hesoyam-for-claude-code: Skill for Claude Code

.claude/skills/research-sprint/SKILL.md

research-sprint is a skill for Claude Code from Shashank2577/hesoyam-for-claude-code. It costs 13 tokens per session (509 once invoked), scanned A, original, MIT.

A structured, time-limited process for researching a technical question and producing written deliverables. It compares approaches, trade-offs, integration concerns, production experience, and risks.

In plain words
What is it for?
Use it to create research notes, a comparison matrix, and a recommendation for a technical decision.
Why use it?
It gives technical research a defined scope and records the findings instead of leaving decisions based on scattered reading.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is Shashank2577/hesoyam-for-claude-code's own configuration. It tells Claude Code how to work on hesoyam-for-claude-code itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything hesoyam-for-claude-code configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Shashank2577/hesoyam-for-claude-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Shashank2577/hesoyam-for-claude-code/main/.claude/skills/research-sprint/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Shashank2577/hesoyam-for-claude-code

Made for: Claude Code.

Wrote this? Show the measurements

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README.md
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Per session 13 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 509 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00013 $0.00509
Opus 5.5 $0.00005 $0.00204
Sonnet 5.5 $0.00003 $0.00102
Haiku 4.5 $0.00001 $0.00051

Measured 2mo ago against content hash 908401b29a53, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

research-sprint 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.

.claude/skills/research-sprint/SKILL.md · 46 lines

What it actually says

$VAULT_PATH defaults to ~/Documents/obsidian-vault. Override by setting VAULT_PATH in your environment or passing --vault-path during install.

You are conducting a timeboxed research sprint. Follow this process exactly.

Step 1: Define the question. Ask the user:

  • What specifically do you need to decide?
  • What are the constraints (budget, timeline, team skills, infrastructure)?
  • What output format do you need? (ADR, comparison matrix, team briefing, all of the above?)

Step 2: Research. Search across multiple dimensions:

  1. Current industry best practices for this problem
  2. Top 3 approaches with their tradeoffs
  3. How each approach integrates with the user's existing stack
  4. Real-world production experience (blog posts, conference talks, post-mortems)
  5. Failure modes and risks for each approach

Save findings as structured markdown files in research/[topic]/ under the current project root:

  • findings.md — Raw research notes organized by dimension
  • comparison-matrix.md — Side-by-side comparison table
  • recommendation.md — Your recommended approach with rationale

Step 3: Synthesize. Create a final deliverable based on the requested output format:

  • ADR: Use the standard ADR format (Context, Options Considered, Decision, Rationale, Consequences). Save to the vault if Obsidian is configured.
  • Comparison matrix: Weighted scoring table with clear winner highlighted.
  • Team briefing: Executive summary (5 bullet points max) + detailed appendix.

Step 4: Capture.

  • Save the research to Obsidian vault if configured ($VAULT_PATH/03_Resources/research/[topic]/)
  • Add frontmatter with type: research, topic, date, status, decision
  • Create claude-mem observation summarizing the decision (skip if claude-mem is not installed)

Rules:

  • Cite sources for every claim. "I think" is not a source.
  • Present tradeoffs honestly. Every option has downsides — state them clearly.
  • Make a clear recommendation. Don't leave the user with "it depends" unless it truly does, and then specify what it depends on.
  • Stay within the user's constraints. Don't recommend a $50K/year solution to a solo developer.
Changes

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.

  1. 2mo ago First seen · 46 lines · 13 tokens per session scan A 908401b29a53

Subscribe to this mod's changes

research-sprint is a skill published in the GitHub repository Shashank2577/hesoyam-for-claude-code (11 stars, last pushed 7mo ago), licensed MIT. It adds 13 tokens to every session and 509 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.

Related

research-sprint compared with skills from other repositories

research-sprint next to 6 skills from other repositories
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