Agent skill

Disqualification Handling

by gooseworks-ai in gooseworks-ai/goose-skills

Handles disqualified and near-miss inbound leads gracefully.

MITAuto-check passedMarketing & SEO

Install Disqualification Handling

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill disqualification-handling -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills disqualification-handling --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sales/composites/disqualification-handling .claude/skills/disqualification-handling && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
disqualification-handling
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.9k tokens
SKILL.md length
1,504 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Handles disqualified and near-miss inbound leads gracefully.

  • Works in 4 steps: Configuration (Once Per Client) → Categorize Disqualified Leads → Draft Responses → …
  • Tasks that involve Lead generation
  • SKILL.md covers When to Auto-Load, Architecture, Step 0: Configuration (Once… and Step 1: Categorize…, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Disqualification Handling is an agent skill from gooseworks-ai/goose-skills. Handles disqualified and near-miss inbound leads gracefully. Drafts polite rejection emails, referral requests (right company wrong person), and nurture routing (future fit). Ensures no inbound lead gets ignored and every disqualification preserves the relationship. Tool-agnostic.

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).

It sits in Marketing & SEO, covering Lead generation. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve Lead generation

Example prompts

  • “Use the disqualification-handling skill to handle disqualified and near-miss inbound leads gracefully”
  • “/disqualification-handling”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Configuration (Once Per Client)
  2. Categorize Disqualified Leads
  3. Draft Responses
  4. Route to Destination

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Disqualification Handling loads about 3.9k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 1,504 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,504 words, ~3,866 tokens.

Download SKILL.mdSave it as .claude/skills/disqualification-handling/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
disqualification-handling
description
Handles disqualified and near-miss inbound leads gracefully. Drafts polite rejection emails, referral requests (right company wrong person), and nurture routing (future fit). Ensures no inbound lead gets ignored and every disqualification preserves the relationship. Tool-agnostic.
version
1.0.0
tags
outreach

Disqualification Handling

Processes leads that didn't pass ICP qualification. Instead of ignoring them, this composite handles each category appropriately: polite rejection, referral request, or nurture routing. No inbound lead should feel ghosted.

When to Auto-Load

Load this composite when:

  • User says "handle the disqualified leads", "draft rejection emails", "what do we do with the ones that didn't qualify?"
  • inbound-lead-qualification has completed and disqualified/near-miss leads need handling
  • User has a list of leads that don't fit and wants appropriate responses

Architecture

[Disqualified Leads] → Step 1: Categorize → Step 2: Draft Responses → Step 3: Route to Destination
                              ↓                      ↓                         ↓
                     4 handling categories    Tailored email per type    Nurture/CRM/archive

Step 0: Configuration (Once Per Client)

On first run, establish response preferences.

json
{
  "sender_name": "",
  "sender_title": "",
  "company_name": "",
  "tone": "warm-professional | casual-friendly | formal",
  "nurture_sequence_tool": "Smartlead | HubSpot | Mailchimp | CSV export | none",
  "referral_incentive": "none | mention mutual value | offer resource",
  "include_resource_link": true,
  "resource_url": "",
  "resource_description": "",
  "feedback_survey_url": "",
  "do_not_contact_list": "Supabase | CSV | none"
}

On subsequent runs: Load config silently.


Step 1: Categorize Disqualified Leads

Input

Leads from inbound-lead-qualification with sub-verdicts. Categorize each into one of four handling paths:

Category 1: Right Company, Wrong Person → REFERRAL REQUEST

Trigger: mismatch_type = right_company_wrong_person

  • The company fits ICP but the person who came inbound isn't a buyer/champion/user
  • Example: A marketing intern at a perfect-fit company downloaded a whitepaper
  • Goal: Get an introduction to the right person at that company

Identify the referral target:

  • Using the ICP's buyer personas, determine who at this company we SHOULD be talking to
  • If enrichment data or CRM has other contacts at the company, note them (but still ask for the intro — warmer than cold outreach)
Category 2: Close But Not Quite → NURTURE (FUTURE FIT)

Trigger: sub_verdict = near_miss_nurture OR score 30-49 without a referral opportunity

  • Company is almost ICP but not today — too small, too early stage, wrong geography (but expanding), missing a key requirement that might change
  • Example: A 15-person startup that's clearly growing fast but below your 50-employee minimum
  • Goal: Stay on their radar. When they hit the threshold, you're top of mind.
Category 3: Clearly Outside ICP → POLITE DECLINE

