setup
Skill9 starsChanged 3 months agoMITView on GitHub
Use when configuring SignalForce for a new ICP, when setting up the project for the first time, or when switching to a different target market
191 other projects use a version of this.
Adopted by
Most recently taken up by
- hydra-db/hydradb-claude-codetheir own version, first seen Nov 8, 2026
- WALKERKILLER/Look-Tongji-Notestheir own version, first seen Nov 8, 2026
- steerlabs/opensteertheir own version, first seen Nov 8, 2026
- ansvisor/ansvisortheir own version, first seen Nov 8, 2026
- dsifry/metaswarmtheir own version, first seen Nov 8, 2026
- anombyte93/prd-taskmastertheir own version, first seen Nov 8, 2026
Used with
Projects with it also use add-voice-transcription, add-discord and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
juancruzmunozalbeloSkilladd-voice-transcription
Add voice message transcription to NanoClaw using OpenAI's Whisper API. Automatically transcribes WhatsApp voice notes so the agent can read and respond to them.
Also in 5 projects that use this
MicrosoftSkilladd-discord
Add Discord bot channel integration via Chat SDK.
Also in 11 projects that use this
MicrosoftSkilladd-slack
Add Slack channel integration via Chat SDK.
Also in 11 projects that use this
juancruzmunozalbeloSkilladd-telegram-swarm
Add Agent Swarm (Teams) support to Telegram. Each subagent gets its own bot identity in the group. Requires Telegram channel to be set up first (use /add-telegram). Triggers on "agent swarm", "agent teams telegram", "telegram swarm", "bot pool".
Also in 7 projects that use this
- +8 more from microsoft
Other versions191 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 191
- anthropics/knowledge-work-plugins★ 35ksame name
- garrytan/gbrain★ 32ksame name
- gavrielc/nanoclaw★ 31ksame name
- nanocoai/nanoclaw★ 31ksame name
- qwibitai/nanoclaw★ 31ksame name
- Q00/ouroboros★ 6.3ksame name
- pytorch/executorch★ 5.2ksame name
- agenticnotetaking/arscontexta★ 3.5ksame name
- aws/agent-toolkit-for-aws★ 3.4ksame name
- spotify/portal-ai-plugins★ 2.8ksame name
181 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository15 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- champion-trackerSkill
- compliance-managerSkill
- contact-finderSkill
- deliverability-managerSkill
- email-writerSkill
- linkedin-contentSkill
- meeting-followupSkill
- multi-channel-writerSkill
- pipeline-trackerSkill
- prospect-researcherSkill
- resource-offerSkill
- signal-scannerSkill
- validateSkill
The file
We split this skill into the rules it gives an agent
10 rules. 0 are common, 7 are distinctive, and 3 are very specific to this project.
0
Common
Most of the 170 edited versions we read keep these.
7
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 15
Wait for answer, then:
- Distinctive: line 30
Use all four blocks to inform the config generation.
- Very specific: line 34
Use config.example/config.yaml as the schema template.
- Distinctive: line 52
Follow the 4-sentence rule for initial outreach.
- Distinctive: line 56
Keep connection requests under 300 characters
- Very specific: line 58
Create config/ if it does not exist.
- Distinctive: line 72
Ask: "What did I get wrong?"
- Very specific: line 80
Run /validate on the generated config before confirming setup is complete.
- Distinctive: line 85
Never copy example values verbatim — always infer values from the user's domain.
- Distinctive: line 86
If the user's market has no obvious GitHub or ArXiv signal, disable those scanners and explain why.
“Common” is measured: kept by at least 6 in 10 of the 170 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Setup
Step 1 — Understand the business
Work through these questions in stages. Do not ask all at once — ask each block, wait for answers, then move forward.
Block A — Identity:
"What does your company sell, and who do you sell to?"
Wait for answer, then:
Block B — Voice and positioning:
"How would you describe your brand's tone? (e.g., technical/accessible, formal/casual, bold/understated) Any words or phrases that are on-brand? Anything that's off-brand — jargon, hype words, clichés to avoid?"
Wait for answer, then:
Block C — Competitive landscape:
"Who are your top 2-3 competitors? What do they do well and where do they fall short?"
Wait for answer, then:
Block D — Success criteria:
"What does success look like in 90 days? Is there a specific metric that matters most?"
Use all four blocks to inform the config generation. If the user gives a rich first answer, you may skip Block B-D and infer — but present your inferences for review.
Step 2 — Auto-generate config
Use config.example/config.yaml as the schema template. Based on the user's answer, generate:
config/config.yaml
- Keywords tuned to their domain (product names, technology terms, research areas)
- Job titles that map to their buyer personas (economic buyer, technical buyer, champion)
- ICP tiers ranked by fit (Tier 1 = ideal, Tier 2 = good, Tier 3 = marginal)
- Scoring weights that match signal value for their market
- Enable scanners that are likely to surface signal; disable those that won't
config/gtm-context.md
- Product positioning in 2–3 sentences (what it does, for whom, why now)
- Voice and tone rules: describe brand voice in 3-5 adjectives, on-brand language, off-brand language, channel-specific tone adaptations (email vs LinkedIn vs Slack)
- Target personas with pain points and buying triggers per tier
- Competitors section with strengths/weaknesses
- Anti-examples: "content that represents what the brand should never sound like"
config/templates/email-sequences/
- One draft 3-email sequence per enabled scanner signal type
- Follow the 4-sentence rule for initial outreach; low-friction CTA
config/templates/linkedin-sequences/
- One draft connection request + follow-up per enabled signal type
- Keep connection requests under 300 characters
Create config/ if it does not exist. Write all files before presenting for review.
Step 3 — Present inferences for review
Show a concise summary:
ICP Tiers: [Tier 1 name] / [Tier 2 name] / [Tier 3 name]
Key signals: [list of enabled scanners]
Disabled: [list of disabled scanners and why]
Keywords: [top 10 keywords]
Buyer titles: [list]Ask: "What did I get wrong?"
Step 4 — Iterate on corrections
Apply each correction the user identifies and regenerate the affected section. Re-present the summary. Repeat until the user confirms the config looks correct.
Step 5 — Validate
Run /validate on the generated config before confirming setup is complete. Fix any ERRORs before finishing. Surface WARNs to the user with suggested fixes.
Notes
- Reference
config.example/config.yamlfor field names, types, and defaults. - Never copy example values verbatim — always infer values from the user's domain.
- If the user's market has no obvious GitHub or ArXiv signal, disable those scanners and explain why.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

