Agent skill

Night Market Research Methodology

by athola in athola/claude-night-market

Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules.

MITAuto-check passedMarketing & SEO

Install Night Market Research Methodology

skills CLI
$ npx skills add athola/claude-night-market --skill night-market-research-methodology -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install athola/claude-night-market night-market-research-methodology --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/athola/claude-night-market.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/night-market-research-methodology .claude/skills/night-market-research-methodology && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
night-market-research-methodology
GitHub stars
341
Token cost
~3.8k tokens
SKILL.md length
1,905 words
Files
1
Skills in repo
152
Repo updated
First seen
Licence
MIT

At a glance

Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules.

  • Works in 4 steps: One mechanism explains all observations,… → Predict numbers before running. Write… → Survive assigned adversarial refutation.… → …
  • Tasks that involve Market research
  • SKILL.md covers The evidence bar, Idea lifecycle, The research-to-rules pipeline and The audit protocol, plus 5 more sections
  • Calls rg, python3 and gh

What it does

Night Market Research Methodology is an agent skill from athola/claude-night-market. Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules. Use when vetting ideas. Not for QA; use night-market-validation-and-qa.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Market research. The repository describes itself as: 23 Claude Code plugins: TDD enforcement hooks, git/PR workflows, spec-driven development, code review, project lifecycle, fix-from-error, maintenance automation, context… The licence is MIT.

When your agent uses it

  • Tasks that involve Market research

Example prompts

  • “/night-market-research-methodology”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. One mechanism explains all observations, including negatives.
  2. Predict numbers before running. Write down the expected
  3. Survive assigned adversarial refutation. Assign a reviewer or
  4. Never let the generator judge itself. The agent that produced

What it can do on your machine

Read from SKILL.md and the folder at commit 9f3eb00. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • rg
    • python3
    • gh
    • uv
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh, uv and git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Night Market Research Methodology loads about 3.8k tokens when it runs. Until then it costs about 48 tokens; SKILL.md has 1,905 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~48
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from athola/claude-night-market at commit 9f3eb00, republished under its MIT licence (© athola). 1,905 words, ~3,836 tokens.

Download SKILL.mdSave it as .claude/skills/night-market-research-methodology/SKILL.md (or your agent's skills folder).
name
night-market-research-methodology
description
Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules. Use when vetting ideas. Not for QA; use night-market-validation-and-qa.

Night Market Research Methodology

The discipline that turns a hunch into an accepted result in this repo. An "accepted result" is a change that survived the evidence bar and landed through change control as a rule, a skill module, a config gate, or an ADR. Everything else is either a local working note or a documented retirement. This skill covers the full path: score the idea, experiment behind a default-off flag, meet the evidence bar, land the durable artifact, or retire the idea on the record.

The evidence bar

A claim graduates from hunch to result only when it passes all four tests.

  1. One mechanism explains all observations, including negatives. If the hypothesis explains the three failing cases but not why the fourth case passed, it is incomplete. Keep digging until a single mechanism accounts for everything you saw.

  2. Predict numbers before running. Write down the expected measurement first, then measure. In-repo anchor: the forced-eval harness labels expected activations in prototypes/forced-eval/activation_cases.json before any run, then compares baseline against treatment with a McNemar paired test (a significance test for paired binary outcomes).

  3. Survive assigned adversarial refutation. Assign a reviewer or agent whose explicit job is to break the claim. Use Skill(attune:war-room) for hard-to-reverse decisions and Skill(imbue:rigorous-reasoning) to counter agreement bias. A claim nobody tried to break is unproven.

  4. Never let the generator judge itself. The agent that produced the work must not be its sole verifier. See plugins/imbue/skills/proof-of-work/modules/independent-verification.md. Prefer executable checks over an LLM judge, and prove the check can fail before trusting it (Guards 2 and 3 in plugins/imbue/skills/proof-of-work/modules/verifier-integrity.md).

Corollary from verifier-integrity: a green check proves the code satisfies the spec as written. It cannot prove the spec says what you meant, and it proves nothing if the check cannot fail. Validate the spec separately from the code, and mutation-test the check itself.

Idea lifecycle

An idea moves through four gates in order. Skipping a gate is how speculative infrastructure gets built and reverted.

