Borrowing it
Nothing to install: this file belongs to chacosoldier/compabob. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/chacosoldier/compabob/main/.claude/skills/build-list/SKILL.mdgit clone --depth 1 https://github.com/chacosoldier/compabobWrote 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/chacosoldier/compabob/build-list)<a href="https://agentmods.dev/skills/chacosoldier/compabob/build-list"><img src="https://agentmods.dev/badge/skills/chacosoldier/compabob/build-list/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/chacosoldier/compabob/build-list"><img src="https://agentmods.dev/badge/skills/chacosoldier/compabob/build-list.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 | $0.00064 | $0.01021 |
| Opus 5.5 | $0.00026 | $0.00408 |
| Sonnet 5.5 | $0.00013 | $0.00204 |
| Haiku 4.5 | $0.00006 | $0.00102 |
Grade A, and why
build-list 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build List
Turn an ICP description into a ranked, deduped, CRM-aware outbound list using the
lead-pipeline module. You run the judgement/IO steps (discover, enrich); the
script runs the deterministic steps (clean, dedup, score). The cardinal rule:
Dedup against the CRM before enriching. Never spend enrichment effort or credits on accounts the user already knows. Known accounts become warm intros.
Preconditions
- A CRM to dedup against:
data/crm-merge/contacts.json. If it is missing, tell the user to run/merge-contactsfirst (dedup is the whole point); offer to proceed without it only if they insist, noting every lead will beproceed. - An ICP rubric at
modules/lead-pipeline/icp.json. If onlyicp.example.jsonexists, copy it and adjust it to the user's request before scoring.
Steps
-
Read the ICP from the request. Pull out: industry/segment, geography, company-size band, and target buyer titles. Confirm anything ambiguous in one short question rather than guessing.
-
Discover candidates → raw CSV. Use the web-search MCP (e.g. exa
web_search_exa/linkedin_search_exa) to find matching accounts. Write a CSV toreports/lead-pipeline/00-raw.csvwith whatever you found across these columns:company, domain, contact_name, contact_email, title, country(partial rows are fine; later stages fill gaps). If no search MCP is available, ask the user for a raw CSV and skip to step 3. -
Clean (script):
python3 modules/lead-pipeline/pipeline.py clean \ --in reports/lead-pipeline/00-raw.csv \ --out reports/lead-pipeline/01-cleaned.csv --country <codes> -
Dedup against the CRM (script):
python3 modules/lead-pipeline/pipeline.py dedup \ --in reports/lead-pipeline/01-cleaned.csv \ --crm data/crm-merge/contacts.json \ --out reports/lead-pipeline/02-deduped.csvReport the split: how many
proceed(cold),warm(known account, route viacrm_known_contact),skip(already known, drop from outbound).
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 Changed 453e14722d08
- 1mo ago First seen · 85 lines · 64 tokens per session scan A f3e9cc1968ea
build-list is a skill published in the GitHub repository chacosoldier/compabob (34 stars, last pushed 5d ago), licensed MIT. It adds 64 tokens to every session and 1,021 once invoked, about $0.0003 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 skills, from other repositories
ha-logs
A read-only troubleshooting skill for querying Hope Agent’s local SQLite databases, which store logs, conversations, and background-job status.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
vellum-memory-v3-migration
One-time migration of an existing memory-v2 concept corpus into the memory-v3 section-grain "wiki" — topical articles with a stand-alone lead and queryable sections — with loss-proof staging, assistant-reviewed authoring, and a retrieval-eval gate before cutover.
media-processing
Ingest and process media files (video, audio, image).
notifications
Send notifications through the unified notification router.
visualize
Render a polished visual inline in the chat as part of your answer — a diagram, a chart, an interactive explainer, or a UI mockup. Load it proactively whenever an explanation would land better as a picture than as prose. Do not wait to be asked.