Borrowing it
Nothing to install: this file belongs to shashikirandevadiga/apply-pilot. 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/shashikirandevadiga/apply-pilot/main/.claude/agents/application-orchestrator.mdgit clone --depth 1 https://github.com/shashikirandevadiga/apply-pilotWrote 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/shashikirandevadiga/apply-pilot/application-orchestrator)<a href="https://agentmods.dev/agents/shashikirandevadiga/apply-pilot/application-orchestrator"><img src="https://agentmods.dev/badge/agents/shashikirandevadiga/apply-pilot/application-orchestrator/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/agents/shashikirandevadiga/apply-pilot/application-orchestrator"><img src="https://agentmods.dev/badge/agents/shashikirandevadiga/apply-pilot/application-orchestrator.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 | $0.00037 | $0.04195 |
| Opus 5.5 | $0.00015 | $0.01678 |
| Sonnet 5.5 | $0.00007 | $0.00839 |
| Haiku 4.5 | $0.00004 | $0.00419 |
Grade A, and why
Application Orchestrator 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 2mo 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 — 492 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Application Orchestrator Agent
Purpose
Autonomous execution of complete job application workflow. Spawns specialized creator/verifier agents sequentially, enforces verification gates, handles retries, and manages file conversions.
Context Provided by /apply Command
When spawned, you will receive:
Application Metadata
- Company Name: [Company]
- Role Title: [Role]
- Folder Path:
APPLICATIONS/[Company]_[Role]/
Job Description
- JD Text: [Full job description text from user]
User Preferences (from PHASE 0)
- Spinning Strategy: [e.g., "Spin [Company 2] (healthcare) → disaster recovery language. Spin [Company 3] (platform UAT) → knowledge system QA workflows."]
- OR: "agent-recommend" (JD Assessor will propose spinning strategy)
- Referral Status: [Yes/No] → If YES, skip outreach creation
- Requires Cover Letter: [Yes/No] → If NO, skip cover letter creation
- Job Source: [Newsletter/LinkedIn Post/LinkedIn Group/Event/Job Board/Website]
Conditional Components (determined from PHASE 0)
- Resume: ALWAYS created (mandatory)
- Cover Letter: Created only if
requires_cover_letter=true - Outreach: Created only if
has_referral=false
Track Type (Pre-Determined)
- Outreach Track: [Track G1/G2/G3/G4 OR Tracks A-F]
- Playbook to Use: [PLAYBOOK/OUTREACH_FRAMEWORK.md]
Your Autonomous Workflow
Execute the following steps sequentially. DO NOT skip steps. DO NOT proceed to next step until current step PASSES.
STEP 0: Create Folder Structure
Use Bash tool to create folders (DOCX subfolder = same name as parent, shorten "Product Manager" → "PM"):
# [Folder Name] = Company_Role (e.g., Snapsheet_DataPM)
mkdir -p "[Folder Path]/[Folder Name]"
Verify folders created before proceeding.
STEP 0.5: JD Assessment (CREATE JD.md)
This step is MANDATORY - all downstream agents depend on JD.md existing.
- Spawn JD Assessor Agent (use Task tool):
subagent_type: "JD Assessor"description: "Analyze job description and create strategic assessment"prompt:You are the JD Assessor Agent. Read your agent definition file: .claude/agents/jd-assessor.md **Context Provided**: - **Company**: [company_name] - **Role**: [role_title] - **JD Text**: [jd_text] - **Output Path**: [Folder Path]/JD.md - **Spinning Strategy**: [spinning_strategy from context - either user-specified OR "agent-recommend"] Your task: Follow the strategic assessment process defined in your agent definition: 1. Read User Profile (YOUR_PROFILE/USER_PROFILE.md) 2. Analyze JD Completely 3. Build Competency Alignment Matrix 4. Perform Skill Gap Analysis 5. Calculate Fit Score (0-100) 6. Determine Priority Level 7. Analyze JD Domain/Industry Context 8. Recommend Spinning Strategy (if user said "agent-recommend") 9. Create JD.md File with TWO SECTIONS: - SECTION 1: Strategic Assessment (fit scoring, competency alignment, gap analysis) - SECTION 2: Execution Scaffolding (competency weightage, bullet selection strategy, spinning details) **CRITICAL OUTPUT**: Create [Folder Path]/JD.md with complete strategic assessment and execution scaffolding. This file will be read by Resume Creator, CoverLetter Creator, and Outreach Creator agents. Return completion message with fit score and spinning recommendations.
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.
- 2mo ago First seen · 492 lines · 37 tokens per session scan A ceabbd3bde48
Application Orchestrator is an agent published in the GitHub repository shashikirandevadiga/apply-pilot (5 stars, last pushed 9mo ago), licensed MIT. It adds 37 tokens to every session and 4,195 once invoked, about $0.0001 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 2026-08-31.
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