Gate 1: score worthiness before building

Formula and thresholds from docs/backlog/queue.md (a local, gitignored working file):

Worthiness = (Business Value + Time Criticality + Risk Reduction)
           / (Complexity + Token Cost + Scope Drift)
ScoreAction
> 2.0Implement now
1.0 to 2.0Discuss before proceeding
< 1.0Keep in backlog

Queue rules: at most 10 active items. Items untouched for 30 days are archived to a GitHub issue (labels backlog,deferred) and removed from the queue. Because docs/backlog/ and docs/research/ are gitignored, the durable record of a deferred idea is the issue, not the queue file.

Gate 2: experiment behind a default-off flag

Exemplar: the egregore completion-integrity gate.

  • Commit 83281337 added the gate with completion_integrity: bool = False in plugins/egregore/scripts/config.py (still False as of 2026-07-02).
  • Commit cd903cbf added a test covering the raw-JSON opt-in path.

Pattern: land the mechanism off by default, cover the opt-in path with a test, and collect usage before proposing a default change.

Gate 3: data-collection window before structural change

ADR-0015 (docs/adr/0015-orchestrator-skill-simplification.md) requires 30 days of usage data before simplifying the over-built orchestrator skills. Apply the same bar to any promotion or simplification: name the data window in the PR, not an intuition.

Gate 4: adopt through change control or retire on the record

Adoption goes through the process in night-market-change-control. Retirement is written down, never silent. ADR-0012 (confidence-tagged claims) and ADR-0013 (Naur theory-building) carry Status: Superseded by ADR-0017, which is Accepted and rules "Do not build an enforcement mechanism. Permit voluntary use." A documented no is a valid result.

The research-to-rules pipeline

  1. Run multi-channel research (Skill(tome:research) or manual) into a dated synthesis at docs/research/YYYY-MM-DD-<topic>.md. Match the shape of the existing docs: Thesis, What the evidence says, solution pattern, Mapping to the night-market ecosystem, Evidence gaps and caveats.

  2. Map every gap against existing ecosystem assets before proposing new code. Most gaps turn out to be covered already (see case study 3).

  3. Land each real gap as the smallest durable artifact: a .claude/rules/ file, a module inside an existing skill, or a config gate. A new skill is the last resort (.claude/rules/shared-utility-consumer-rule.md requires 2+ consumers within 30 days).

  4. Fold the load-bearing evidence out of the synthesis and into the artifact that relies on it, as a table of sources and findings with resolvable identifiers (arXiv IDs, URLs), plus the caveats that bound them. Then delete the citation to the research file.

Caution: docs/research/ and docs/superpowers/ are both gitignored, so a tracked doc that cites a path under either is a dangling reference for every checkout but the author's. Step 4 is what prevents this, and it is not optional. Five syntheses were folded back into their consumers on 2026-07-27 for exactly this reason, and a brainstorm design record cited by .claude/rules/ceremony-requires-need.md survived that pass because the check below only looked at docs/research/. Verify with:

bash
rg -o --hidden 'docs/(research|superpowers)/[A-Za-z0-9._/-]+\.md' \
   -g '!docs/research/**' -g '!docs/superpowers/**' -g '!.git/**' . \
  | sed 's/.*://' | sort -u \
  | while read -r p; do
      git check-ignore -q "$p" && echo "DANGLING: $p"
    done

docs/backlog/ is gitignored too but stays out of the alternation on purpose. Every tracked citation of it is framed as a local convention, which .claude/skills/night-market-docs-and-writing/SKILL.md states outright, so adding it here would report three intentional hits and train the next reader to skip the output.

The failure it does invite is different, and ADR-0019 nearly shipped it: a tracked doc of record delegating its content to a gitignored path. Citing the backlog as the local ranking list is fine. Saying "the design is recorded in docs/backlog/queue.md" is not, because on a fresh clone nothing is recorded anywhere. A doc of record carries its own content.

Silence means every cited background path resolves on a fresh clone. The check tests whether the cited path is gitignored rather than matching on filename shape, so {session}-style templates in tome's own docs do not trip it. Add any newly ignored docs directory to the alternation, or the next draft cited from a rule repeats this.

The research doc is background, not the record.

Case study 1: coming loop (one doc, two artifact types)

The 2026-07-01 synthesis of Armin Ronacher's "The Coming Loop" pulled in the METR randomized trial (arXiv 2507.09089: 16 developers, 246 tasks), GitClear 2025 (211M changed lines), and Karpathy's "mortal terror of exceptions". It produced two artifacts:

  • a review-time rule, .claude/rules/prefer-invariants-over-fallbacks.md (commit 9f771794), and
  • a runtime gate, egregore completion_integrity, default off (commit 83281337).

