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/hezaohezao/poirot/github-deep-researchnpx skills add HezaoHezao/poirot --skill github-deep-researchgit clone --depth 1 https://github.com/HezaoHezao/poirotWrote 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/hezaohezao/poirot/github-deep-research)<a href="https://agentmods.dev/skills/hezaohezao/poirot/github-deep-research"><img src="https://agentmods.dev/badge/skills/hezaohezao/poirot/github-deep-research.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 5.1 | $0.00018 | $0.01242 |
| Opus 5 | $0.00009 | $0.00621 |
| Sonnet 5 | $0.00004 | $0.00248 |
| Haiku 4.5 | $0.00002 | $0.00124 |
Grade C, and why
github-deep-research scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s "https://api.github.com/repos/$OWNER/$REPO" | python3 -c " Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
> version uses `bash` with `curl` to the GitHub API directly + `gh` CLI when How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Deep Research
Multi-round research combining GitHub API, web_search, and browse_page to
produce comprehensive markdown reports on any GitHub repository.
Poirot note: The original deer-flow skill uses a bundled
scripts/github_api.pyhelper. Poirot doesn't bundle that script, so this version usesbashwithcurlto the GitHub API directly +ghCLI when available.
When to Use
- User provides a GitHub repository URL
- User asks for comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of an open source project
- User wants to understand a project's architecture, history, or community
Research Workflow
- Round 1: GitHub API (repo metadata, README, file tree, contributors, commits)
- Round 2: Discovery (web search for overview, competitors)
- Round 3: Deep Investigation (architecture, timeline, community sentiment)
- Round 4: Deep Dive (commit history, issues/PRs for feature evolution)
Round 1 — GitHub API
Setup
# Resolve owner/repo from remote URL
REMOTE_URL=$(git remote get-url origin 2>/dev/null || echo "")
# Or user provides owner/repo directly
OWNER="owner"
REPO="repo"
Repo metadata via curl
# Repo summary
curl -s "https://api.github.com/repos/$OWNER/$REPO" | python3 -c "
import sys, json
r = json.load(sys.stdin)
print(f'Name: {r[\"full_name\"]}')
print(f'Description: {r[\"description\"]}')
print(f'Stars: {r[\"stargazers_count\"]}')
print(f'Forks: {r[\"forks_count\"]}')
print(f'Language: {r[\"language\"]}')
print(f'License: {r.get(\"license\",{}).get(\"spdx_id\",\"N/A\")}')
print(f'Created: {r[\"created_at\"][:10]}')
print(f'Updated: {r[\"updated_at\"][:10]}')
"
# README
curl -s "https://api.github.com/repos/$OWNER/$REPO/readme" | python3 -c "
import sys, json, base64
r = json.load(sys.stdin)
print(base64.b64decode(r['content']).decode('utf-8'))
"
# Recent commits
curl -s "https://api.github.com/repos/$OWNER/$REPO/commits?per_page=10" | python3 -c "
import sys, json
for c in json.load(sys.stdin):
print(f'{c[\"sha\"][:7]} {c[\"commit\"][\"author\"][\"date\"][:10]} {c[\"commit\"][\"message\"].splitlines()[0][:80]}')
"
# Languages
curl -s "https://api.github.com/repos/$OWNER/$REPO/languages"
# Contributors
curl -s "https://api.github.com/repos/$OWNER/$REPO/contributors?per_page=10" | python3 -c "
import sys, json
for c in json.load(sys.stdin):
print(f'{c[\"login\"]:20s} {c[\"contributions\"]} commits')
"
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.
- 6d ago First seen · 155 lines · 18 tokens per session scan C a281a58db8f3
github-deep-research is a skill published in the GitHub repository HezaoHezao/poirot (215 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 1,242 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
commit
Atomic git commit with conventional message. Use when the user says "commit", "save my changes", "commit this", or wants to create a git commit. Stages specific files, writes a conventional commit message with body explaining non-obvious decisions. Never uses git add -A.
python-run
Run and debug Python scripts in the project. Use when the user says "run python", "execute this script", "debug this py file", or wants to run/modify a .py file. Handles dependency checks, linting, execution, and error analysis.
hqe
Comprehensive codebase health auditing, remediation, and verification skill based on the canonical HQE Protocol v5.0.0.
cost-efficiency-analyzer
Analyzes cost structure, cost efficiency, and expense management from P&L data. Use when the user asks about costs, expenses, COGS, operating expenses, cost ratios, cost control, spending efficiency, margin compression from cost side, or wants to understand where money is going. Also use for "are we spending too…
computer-use
Read and drive native desktop applications through the accessibility layer — list on-screen apps, snapshot one window as a numbered element tree, then click / type / set a value / scroll / drag / run a named action, by element index or by screen coordinates. Use for work in a desktop app rather than a web page. Full…
goal-conductor
Own a long-horizon goal end to end - decompose it into work items, stand up one top-level session per item, patrol their state on a nudge loop, and decide each next round until the goal is met or a stop condition fires. Use when the user hands over a goal too large for one session ("clear the flaky-test backlog"…