Trigger: sub_verdict = disqualified_polite OR score < 30

  • Not a fit now and unlikely to become one
  • Example: A freelancer, a student, a company in an excluded industry
  • Goal: Decline gracefully. Don't burn bridges — they might refer someone, post about you, or their situation might change unexpectedly.
Category 4: Special Flags → ROUTE DIFFERENTLY

Trigger: sub_verdict = disqualified_competitor OR pipeline_status = existing_customer

  • Competitor employee: Do NOT send a rejection email. Log for competitive intelligence. No outreach.
  • Existing customer: Do NOT treat as disqualified. Route to customer success / account management. May be an upsell or support need.
Output

Each lead tagged with: handling_category, handling_action, referral_target_persona (for Category 1)


Step 2: Draft Responses

Category 1 Response: Referral Request Email

Objective: Thank them, subtly indicate they may not be the right person, and ask for an introduction — without making them feel dismissed.

Structure:

  1. Thank them for their interest (reference what they did — demo request, download, etc.)
  2. Acknowledge their role — show you understand what they do
  3. Pivot to the right person — frame it as "wanting to make sure the right person gets the most value"
  4. Make the ask easy — provide a specific persona/title to connect you with
  5. Offer something in return — a resource relevant to THEIR role (not just the buyer's)

Template framework:

Subject: Thanks for checking us out, [First Name]

Hi [First Name],

Thanks for [downloading our guide on X / requesting a demo / signing up for the trial].
[One sentence connecting their action to a genuine insight — "it's a great resource for
understanding Y."]

I took a look at [Company] — [one sentence showing you understand the company].
Given what [Product] does, I think [target persona title, e.g., "your VP of Engineering"
or "whoever leads infrastructure decisions"] would get the most out of a conversation
with us.

Would you be open to making an introduction? Happy to send you a quick blurb you
can forward, so it's zero effort on your end.

[If resource_link configured: "In the meantime, here's [resource] that might be
useful for [something relevant to THEIR role]."]

Thanks,
[Sender Name]

Rules:

  • Never say "you're not the right person" or "you don't have buying authority"
  • Never use the word "unfortunately" or "however"
  • Frame the referral as maximizing value, not correcting a mistake
  • Keep it under 120 words
  • If you know the specific name of the right person (from enrichment/CRM), mention them: "I think [Name] on your [team] would find this valuable"
Category 2 Response: Nurture Warm-Down Email

Objective: Keep the relationship warm without false promises. Position yourself as a future resource.

Structure:

  1. Thank them for their interest
  2. Be honest (but tactful) about fit — don't say "you're disqualified", say "given where [Company] is today, the timing might not be perfect"
  3. Offer genuine value — share a resource relevant to their current stage/situation
  4. Leave the door open — invite them to reach out when circumstances change
  5. No hard CTA — this is a soft touch, not a sales push

Template framework:

Subject: [First Name] — thanks for your interest in [Product]

Hi [First Name],

Thanks for [action they took]. I looked into [Company] and really like
[genuine compliment — what they're building, their growth, their approach].

Based on where [Company] is right now, I think [honest reason the timing
isn't right, framed positively — e.g., "you'd get the most value from us
once your team scales past X" or "this tends to be most impactful after Y
milestone"].

In the meantime, [here's a resource / I'd recommend / you might find this useful] —
[brief description of why it's relevant to them NOW, not just to your product].

I'll keep an eye on [Company] — would love to reconnect when the timing
is better. Feel free to reach out anytime.

Best,
[Sender Name]

Rules:

  • Never say "disqualified", "not a fit", "don't meet our criteria"
  • Frame timing as the issue, not them — "not yet" beats "not ever"
  • The shared resource should be genuinely useful to them at their current stage, not just a product pitch
  • Keep it under 100 words
  • Warm but not overly familiar
Category 3 Response: Polite Decline Email

Objective: Decline gracefully while leaving a positive impression of your brand.

Structure:

  1. Thank them for their interest
  2. Brief honest explanation — keep it high-level ("we specialize in X and it sounds like you need Y")
  3. Redirect if possible — suggest an alternative tool/service if you know one
  4. Close warmly

Template framework:

Subject: Thanks for reaching out, [First Name]

Hi [First Name],

Thanks for your interest in [Product]. I appreciate you taking the time to
[action they took].

We're focused specifically on [your niche/ICP in plain language], and it
sounds like [their situation — framed neutrally, not critically] might be
a different use case from what we're built for.

[If you know an alternative: "You might want to check out [Alternative] —
they're strong for [their actual need]."]