Lesson: one research doc can fan out into different artifact types. Match the artifact to where the failure occurs (review time versus runtime).

Case study 2: prover-verifier (module, not skill)

The 2026-07-01 prover-verifier synthesis landed as commit 29081fda: a 146-line module with six guards, plugins/imbue/skills/proof-of-work/modules/verifier-integrity.md, inside the existing proof-of-work skill. No new skill was created. Lesson: extend the consumer that already exists.

Case study 3: karpathy-derivation (build only the delta)

docs/karpathy-derivation/project-brief.md (tracked in git) maps four Karpathy principles against existing skills in a coverage matrix and concludes "~90% coverage exists." Only the delta was built: imbue:karpathy-principles, a compact synthesis with an anti-pattern catalog. The same matrix was later reused as the lens for the April 2026 skill audit. Lesson: run the coverage analysis first. The most common honest research outcome is "we already have this."

Show full SKILL.md (761 more words)Show less

The audit protocol

As practiced in Discussion #449, the April 2026 skill-audit synthesis (category [Knowledge]).

  1. Tier 1 first: git history and rg scans (Skill(pensive:tiered-audit)). Escalate only what Tier 1 flags.

  2. Targeted parallel agents require output contracts, per .claude/rules/plan-before-large-dispatch.md (plan mode at 4+ agents). Contract schema in plugins/imbue/skills/proof-of-work/modules/output-contracts.md: required_sections, min_evidence_count (minimum [EN] evidence tags in findings), strictness (strict/normal/lenient). Findings carry file:line evidence. An empty findings list is a valid result. Report "no findings" as such rather than padding.

  3. Findings land as waves of inline fixes.

  4. Policy-shaped findings become issues, and landed issues become rules. Issues #454 (Exit Criteria required in every SKILL.md) and #457 (utility skills need 2+ consumers) both followed this path, are CLOSED, and live on as .claude/rules/skill-exit-criteria.md and .claude/rules/shared-utility-consumer-rule.md.

  5. The deferred remainder gets a tracking issue: #574 (Wave-3 skill-audit backlog, OPEN as of 2026-07-02).

Reading Discussions requires GraphQL. The gh discussion subcommand does not exist:

bash
gh api graphql -f query='query {
  repository(owner: "athola", name: "claude-night-market") {
    discussion(number: 449) { title body }
  }
}'

Proof-and-analysis recipes

Activation lift: does a skill actually fire?

prototypes/forced-eval/ (commit 5683e89b) measures whether a forced-evaluation hook lifts skill activation:

  • activation_cases.json holds labeled prompts with expected Skill() activations, recorded before measurement.
  • measure_activation.py runs each prompt via claude -p --output-format stream-json --max-turns 1 --allowedTools Skill, baseline (hook off) against treatment (hook on), and applies the McNemar paired test. True-negative cases count false activations, so a high positive rate alone is not treated as success.

Status: PROTOTYPE, not wired into any plugin.json. The harness is unit-tested but the live lift is unmeasured as of 2026-07-02 (the README says so). Run the harness tests:

bash
uv run python -m pytest prototypes/forced-eval/ -q

Verified 2026-07-02: 20 passed.

Mutation testing: are the tests real?

Mutation testing mutates source code and checks whether the tests notice. A surviving mutant is a test that cannot fail on that behavior, which is the "hollow check" failure mode from verifier-integrity Guard 2. CI runs it weekly plus on dispatch (.github/workflows/mutation-testing.yml). Exit codes: 0 means no survivors, 2 means survivors found, anything else is a crash. Local, per plugin:

bash
cd plugins/<plugin>
uv pip install mutmut --quiet
uv run mutmut run --paths-to-mutate=scripts/,src/ --tests-dir=tests/

Adjust --paths-to-mutate to the directories that exist. CI builds the list from the plugin's top-level scripts/ and src/ dirs.

Ratchet baselines: debt burndown you can prove

A ratchet baseline freezes today's debt count in a JSON file. The check fails only when new debt appears, and prints when the count drops so you can tighten the baseline and lock the win. Two live ratchets, both pre-commit hooks and standalone scripts:

bash
python3 scripts/check_skill_graph_drift.py
python3 scripts/check_skill_exit_criteria_drift.py

Verified output on 2026-07-02: dangling Skill() refs at 5 against a baseline of 31, and SKILL.md files missing Exit Criteria at 1 against a baseline of 127. Each script names the baseline key to lower. The shrinking baseline diff is the burndown proof: cite it in the PR.

