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/approve-lab)<a href="https://agentmods.dev/commands/001tmf/blatant-why/approve-lab"><img src="https://agentmods.dev/badge/commands/001tmf/blatant-why/approve-lab.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 | $0.00021 | $0.00638 |
| Opus 5 | $0.00010 | $0.00319 |
| Sonnet 5 | $0.00004 | $0.00128 |
| Haiku 4.5 | $0.00002 | $0.00064 |
Grade A, and why
by:approve-lab scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/approve-lab — Approve Lab Submission (Triple-Gated)
Initiate the lab submission approval process for sending designs to Adaptyv Bio for experimental validation. This is a GATED operation requiring explicit confirmation.
Instructions
Step 0: Read model profile
MODEL_PROFILE=$(cat .by/config.json 2>/dev/null | grep -o '"model_profile"[[:space:]]*:[[:space:]]*"[^"]*"' | grep -o '"[^"]*"$' | tr -d '"' || echo "balanced")
Model lookup for this command:
| Agent | quality | balanced | budget |
|---|---|---|---|
| by-lab | opus | opus | sonnet |
Step 1: Pre-flight checks
Verify before proceeding:
- An active campaign exists with completed screening
- At least 1 design has PASS status in screening results
- Environment has Adaptyv API key configured
- Campaign state is at RANKING phase or later
If any check fails, report the issue and stop. Do NOT proceed.
Step 2: Show submission summary
Display what will be submitted:
- Campaign ID and target name
- Number of designs to submit (PASS status only)
- Top designs with scores (ipSAE, ipTM, p_bind)
- Estimated cost and turnaround time
- Adaptyv Bio endpoint being used
Step 3: Require explicit confirmation
Ask the user to type CONFIRM to proceed.
If the user does not type CONFIRM, abort with: "Lab submission cancelled. No data was sent."
Step 4: Write approval file (Layer 3)
cat > .by/campaigns/$CAMPAIGN_ID/lab/approval.json << EOF
{
"approved": true,
"approved_by": "user",
"approved_at": "$(date -u +%Y-%m-%dT%H:%M:%SZ)",
"ttl_minutes": 5,
"campaign_id": "$CAMPAIGN_ID",
"design_count": $DESIGN_COUNT
}
EOF
Step 5: Update campaign state (Layer 2)
Set labApproved: true in state.json.
Step 6: Spawn by-lab agent
Delegate to a by-lab agent (model per profile table above):
Submit approved designs from
{campaign_dir}to Adaptyv Bio. Use the MCP confirmation code from the adaptyv server (Layer 1). Verify approval.json exists and TTL has not expired (5 minutes). Verify campaignState.labApproved is true. Log all submission details to{campaign_dir}/logs/lab_submission.log.
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.
- 7d ago First seen · 86 lines · 21 tokens per session scan A 1ac76837d5c2
by:approve-lab is a command published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 21d ago), licensed MIT. It adds 21 tokens to every session and 638 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 2026-08-30.
Other commands, from other repositories
read
Accepts: local PDF path, DOI, journal URL, or a pasted abstract with basic metadata.
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.