[If no alternative: "I don't want to waste your time, but if your needs
evolve, we'd be happy to chat."]

Best of luck with [something specific to their situation],
[Sender Name]

Rules:

  • Never apologize ("sorry we can't help") — you're not obligated
  • Never be dismissive or condescending
  • Keep it under 80 words
  • If you can genuinely suggest an alternative, do — it builds goodwill
  • Don't over-explain why they don't fit. One sentence is enough.
Category 4: No Email
  • Competitor: Add to competitive intel tracking. No outbound response. Log: "Competitor employee [Name] from [Company] engaged via [source] on [date]."
  • Existing customer: Draft a brief internal routing note: "Existing customer [Company] ([contact name]) came inbound via [source]. Current plan: [plan]. Account owner: [owner]. Possible upsell signal." Route to CS/AM.

Step 3: Route to Destination

For Each Category:
CategoryEmail ActionCRM ActionSequence Action
Referral RequestDraft ready for human review & sendTag as referral_pendingNone — single touch
NurtureDraft ready for human review & sendTag as nurture_future_fit, set reminder for re-evaluation in 3-6 monthsAdd to nurture drip if configured
Polite DeclineDraft ready for human review & sendTag as disqualified_responded, note reasonNone — single touch
CompetitorNo emailTag as competitor_employee, log in competitive intelNone
Existing CustomerNo sales emailFlag for CS/AM team, note the inbound actionNone
Show full SKILL.md (567 more words)Show less
Nurture Sequence Setup (Category 2)

If a nurture sequence tool is configured:

  1. Group nurture leads by their "why not yet" reason — this determines the drip content
  2. Suggest sequence themes:
    • "Too small right now" → Growth-stage content, case studies of similar companies that grew into ICP
    • "Wrong stage" → Stage-appropriate content, milestone-based check-ins
    • "Missing requirement" → Product update notifications, roadmap content
  3. Add to appropriate sequence or produce a CSV for manual import
Re-Evaluation Triggers

For Category 2 (nurture) leads, define when to re-evaluate:

  • Company raises funding → re-run qualification
  • Company headcount crosses threshold → re-run qualification
  • Person gets promoted → re-run qualification
  • 6 months have passed → manual re-evaluation prompt

If signal composites are active (funding-signal-outreach, hiring-signal-outreach, champion-move-outreach), these triggers happen automatically.


Output Format

Primary: Response Queue for Human Review
markdown
## Disqualification Handling: [Date]

### Summary
- **Total disqualified/near-miss leads:** X
- **Referral requests (right co, wrong person):** X
- **Nurture (future fit):** X
- **Polite decline:** X
- **Competitor (no response):** X
- **Existing customer (route to CS):** X

---

### Referral Requests — Review & Send

#### [Name] — [Title] at [Company]
- **Why not them:** [one sentence — e.g., "Marketing coordinator, we need VP Engineering"]
- **Referral target:** [Title/persona we want to reach]
- **Known contacts at company:** [from enrichment/CRM, if any]
- **Draft email:**
  > [email draft]

---

### Nurture — Review & Send

#### [Name] — [Title] at [Company]
- **Why not yet:** [one sentence — e.g., "20 employees, ICP starts at 50"]
- **Re-evaluate when:** [trigger — e.g., "headcount crosses 50" or "6 months"]
- **Draft email:**
  > [email draft]

---

### Polite Decline — Review & Send

#### [Name] — [Title] at [Company]
- **Why not a fit:** [one sentence]
- **Suggested alternative:** [if known]
- **Draft email:**
  > [email draft]

---

### Flagged (No Outreach)

**Competitors:**
- [Name] from [Company] — [what they did] on [date]

**Existing Customers:**
- [Name] from [Company] — [what they did] — Current account owner: [name]
Secondary: CSV Export

Append to the qualification CSV or produce a separate handling CSV:

  • All lead fields + handling_category, handling_action, email_draft, referral_target, nurture_trigger, re_evaluate_date

Save to the current working directory or wherever the user prefers.


Handling Edge Cases

Lead who's clearly a bot or spam: Skip all categories. Tag as spam and archive. Don't waste an email draft. Signs: gibberish name, test@test.com, form filled in < 2 seconds, obvious fake company.

Lead who requested a demo but is clearly disqualified: Still draft a polite decline. A demo request deserves a human response even if the answer is no. Consider: if they're a strong referral opportunity (right company), prioritize the referral path over the decline path.