Where good ideas came from

SourcePath taken
External researchRonacher, METR, Karpathy syntheses became rules and gates (case studies above)
PR-review painRecurring finding classes became pre-commit guards and .claude/rules/ entries
AuditsDiscussion #449 became issues #454/#457, which became rules
Incident lessonsSee night-market-failure-archaeology for the chronicle

When NOT to use

  • Running tests, coverage, or the evidence gates for a concrete change: use night-market-validation-and-qa instead.
  • Classifying, gating, and landing a change: use night-market-change-control instead.
  • Mechanics of Discussions, the decision journal, or ADR practice: use night-market-collective-memory instead.
  • Understanding settled incidents and reverts: use night-market-failure-archaeology instead.
  • Choosing an open problem worth attacking: use night-market-research-frontier instead.
  • Executing the completion-integrity work: use night-market-completion-integrity-campaign instead.

Exit Criteria

  • The idea has a written worthiness score with all six factors, and any score at or below 2.0 was discussed or queued, not built.
  • Predicted numbers were recorded before the measurement ran.
  • One mechanism explains every observation, including the cases that did not fail.
  • Verification names an independent check the generator cannot influence, and that check has been shown able to fail.
  • Any experiment shipped behind a default-off flag with a test covering the opt-in path.
  • The outcome is on the record: a landed rule, module, gate, or ADR, or a superseding ADR documenting retirement.

Provenance and maintenance

Compiled 2026-07-02 against repo v1.9.15, branch discussions-fix-1.9.14. Commit anchors (9f771794, 83281337, cd903cbf, 29081fda, 5683e89b) are stable. Volatile facts and one-line re-verification:

  • completion_integrity still default False: rg -n "completion_integrity" plugins/egregore/scripts/config.py
  • Ratchet counts (5/31 dangling refs, 1/127 missing Exit Criteria on 2026-07-02): rerun python3 scripts/check_skill_graph_drift.py and python3 scripts/check_skill_exit_criteria_drift.py
  • Issue states (#454 CLOSED, #457 CLOSED, #574 OPEN on 2026-07-02): gh issue view 574 --json state -q .state
  • Forced-eval lift still unmeasured: rg -n "not measured" prototypes/forced-eval/README.md
  • Background doc dirs still gitignored: git check-ignore docs/research docs/backlog docs/superpowers
  • Mutation exit-code semantics: rg -n "Exit codes" .github/workflows/mutation-testing.yml
  • Worthiness thresholds: reread docs/backlog/queue.md. It is a local file, absent on fresh clones. The thresholds are restated above.

© athola, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/night-market-research-methodology of athola/claude-night-market.

Open the folder on GitHubat commit 9f3eb00

Compare with similar skills

Night Market Research Methodology next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Night Market Research Methodology compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Night Market Research Methodology this skillathola/claude-night-market341—~3.8kAutomated safety check: PassMIT
Customer ResearchNexus-JPF/note-companion8706 repos~3.2kAutomated safety check: PassMIT
Creative Directorsmixs/creative-director-skill247—~5.1kAutomated safety check: PassCC-BY-4.0
Audience ResearchScrapeCreators/social-media-research-skills3.4k—~635Automated safety check: NotesMIT
Last 30 Days Trend Researchnexu-io/open-design100k—~1.3kAutomated safety check: PassMIT
Bggg Data Redditbinggandata/bggg-skills605—~1.2kAutomated safety check: PassMIT

Similar skills

  • Customer Research

    Nexus-JPF/note-companion

    When the user wants to conduct, analyze, or synthesize customer research.

    870 GitHub starsUsed in 6 repos~3.2k tokens
    Marketing & SEOAuto-check passed
  • Creative Director

    smixs/creative-director-skill

    AI creative director with recursive self-assessment. An agent skill from smixs/creative-director-skill.

    247 GitHub stars~5.1k tokensUpdated 2 mo ago
    Marketing & SEOAuto-check passed
  • Audience Research

    ScrapeCreators/social-media-research-skills

    A skill your agent uses when the user wants to evaluate a creator, influencer, or brand audience using public profile signals, TikTok audience demographics, follower/following data, comments…

    3.4k GitHub stars~635 tokensUpdated 1 mo ago
    Marketing & SEOAuto-check: notes
  • Last 30 Days Trend Research

    nexu-io/open-design

    Produces a cited Markdown briefing on recent community sentiment and social reaction to a topic, labeling every source it could not actually check.

