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.
npx agentmods add skills/shinpr/ai-coding-project-boilerplate/task-analyzernpx skills add shinpr/ai-coding-project-boilerplate --skill task-analyzergit clone --depth 1 https://github.com/shinpr/ai-coding-project-boilerplateWrote 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/shinpr/ai-coding-project-boilerplate/task-analyzer)<a href="https://agentmods.dev/skills/shinpr/ai-coding-project-boilerplate/task-analyzer"><img src="https://agentmods.dev/badge/skills/shinpr/ai-coding-project-boilerplate/task-analyzer.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 | $0.00041 | $0.01588 |
| Opus 5 | $0.00020 | $0.00794 |
| Sonnet 5 | $0.00008 | $0.00318 |
| Haiku 4.5 | $0.00004 | $0.00159 |
Grade A, and why
task-analyzer 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 4d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task Analyzer
Provides metacognitive task analysis and skill selection guidance.
Skills Index
See skills-index.yaml for available skills metadata.
Task Analysis Process
1. Understand Task Essence
Identify the fundamental purpose beyond surface-level work:
| Surface Work | Fundamental Purpose |
|---|---|
| "Fix this bug" | Problem solving, root cause analysis |
| "Implement this feature" | Feature addition, value delivery |
| "Refactor this code" | Quality improvement, maintainability |
| "Update this file" | Change management, consistency |
Key Questions:
- What problem are we really solving?
- What is the expected outcome?
- What could go wrong if we approach this superficially?
2. Estimate Structural Scale
Classify decision burden from the intended outcomes and responsibility boundaries. File count is supporting evidence only.
| Scale | Decision burden |
|---|---|
| Small | One coherent outcome, one evident repository-supported implementation within one responsibility boundary, and no unresolved durable choice |
| Medium | One coherent outcome that coordinates a boundary or contains a potentially durable choice |
| Large | Multiple independently valuable outcomes that require separate design decisions |
A cross-layer implementation can remain Medium when it serves one coherent outcome. A decision point passing both documentation-criteria ADR filters raises the scale to Medium at minimum. Record the evidence that established the outcome and boundary classification in scaleRationale.
Scale affects skill priority:
- Larger scale → process/documentation skills more important
- Smaller scale → implementation skills more focused
3. Identify Task Type
| Type | Characteristics | Key Skills |
|---|---|---|
| implementation | New code or user-visible behavior | coding-standards, typescript-testing |
| fix | Defect or regression resolution | coding-standards, typescript-testing |
| refactoring | Behavior-preserving structure improvement | coding-standards, implementation-approach |
| design | Architecture or contract decisions | documentation-criteria, implementation-approach |
| quality | Testing, review, verification | typescript-testing, integration-e2e-testing |
| documentation | PRD, ADR, Design Doc, UI Spec, plan, or instruction content | documentation-criteria |
| investigation | Evidence gathering without implementation | project-context plus the domain skill selected from the index |
| migration | Data, schema, API, dependency, or runtime transition | implementation-approach, documentation-criteria |
| operations | Environment, deployment, or runtime operation | technical-spec plus the domain skill selected from the index |
| security | Security design or review | coding-standards plus the implementation-domain skill |
| skill | Skill creation, prompt-quality review, or skill metadata change | skill-optimization, llm-friendly-context |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 162 lines · 41 tokens per session scan A 38be4fa4b24e
task-analyzer is a skill published in the GitHub repository shinpr/ai-coding-project-boilerplate (227 stars, last pushed 7d ago), licensed MIT. It adds 41 tokens to every session and 1,588 once invoked, about $0.0002 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 2026-08-30.
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