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/taichuy/1flowbase/github-solution-researchnpx skills add taichuy/1flowbase --skill github-solution-researchgit clone --depth 1 https://github.com/taichuy/1flowbaseWhat 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 | $0.00091 | $0.01880 |
| Opus 5 | $0.00046 | $0.00940 |
| Sonnet 5 | $0.00018 | $0.00376 |
| Haiku 4.5 | $0.00009 | $0.00188 |
Grade A, and why
github-solution-research 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 2d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Solution Research
Use GitHub as problem-solving evidence and an implementation source. The goal is to find open-source projects and GitHub evidence that already solve the user's specific engineering problem, report the relevant project information, then translate the existing solution into a local fix, implementation path, or verification plan.
This skill is for concrete problems first. For general tool or architecture selection, use it only after the local goal has been framed as a specific capability, blocker, workflow, or integration need.
When to Use
- Runtime, build, test, deploy, package, SDK, API, dependency, framework, or integration errors.
- A feature implementation is blocked by an unclear edge case, missing usage pattern, or uncertain API behavior.
- A local issue resembles something that maintainers or other open-source users may have resolved in issues, PRs, examples, code, or release notes.
- The user asks whether GitHub/open-source projects can solve the same problem.
- Mature implementation examples or reusable projects would reduce uncertainty for one concrete capability.
- The answer should compare suitable GitHub repositories and explain how to use one with local adaptation.
Do not use for tiny edits, copy changes, local-only refactors where the codebase already dictates the answer, or requests that explicitly forbid web/GitHub research. Do not inspect private repositories unless the user explicitly scopes and authorizes that access.
Default Workflow
- Frame the problem locally first. Capture the goal, actual symptom, error signature, reproduction path, versions, runtime, dependency/framework names, recent changes, constraints, and attempted fixes. If a discoverable fact is missing, inspect local files/logs before asking.
- Choose the evidence mode. For errors/regressions, search issues, PRs, releases, and code first. For capability or tool needs, search repository candidates first. For feature implementation, use both repository candidates and issue/PR/code evidence.
- Create targeted searches. Prefer exact error text, package/API names, version numbers, framework + symptom, file names, config keys, stack trace fragments, failing command names, or capability + framework/runtime/API names.
- Find suitable GitHub projects when relevant. Prefer high-fit, high-Star, active, non-archived repositories with clear licenses and real examples. Lower the Star threshold when the high-Star set is too broad or misses the exact problem.
- Search GitHub evidence surfaces. Use issues, PRs, discussions, code, examples, release notes, and official project docs within relevant open-source repos. Repository search is required when a project itself may solve the problem.
- Rank by problem fit first, with Stars as a strong maturity signal. A high-Star repository is a strong candidate for inspection, but maintainer-confirmed issues, merged PRs, released fixes, official examples, and exact matching code beat popular adjacent projects. Use research-rubric.md when ranking matters.
- Deep-read the strongest projects and evidence. Use extraction-playbook.md to extract project basics, reusable surfaces, root cause or implementation pattern, version constraints, risks, adaptation boundaries, and verification steps.
- Translate to local work with minimal adaptation. Prefer the existing GitHub solution's public workflow, API, or architecture. Adapt only the parts required by the user's local interfaces, configuration, data/auth model, deployment target, or language/runtime.
- If evidence is weak, say so. Do not stretch weak matches into a confident recommendation. Mark the recommendation as first-principles or local-only when GitHub evidence is insufficient.
What ships with it
8 files 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.
- 2d ago First seen · 86 lines · 91 tokens per session scan A 0e35d14a32be
github-solution-research is a skill published in the GitHub repository taichuy/1flowbase (259 stars, last pushed 2d ago), licensed Apache-2.0. It adds 91 tokens to every session and 1,880 once invoked, about $0.0005 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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