Lead at a competitor's customer: This is NOT a competitor employee — this is someone at a company that uses a competitor. They came inbound, which means they might be looking to switch. Do NOT disqualify. Route back to qualification as a high-priority signal.

Multiple people from the same company, some qualified, some not: For disqualified people at a company where someone else qualified: Draft a softer version that acknowledges "we're already in touch with your team at [Company]" — don't send a rejection to someone whose colleague is in active talks.

Lead who's a journalist, analyst, or investor: Not a customer, but not a "decline" either. Flag separately: "Non-customer interest from [Name], [Role] at [Publication/Fund]. May be PR/AR opportunity." Route to marketing or founder.

Very high-volume batches (50+ disqualified):

  • Category 3 (polite decline) and Category 2 (nurture) can use lighter-touch templates rather than personalized drafts
  • Category 1 (referral requests) should always be personalized — these are the highest-value disqualified leads

Email Hard Rules

All draft emails must follow these rules regardless of category:

  1. No lies. Don't say "we'd love to work with you" if you're declining them.
  2. No guilt. Don't make them feel bad for reaching out.
  3. No jargon. "ICP", "qualification", "disqualified" never appear in emails.
  4. Brevity. Referral < 120 words. Nurture < 100 words. Decline < 80 words.
  5. One CTA maximum. Referral: make the intro. Nurture: check the resource. Decline: none or soft redirect.
  6. No false urgency. These are goodwill emails, not sales emails.
  7. Genuine value. If you offer a resource, it should actually help them — not be a disguised pitch.
  8. Reply-friendly. Write in a way that they COULD reply if they wanted to. No "noreply" energy.

Tools Required

  • Email drafting — references email-drafting capability for tone and structure
  • CRM access — to update lead status, add tags, set reminders
  • Nurture sequence tool — to add leads to drip campaigns (Smartlead, HubSpot, etc.)
  • Supabase client — for pipeline lookups and signal cross-reference
  • Read/Write — for CSV output and config management

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/sales/composites/disqualification-handling of gooseworks-ai/goose-skills.

  • SKILL.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Disqualification Handling next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Disqualification Handling compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Disqualification Handling this skillgooseworks-ai/goose-skills1.2k1 repos~3.9kAutomated safety check: PassMIT
Find Leadseracle/OpenOutreach3.2k—~4.6kAutomated safety check: PassGPL-3.0
100m Leadsgetagentseal/founder-playbook729—~2.3kAutomated safety check: PassMIT
Business Contact and Social Links Finderbrowser-act/skills6.1k1 repos~1.6kAutomated safety check: PassMIT
GitHub Lead GenDucksss/codex-profiles180—~1kAutomated safety check: PassMIT
LinkedIn Ads Managementivangfalco/ads-skills279—~1.7kAutomated safety check: PassCustom licence

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Categories

Questions about Disqualification Handling

What does Disqualification Handling do?

Handles disqualified and near-miss inbound leads gracefully. Disqualification Handling is an agent skill from gooseworks-ai/goose-skills. Handles disqualified and near-miss inbound leads gracefully.

When should I use Disqualification Handling?

Disqualification Handling fits situations like: tasks that involve Lead generation.

How do I install Disqualification Handling in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill disqualification-handling -a claude-code`. Or copy the skill folder (skills/sales/composites/disqualification-handling in gooseworks-ai/goose-skills) into .claude/skills/disqualification-handling in your project. Claude Code loads it when a task matches its description.

How do I install Disqualification Handling in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill disqualification-handling -a codex`. Or copy the skill folder (skills/sales/composites/disqualification-handling in gooseworks-ai/goose-skills) into .agents/skills/disqualification-handling in your project. Codex loads it when a task matches its description.

Can I use Disqualification Handling in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill disqualification-handling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/disqualification-handling, .gemini/skills/disqualification-handling, .github/skills/disqualification-handling and .opencode/skills/disqualification-handling in your project.

What does Disqualification Handling need to run?

SKILL.md names no scripts, command-line tools or credentials: Disqualification Handling is instructions for the agent only.

Does Disqualification Handling access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Disqualification Handling safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Disqualification Handling use?

Disqualification Handling is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Disqualification Handling use?

About 3.9k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Disqualification Handling?

Skills that share tags, products or a category with Disqualification Handling: Find Leads (eracle/OpenOutreach, 3.2k stars), 100m Leads (getagentseal/founder-playbook, 729 stars), Business Contact and Social Links Finder (browser-act/skills, 6.1k stars) and GitHub Lead Gen (Ducksss/codex-profiles, 180 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Disqualification Handling?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.