entry-gate
Skill23 starsChanged 3 months agoMITView on GitHub
Pre-investment screening gate for a name that is not yet researched or held. Use before spending a deep-research run or opening new exposure, to answer only "is this understandable and researchable enough to proceed?". Grades information richness separately from investment quality, applies deterministic hard vetoes from config/entry-gate-vetoes.json, and returns pass_to_deep_research / reject / gray_needs_evidence via the packaged clawock entry-gate command. Recommends research; never sizes or places a trade.
In this repository14 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- site/llms.txtllms.txt
- daily-deep-briefSkill
- earnings-reviewSkill
- hk-stock-analysisSkill
- openclaw-tuneSkill
- openclaw-upgradeSkill
- portfolio-risk-reviewSkill
- portfolio-swarm-reviewSkill
- scraplingSkill
- serenity-skillSkill
- tavilySkill
- us-stock-analysisSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 12
Write memory/entry-gates/<TICKER>-<YYYY-MM-DD>.json.
- Distinctive: line 32
Use the workspace pipelines, never a web price:
- Very specific: line 89
Follow assess.research_verdict (your stated judgment, or the policy default):
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Entry Gate
Manual, run once per candidate name. It is cheap on purpose: it decides whether the expensive work is worth doing, and its output routes straight into the full-report mode of the market's analysis skill.
Write memory/entry-gates/<TICKER>-<YYYY-MM-DD>.json. The script computes the
registered screening policy's verdict; you decide whether more research is worth
it. To disagree, add research_judgment: {"verdict": "pass_to_deep_research",
"rationale": "why the concern warrants investigation and what would change my view"}.
assess returns your research_verdict and research_departs_from_policy alongside
the original policy result. Neither result grants permission to open a position.
What the script verifies
information.grade(A/B/C) from the evidence source classes you cite;quote_freshnessfromquote.as_ofagainstassessed_at;verdictandrouting— a stated verdict that disagrees with the computed one is a false claim about the policy; put a differing research choice inresearch_judgment;- whether a hard veto's industry exception is actually encoded for this sector.
Vetoes are resolved before any check is counted, so checks_passed can never
rescue a vetoed name.
Step 1 — quote and instrument kind
Use the workspace pipelines, never a web price:
clawock fetch-peers --help # fetch verified quotes for an unheld name; use the documented JSON stdin requestquote.source_class must be one of the pipeline names, or validation fails outright.
A quote older than 24h makes the verdict gray rather than pass.
Set instrument_kind. A leveraged ETF has no company fundamentals to grade: it
takes the underlying_exposure / decay_and_regime / sizing_limit / liquidity
checks and routes to leverage_look_through. When the ticker is in
config/instruments.json, the registry's leverage_multiple overrules your
declaration.
Step 2 — mechanism and key variables
mechanism is one sentence: who pays, why, and what makes it repeat. If you
cannot write it from primary sources, that is the unintelligible_revenue_mechanism
veto, not a writing problem.
Then 3–7 key_variables, each with why it decides the outcome — not a metric dump.
Step 3 — checks, each carrying evidence
Company: business_quality, moat, management_governance, valuation,
dilution, downside. Every pass/fail needs at least one evidence_ids entry;
unknown is the honest answer when you have not verified it, and it makes the
verdict gray.
A failed business_quality, management_governance or downside (or, for a
leveraged ETF, underlying_exposure or sizing_limit) is a reject. A failed
moat, valuation, dilution, decay_and_regime or liquidity is not: an
expensive or shallow-moat name is still worth understanding, and sizing stays with
the existing risk and decision contracts.
Step 4 — the four hard vetoes
State a status for every veto in config/entry-gate-vetoes.json:
integrity/governance, unintelligible revenue mechanism, persistent negative cash
generation, destructive dilution. triggered requires evidence.
Two of them encode industry exceptions (clinical-stage biotech, pre-revenue
infrastructure). An exception applies only when the artifact's sector matches the
encoded one and the exception cites its own evidence row. Integrity and
unintelligible-mechanism encode none — there is no sector where they are acceptable.
Step 5 — mirror test and research judgment
Exactly five distinct sentences: what it does, why it earns, what must stay true, what would break it, and what you would do then.
clawock entry-gate validate memory/entry-gates/<TICKER>-<date>.json
clawock entry-gate assess memory/entry-gates/<TICKER>-<date>.jsonFollow assess.research_verdict (your stated judgment, or the policy default):
pass_to_deep_research→ go runus-stock-analysis/hk-stock-analysisMode 4 (Full Report), or the leverage look-through path for a leveraged ETF. Carrykey_variablesin as the questions the report must answer.gray_needs_evidence→next_evidencemust name the question and where to look. State what research would resolve the gap.reject→ record the research decision and its reason. Thin sourcing alone never lands here; aCgrade is gray, because information richness is not investment quality.
Hard limits
- A
Cinformation grade describes the sources, never the company. - A research disagreement preserves the veto evidence and policy verdict; do not erase a concern to justify further research.
- This gate opens no position and changes no thesis. A qualified name goes to full research; exposure still goes through the decision, risk, and settlement chain.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

