
3. Call mcp__by-sabdab__sabdab_search_by_antigen with antigen_name '[target]' for known binders
Return ONLY this exact format (no JSON, no tool output, just this text):
Target: [full name] ([organism]) — [length] aa | UniProt: [accession]
Structures: [N] PDB entries (best: [PDB ID] at [resolution]Å)
Known binders: [N] antibodies/nanobodies in SAbDab
",
description="Research [target]"
)
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 · 396 lines · 16 tokens per session scan A d0d5ab89749f
by:plan-campaign is a command published in the GitHub repository 001TMF/blatant-why (119 stars, last pushed 8d ago), licensed MIT. It adds 16 tokens to every session and 4,159 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 2026-08-30.
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.
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.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.
speckit.bug.test
Validate that a previously fixed bug is resolved and record the verification report.