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 warpdotdev/recruiting-sourcing-agent-oss --skill molly-calibrationgit clone --depth 1 https://github.com/warpdotdev/recruiting-sourcing-agent-ossWrote 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/warpdotdev/recruiting-sourcing-agent-oss/molly-calibration)<a href="https://agentmods.dev/skills/warpdotdev/recruiting-sourcing-agent-oss/molly-calibration"><img src="https://agentmods.dev/badge/skills/warpdotdev/recruiting-sourcing-agent-oss/molly-calibration/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/warpdotdev/recruiting-sourcing-agent-oss/molly-calibration"><img src="https://agentmods.dev/badge/skills/warpdotdev/recruiting-sourcing-agent-oss/molly-calibration.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 | $0.00041 | $0.01114 |
| Opus 5 | $0.00020 | $0.00557 |
| Sonnet 5 | $0.00008 | $0.00223 |
| Haiku 4.5 | $0.00004 | $0.00111 |
Grade A, and why
molly-calibration scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://api.notion.com/v1/blocks/SPEC_PAGE_ID/children?page_size=100" \ How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Calibration
Triggered when a recruiter submits the "Review All Candidates" modal (via your Slack interactivity handler — see the README). Your prompt contains every decision at once: ROLE, CALIBRATION CYCLE, SPEC PAGE ID, JD PAGE ID, SLACK CHANNEL, SLACK THREAD TS, and a numbered list of candidates with APPROVED/REJECTED + optional feedback.
Steps
1. Update all rows in the calibration table
Read the spec page to find the calibration cycle table:
curl -s "https://api.notion.com/v1/blocks/SPEC_PAGE_ID/children?page_size=100" \
-H "Authorization: Bearer $MOLLY_NOTION_API_KEY" \
-H "Notion-Version: 2022-06-28"
Find the heading ## Calibration Cycle N (where N = CALIBRATION CYCLE). Get the table block that follows it. Get the table's row block IDs.
For each candidate, update the corresponding row (row index = candidate number, accounting for the header row at index 0):
curl -s -X PATCH "https://api.notion.com/v1/blocks/ROW_BLOCK_ID" \
-H "Authorization: Bearer $MOLLY_NOTION_API_KEY" \
-H "Content-Type: application/json" \
-H "Notion-Version: 2022-06-28" \
-d '{"table_row": {"cells": [
[{"type": "text", "text": {"content": "NAME", "link": {"url": "LINKEDIN_URL"}}}],
[{"type": "text", "text": {"content": "WHY_IT_FITS"}}],
[{"type": "text", "text": {"content": "yes_or_no"}}],
[{"type": "text", "text": {"content": "FEEDBACK_OR_EMPTY"}}]
]}}'
Count approvals (APPROVED decisions). All rows will always have a result since the modal requires all decisions before submitting. Continue to step 2.
2. Analyze the cycle and write Calibration Notes
Read all rows. Count approvals (yes) and rejections (no). Identify patterns in the feedback.
Append ## Calibration Notes N to the spec page with a brief summary:
- How many approved / rejected
- What the feedback reveals about the spec (e.g. "Rejections suggest candidates with large audiences but without hands-on developer community management are a miss")
- Whether another calibration cycle is needed (yes if fewer than the full batch approved)
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.
- 9d ago First seen · 83 lines · 41 tokens per session scan A 70a2c32d764c
molly-calibration is a skill published in the GitHub repository warpdotdev/recruiting-sourcing-agent-oss (2 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 1,114 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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