    100k GitHub stars~1.3k tokensUpdated yesterday
    Marketing & SEOAuto-check passed
  • Bggg Data Reddit

    binggandata/bggg-skills

    Collect auditable Reddit search results and full comment trees at scale, preserve the source JSON, and normalize posts and comments into analysis-ready JSONL.

    605 GitHub stars~1.2k tokensUpdated 1 mo ago
    Marketing & SEOAuto-check passed
  • Comment Mining

    ScrapeCreators/social-media-research-skills

    A skill your agent uses when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or…

    3.4k GitHub stars~1k tokensUpdated 1 mo ago
    Marketing & SEOAuto-check: notes

More from athola/claude-night-market

All 152 skills in this repo
  • Night Market Diagnostics Toolkit

    athola/claude-night-market

    Run and interpret repo diagnostic scripts (ratchets, validators, token stats).

    341 GitHub stars~3.4k tokensUpdated yesterday
    Auto-check passed
  • Skills Eval

    athola/claude-night-market

    Evaluate Claude skill quality through auditing. An agent skill from athola/claude-night-market.

    341 GitHub stars~1.6k tokensUpdated yesterday
    Auto-check passed
  • Agent Teams

    athola/claude-night-market

    Coordinates Claude agent teams via filesystem protocol. An agent skill from athola/claude-night-market.

    341 GitHub stars~2.5k tokensUpdated yesterday
    Auto-check passed
  • Delegation Core

    athola/claude-night-market

    Delegates execution to eight CLIs (Gemini, Qwen, MiniMax, GLM, Muse, Codex, OpenCode, Glimmer).

    341 GitHub stars~2.5k tokensUpdated yesterday
    Auto-check passed
  • Elegant Code

    athola/claude-night-market

    Guide minimal code via a decision ladder with full safety, edge, and negative-case coverage.

    341 GitHub stars~2.1k tokensUpdated yesterday
    Auto-check passed
  • Skill Library Mission

    athola/claude-night-market

    Build a project skill library in .claude/skills/ via discovery, parallel authoring, and review.

    341 GitHub stars~1.6k tokensUpdated yesterday
    Auto-check passed

Categories

Questions about Night Market Research Methodology

What does Night Market Research Methodology do?

Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules. Night Market Research Methodology is an agent skill from athola/claude-night-market. Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules.

When should I use Night Market Research Methodology?

Night Market Research Methodology fits situations like: tasks that involve Market research.

How do I install Night Market Research Methodology in Claude Code?

Run `npx skills add athola/claude-night-market --skill night-market-research-methodology -a claude-code`. Or copy the skill folder (.claude/skills/night-market-research-methodology in athola/claude-night-market) into .claude/skills/night-market-research-methodology in your project. Claude Code loads it when a task matches its description.

How do I install Night Market Research Methodology in Codex?

Run `npx skills add athola/claude-night-market --skill night-market-research-methodology -a codex`. Or copy the skill folder (.claude/skills/night-market-research-methodology in athola/claude-night-market) into .agents/skills/night-market-research-methodology in your project. Codex loads it when a task matches its description.

Can I use Night Market Research Methodology in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add athola/claude-night-market --skill night-market-research-methodology -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/night-market-research-methodology, .gemini/skills/night-market-research-methodology, .github/skills/night-market-research-methodology and .opencode/skills/night-market-research-methodology in your project.

What does Night Market Research Methodology need to run?

Going by SKILL.md and its folder, Night Market Research Methodology needs the command-line tools its instructions call (rg, python3, gh, uv and git). Our summary lists: Python 3.

Does Night Market Research Methodology access the network?

SKILL.md contains no URLs. Its commands use gh, uv and git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Night Market Research Methodology safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Night Market Research Methodology use?

Night Market Research Methodology is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Night Market Research Methodology use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Night Market Research Methodology?

Skills that share tags, products or a category with Night Market Research Methodology: Customer Research (Nexus-JPF/note-companion, 870 stars), Creative Director (smixs/creative-director-skill, 247 stars), Audience Research (ScrapeCreators/social-media-research-skills, 3.4k stars) and Last 30 Days Trend Research (nexu-io/open-design, 100k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Night Market Research Methodology?

athola (a GitHub user) maintains it in athola/claude-night-market, which has 341 GitHub stars. The repository holds 152 skills in this directory. The repository was last updated on October 9, 2026.

Source: athola/claude-night-market on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.