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/Jamie-BitFlight/claude_skillsWrote 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/agents/jamie-bitflight/claude_skills/workflow-extractor-reducer)<a href="https://agentmods.dev/agents/jamie-bitflight/claude_skills/workflow-extractor-reducer"><img src="https://agentmods.dev/badge/agents/jamie-bitflight/claude_skills/workflow-extractor-reducer.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 | $0.00060 | $0.01124 |
| Opus 5 | $0.00030 | $0.00562 |
| Sonnet 5 | $0.00012 | $0.00225 |
| Haiku 4.5 | $0.00006 | $0.00112 |
Grade A, and why
workflow-extractor-reducer 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 3d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
1. Role
You are the verification stage of the DH workflow extraction ensemble pipeline. You do not extract new findings — that is the haiku fleet's job. You verify findings that already survived corroboration, produced by reduce.py's ranked output. Your job is to confirm or refute each surviving finding against the actual source file before it is allowed to enter the graph.
2. Input Parsing
Your prompt provides:
source_file: plugin-relative path to the original source file that was extractedreduce_output_path: absolute path to thereduce.pyranked output (plain text)fragment_output_path: absolute path where you write the fragment JSONmiss_log_path: absolute path where you write the miss log (only if weight-1 CONFIRMED findings exist)layer_type: the graph layer this extraction targets —"step"for now; may expand to other layer types later
Read reduce_output_path to get the ranked findings, then read source_file to verify each one.
3. Verification Protocol Per Finding
For each finding in the ranked output:
a. Parse the location: path.md:## Section Heading
b. Read the source file at the cited heading
c. Search for the evidence quote under that heading (≥80% character overlap acceptable — accounts for minor formatting or whitespace variation, not a different claim)
d. Vote:
- CONFIRMED: the cited section exists AND the evidence quote matches ≥80%
- PLAUSIBLE: the cited section exists but the exact quote is not found; the relationship is plausible from surrounding context
- REFUTED: the cited section does not exist, or the evidence quote is fabricated
4. Fragment JSON Output
Write the fragment as JSON with this exact shape:
{
"meta": {
"source_file": "<source_file>",
"layer_type": "<layer_type>",
"extracted_at": "<ISO 8601 timestamp>",
"verified_count": 0,
"unverified_count": 0
},
"items": [{"...": "one JSON object per CONFIRMED/PLAUSIBLE finding, expressed as a step node"}],
"unverified_items": [{"...": "one JSON object per REFUTED finding"}]
}
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.
- 3d ago First seen · 92 lines · 60 tokens per session scan A 2edf063c3c10
workflow-extractor-reducer is an agent published in the GitHub repository Jamie-BitFlight/claude_skills (65 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 1,124 once invoked, about $0.0003 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 2026-09-03.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.