Chalk Labs
Need help deploying ad-investigate?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/ansidev-ad-investigate
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: marketing
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/ad-investigate
What's inside
- Skill name
ad-investigate- Description
- Use when investigating an issue
- License
- MIT
- Instructions
- 79 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Issue Investigation ## Overview Structured analysis of the issue to isolate root causes with evidence before proposing fixes. ## When to Use - User provides a log file path or pasted the input. - User asks for root cause from stack traces or incident logs. - User wants explained causes plus concrete remediation options. ## Inputs Required: - A log file path or pasted input Optional: - Time window where failure occurred - Environment (`dev`, `staging`, `prod`) - Recent changes or deployment info ## Workflow **REQUIRED SUB-SKILL:** Read and follow `superpowers:systematic-debugging` before proposing any fix. 1. Read the full log first, then isolate the earliest high-signal errors. 2. Build an evidence table (timestamp, component, message, probable subsystem). 3. Group related errors to distinguish primary failures from cascade noise. 4. State root-cause hypotheses with direct log evidence. 5. Validate each hypothesis against sequence, context, and known behavior. 6. Report confirmed root causes, explain impact, then propose minimal fixes. ## Output format Use this structure: ```markdown ## Root Cause Analysis ### Root Cause 1 - Evidence: - Why this is the root cause: - Impact: ### Root Cause 2 (if any) - Evidence: - Why this is the root cause: - Impact: ## Proposed Fixes - Fix 1: - Why it addresses the root cause: - Validation steps: - Fix 2: - Why it addresses the root cause: - Validation steps: ## Unknowns / Follow-ups - Missing evidence: - Additional logs or metrics needed: ``` ## Guardrails - Do not propose fixes without identifying root cause evidence first. - Treat repeated downstream exceptions as potential symptoms until proven otherwise. - Prefer smallest safe fix that removes the source failure. - If evidence is insufficient, explicitly say what is missing and request it. - Redact or mask secrets, tokens, credentials, and PII when quoting logs. ## Common Mistakes - Jumping straight to fixes without isolating first-cause errors. - Treating every repeated error as a separate root cause. - Ignoring chronology and missing the first triggering event. - Presenting assumptions without quoting supporting log evidence.
From ansidev/skills, MIT.
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ad-review-pr
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Use when reviewing a pull request for correctness, completeness, and quality.
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Use when research something and save the result to the knowledgebase
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Use to implement a fix for a known issue, or to implement a new feature.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Sep 4, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/ansidev/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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- 0.570score
kp-issue-finish
ansidev
Use this skill to finish a kanpal issue after the user has manually reviewed and approved the implementation.
SkillAFreshness: AClaude Code +9 - 0.570score
kp-issue-start
ansidev
Use this skill to start working on the solution for the relevant kanpal issue.
SkillAFreshness: AClaude Code +9 - 0.560score
add-knowledge
ansidev
Add knowledge to OpenKnowledge base.
SkillAFreshness: AClaude Code +9 - 0.536score
kp-issue-new
ansidev
Use this skill whenever the user wants to create a new issue in Kanpal.
SkillAFreshness: AClaude Code +9 - 0.524score
wt-commit
ansidev
Use this skill when a task is finished and its changes need to be committed — either in a git worktree on a detached HEAD (commit the pending changes and…
SkillBFreshness: BClaude Code +9 - 0.523score
ad-review-pr
ansidev
Use when reviewing a pull request for correctness, completeness, and quality.
SkillBFreshness: BClaude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
ad-investigate skill. Agent Mart. https://agentmart.dev/skills/ansidev-ad-investigate (last updated 2026-10-24)